litellm/litellm/integrations/prometheus.py

# used for /metrics endpoint on LiteLLM Proxy
#### What this does ####
#    On success, log events to Prometheus
from __future__ import annotations

import asyncio
import math
import os
import sys
from collections.abc import Awaitable, Callable, Mapping, Sequence
from dataclasses import replace
from datetime import datetime, timedelta
from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, TypeAlias, TypeVar, cast

from pydantic import BaseModel
from typing_extensions import ReadOnly, TypedDict

import litellm
from litellm._logging import print_verbose, verbose_logger
from litellm.constants import PROXY_LLM_PROVIDER_FALLBACK
from litellm.exceptions import (
    validate_rate_limit_category,
    validate_rate_limit_type,
)
from litellm.integrations.custom_logger import CustomLogger
from litellm.integrations.prometheus_helpers import (
    PrometheusLabelFactoryContext,
    _get_cached_end_user_id_for_cost_tracking,
)
from litellm.integrations.prometheus_helpers.bounded_prometheus_series_tracker import (
    BoundedPrometheusSeriesTracker,
)
from litellm.litellm_core_utils.core_helpers import (
    get_litellm_metadata_from_kwargs,
    get_metadata_variable_name_from_kwargs,
)
from litellm.litellm_core_utils.service_tier_utils import (
    get_service_tier_from_standard_logging_payload,
)
from litellm.models.end_user import LiteLLM_EndUserTable
from litellm.proxy._types import (
    LiteLLM_DeletedVerificationToken,
    LiteLLM_TeamTable,
    LiteLLM_UserTable,
    UserAPIKeyAuth,
)
from litellm.repositories.base_repository import BaseRepository
from litellm.repositories.budget_repository import BudgetRepository
from litellm.repositories.organization_repository import OrganizationRepository
from litellm.repositories.table_repositories import EndUserRepository
from litellm.repositories.team_repository import TeamRepository
from litellm.repositories.user_repository import UserRepository
from litellm.types.guardrails import GuardrailEventHooks
from litellm.types.integrations.prometheus import *
from litellm.types.integrations.prometheus import (
    _sanitize_prometheus_label_name,
    _sanitize_prometheus_label_value,
    validate_prometheus_deployment_and_latency_caller_identity,
)
from litellm.types.proxy.carried_budget_state import (
    KeyBudgetSnapshot,
    OrgBudgetSnapshot,
    TeamBudgetSnapshot,
    UserBudgetSnapshot,
)
from litellm.types.utils import (
    StandardLoggingGuardrailInformation,
    StandardLoggingPayload,
)

if TYPE_CHECKING:
    from apscheduler.schedulers.asyncio import AsyncIOScheduler
    from prisma.types import (
        LiteLLM_BudgetTableWhereUniqueInput,
        LiteLLM_EndUserTableInclude,
        LiteLLM_EndUserTableOrderByInput,
    )
    from prometheus_client import Gauge
    from prometheus_client.metrics import MetricWrapperBase

    from litellm.proxy.utils import PrismaClient
    from litellm.router import Router
else:
    AsyncIOScheduler = Any

_IsNotNull = TypedDict("_IsNotNull", {"not": ReadOnly[None]})


class _BudgetedCustomerFilter(TypedDict):
    budget_id: ReadOnly[_IsNotNull]


_BudgetRowT: Final = TypeVar("_BudgetRowT")
_TableRowT: Final = TypeVar("_TableRowT", bound=BaseModel)

_DEFAULT_BUDGET_METRICS_PER_REQUEST_TIMEOUT: Final = 5.0

UNRECOGNIZED_REQUESTED_MODEL_LABEL: Final = "other"

_NON_ENUM_METRIC_LABELS: Final[frozenset[str]] = frozenset(
    (
        "guardrail_name",
        "status",
        "error_type",
        "hook_type",
        "purpose",
        "file_type",
        "result",
    )
)


class _PaginatedPrismaTable(Protocol[_TableRowT]):
    """The slice of a prisma table action surface used for budget-metric pagination."""

    async def find_many(
        self,
        *,
        skip: int,
        take: int,
        order: Mapping[str, str],
        include: Mapping[str, bool] | None = None,
    ) -> list[_TableRowT]: ...

    async def count(self) -> int: ...


def _paginated_table(repository: BaseRepository[_TableRowT]) -> _PaginatedPrismaTable[_TableRowT]:
    """View a repository's prisma table through the pagination surface budget metrics need."""
    return cast(
        _PaginatedPrismaTable[_TableRowT],
        repository.table,  # cast-ok: prisma rows carry the budget columns the domain model declares
    )


class _JoinedBudgetRow(Protocol):
    """The budget columns joined onto an organization or customer row."""

    @property
    def max_budget(self) -> float | None: ...

    @property
    def budget_reset_at(self) -> datetime | None: ...


class _CustomerBudgetRow(Protocol):
    """The columns of a customer (end user) row that budget gauges read."""

    @property
    def user_id(self) -> str: ...

    @property
    def spend(self) -> float: ...

    @property
    def litellm_budget_table(self) -> _JoinedBudgetRow | None: ...


def _customer_budget_metrics_enabled() -> bool:
    return litellm.enable_end_user_cost_tracking_prometheus_only is True and not litellm.disable_end_user_cost_tracking


class _ExcludedLabelMetric:
    """Proxies a prometheus metric whose declared ``labelnames`` had globally
    excluded labels removed, dropping those labels from every ``labels(...)``
    call so the emitted arguments always match the metric's real label set."""

    def __init__(
        self,
        metric: MetricWrapperBase,
        original_labelnames: tuple[str, ...],
        excluded_labels: frozenset[str],
    ) -> None:
        self._metric = metric
        self._original_labelnames = original_labelnames
        self._excluded_labels = excluded_labels

    def labels(self, *labelvalues: str, **labelkwargs: str) -> MetricWrapperBase:
        values: Final = labelvalues or tuple(labelkwargs[name] for name in self._original_labelnames)
        kept_values: Final = tuple(
            value for name, value in zip(self._original_labelnames, values) if name not in self._excluded_labels
        )
        return self._metric.labels(*kept_values) if kept_values else self._metric


_MetricLike: TypeAlias = "NoOpMetric | _ExcludedLabelMetric | MetricWrapperBase"


def _get_budget_metrics_per_request_timeout() -> float:
    raw: Final = os.getenv("PROMETHEUS_BUDGET_METRICS_PER_REQUEST_TIMEOUT")
    if raw is None:
        return _DEFAULT_BUDGET_METRICS_PER_REQUEST_TIMEOUT
    try:
        parsed = float(raw)
    except ValueError:
        parsed = None
    if parsed is None or not math.isfinite(parsed) or parsed <= 0:
        verbose_logger.debug(
            "[Non-Blocking] Prometheus: invalid PROMETHEUS_BUDGET_METRICS_PER_REQUEST_TIMEOUT=%r; using default %ss.",
            raw,
            _DEFAULT_BUDGET_METRICS_PER_REQUEST_TIMEOUT,
        )
        return _DEFAULT_BUDGET_METRICS_PER_REQUEST_TIMEOUT
    return parsed


def _get_proxy_llm_router() -> Router | None:
    try:
        from litellm.proxy.proxy_server import llm_router
    except Exception:
        return None
    return llm_router


def _bounded_requested_model_label(requested_model: str | None, router_originated: bool = False) -> str | None:
    """
    Bound ``requested_model`` label cardinality: names the router recognizes
    (model names, deployment ids, aliases, routing groups, team public model
    names) or matches via a global or team wildcard/pattern route keep their
    own label value; any other client-supplied string collapses into the
    single ``other`` bucket. With no proxy router to vouch for the string,
    client-supplied values collapse to ``other`` while ``router_originated``
    values (emitted by an SDK ``Router``'s own deployment failure and
    fallback events, where the proxy router never exists) pass through.
    """
    if not requested_model:
        return requested_model
    llm_router: Final = _get_proxy_llm_router()
    if llm_router is None:
        return requested_model if router_originated else UNRECOGNIZED_REQUESTED_MODEL_LABEL
    if llm_router.is_recognized_model(requested_model):
        return requested_model
    if requested_model in llm_router.team_public_model_names:
        return requested_model
    if llm_router.pattern_router.route(requested_model) is not None:
        return requested_model
    if any(
        team_pattern_router.route(requested_model) is not None
        for team_pattern_router in llm_router.team_pattern_routers.values()
    ):
        return requested_model
    return UNRECOGNIZED_REQUESTED_MODEL_LABEL


class PrometheusLogger(CustomLogger):
    # Class variables or attributes

    _ADDITIVE_GUARDRAIL_MODES = frozenset((GuardrailEventHooks.pre_call.value, GuardrailEventHooks.post_call.value))

    @staticmethod
    def get_instance() -> PrometheusLogger | None:
        """Find the PrometheusLogger instance from litellm.callbacks, if registered."""
        import litellm

        for cb in litellm.callbacks:
            if isinstance(cb, PrometheusLogger):
                return cb
        return None

    def __init__(
        self,
        **kwargs,
    ):
        try:
            from prometheus_client import Counter, Gauge, Histogram

            # Validate the caller-identity mode before any collector registers so an
            # invalid value cannot leave partially-registered metrics behind in the
            # process-global registry.
            validate_prometheus_deployment_and_latency_caller_identity()

            # Always initialize label_filters, even for non-premium users
            self.label_filters = self._parse_prometheus_config()

            self.exclude_metrics, self.exclude_labels = self._parse_exclude_config()

            # Cache resolved label sets per metric. Several entries in
            # ``PrometheusMetricLabels.get_labels`` read module-level toggles
            # (e.g. ``litellm.prometheus_emit_stream_label``,
            # ``litellm.prometheus_emit_rate_limit_labels``) that can be
            # changed at runtime. Prometheus counters/gauges/histograms are
            # created with a *fixed* ``labelnames`` set; if a runtime call
            # to ``get_labels_for_metric`` returned a different set, the
            # subsequent ``counter.labels(**_labels)`` would raise a
            # ``ValueError`` from the prometheus client. Snapshotting at
            # logger init time pins the label set for the lifetime of the
            # logger so toggling these flags only takes effect after a
            # restart, keeping init-time and runtime label sets in sync.
            self._cached_metric_labels: dict[str, list[str]] = {}
            self._emit_input_sequence_length_label = litellm.prometheus_emit_input_sequence_length_label is True

            _custom_buckets: Final = litellm.prometheus_latency_buckets
            self.latency_buckets = tuple(_custom_buckets) if _custom_buckets is not None else LATENCY_BUCKETS
            self._bounded_prometheus_series_tracker = BoundedPrometheusSeriesTracker()

            # Create metric factory functions
            self._counter_factory = self._create_metric_factory(Counter)
            self._gauge_factory = self._create_metric_factory(Gauge)
            self._histogram_factory = self._create_metric_factory(Histogram)

            self.litellm_proxy_failed_requests_metric = self._counter_factory(
                name="litellm_proxy_failed_requests_metric",
                documentation="Total number of failed responses from proxy - the client did not get a success response from litellm proxy",
                labelnames=self.get_labels_for_metric("litellm_proxy_failed_requests_metric"),
            )

            self.litellm_proxy_total_requests_metric = self._counter_factory(
                name="litellm_proxy_total_requests_metric",
                documentation="Total number of requests made to the proxy server - track number of client side requests",
                labelnames=self.get_labels_for_metric("litellm_proxy_total_requests_metric"),
            )

            # request latency metrics
            self.litellm_request_total_latency_metric = self._histogram_factory(
                "litellm_request_total_latency_metric",
                "End-to-end latency (seconds) for a request to LiteLLM Proxy Server, from the moment "
                "the request reached the proxy through the end of processing -- includes "
                "authentication, pre-call hooks, the LLM API call, and post-call processing",
                labelnames=self.get_labels_for_metric("litellm_request_total_latency_metric"),
                buckets=self.latency_buckets,
            )

            self.litellm_llm_api_latency_metric = self._histogram_factory(
                "litellm_llm_api_latency_metric",
                "Total latency (seconds) for a models LLM API call",
                labelnames=self.get_labels_for_metric("litellm_llm_api_latency_metric"),
                buckets=self.latency_buckets,
            )

            self.litellm_llm_api_time_to_first_token_metric = self._histogram_factory(
                "litellm_llm_api_time_to_first_token_metric",
                "Time to first token for a models LLM API call",
                # labelnames=[
                #     "model",
                #     "hashed_api_key",
                #     "api_key_alias",
                #     "team",
                #     "team_alias",
                # ],
                labelnames=self.get_labels_for_metric("litellm_llm_api_time_to_first_token_metric"),
                buckets=self.latency_buckets,
            )

            # Counter for spend
            self.litellm_spend_metric = self._counter_factory(
                "litellm_spend_metric",
                "Total spend on LLM requests",
                labelnames=self.get_labels_for_metric("litellm_spend_metric"),
            )

            # Counter for total_output_tokens
            self.litellm_tokens_metric = self._counter_factory(
                "litellm_total_tokens_metric",
                "Total number of input + output tokens from LLM requests",
                labelnames=self.get_labels_for_metric("litellm_total_tokens_metric"),
            )

            self.litellm_input_tokens_metric = self._counter_factory(
                "litellm_input_tokens_metric",
                "Total number of input tokens from LLM requests",
                labelnames=self.get_labels_for_metric("litellm_input_tokens_metric"),
            )

            self.litellm_output_tokens_metric = self._counter_factory(
                "litellm_output_tokens_metric",
                "Total number of output tokens from LLM requests",
                labelnames=self.get_labels_for_metric("litellm_output_tokens_metric"),
            )

            # Token-type detail metrics. These break out cached, cache-creation,
            # audio and reasoning tokens that providers report inside
            # prompt_tokens_details / completion_tokens_details on the usage
            # object. They are sparse (only incremented when the provider
            # reports a non-zero value) and are additive to the existing
            # input/output token totals — no breaking change for existing
            # dashboards built on the totals.
            self.litellm_input_cached_tokens_metric = self._counter_factory(
                "litellm_input_cached_tokens_metric",
                "Provider-side cached input tokens (e.g. OpenAI prompt_tokens_details.cached_tokens, Anthropic cache_read_input_tokens)",
                labelnames=self.get_labels_for_metric("litellm_input_cached_tokens_metric"),
            )

            self.litellm_input_cache_creation_tokens_metric = self._counter_factory(
                "litellm_input_cache_creation_tokens_metric",
                "Provider-side input tokens written to prompt cache (e.g. Anthropic cache_creation_input_tokens)",
                labelnames=self.get_labels_for_metric("litellm_input_cache_creation_tokens_metric"),
            )

            self.litellm_input_audio_tokens_metric = self._counter_factory(
                "litellm_input_audio_tokens_metric",
                "Audio input tokens reported in prompt_tokens_details.audio_tokens",
                labelnames=self.get_labels_for_metric("litellm_input_audio_tokens_metric"),
            )

            self.litellm_output_reasoning_tokens_metric = self._counter_factory(
                "litellm_output_reasoning_tokens_metric",
                "Reasoning tokens reported in completion_tokens_details.reasoning_tokens",
                labelnames=self.get_labels_for_metric("litellm_output_reasoning_tokens_metric"),
            )

            self.litellm_output_audio_tokens_metric = self._counter_factory(
                "litellm_output_audio_tokens_metric",
                "Audio output tokens reported in completion_tokens_details.audio_tokens",
                labelnames=self.get_labels_for_metric("litellm_output_audio_tokens_metric"),
            )

            self.litellm_video_duration_seconds_metric = self._counter_factory(
                "litellm_video_duration_seconds_metric",
                "Seconds of video generated, from usage.duration_seconds on video generation calls",
                labelnames=self.get_labels_for_metric("litellm_video_duration_seconds_metric"),
            )

            self.litellm_images_generated_metric = self._counter_factory(
                "litellm_images_generated_metric",
                "Number of images generated, from the image generation response",
                labelnames=self.get_labels_for_metric("litellm_images_generated_metric"),
            )

            # Remaining Budget for Team
            self.litellm_remaining_team_budget_metric = self._gauge_factory(
                "litellm_remaining_team_budget_metric",
                "Remaining budget for team",
                labelnames=self.get_labels_for_metric("litellm_remaining_team_budget_metric"),
            )

            # Max Budget for Team
            self.litellm_team_max_budget_metric = self._gauge_factory(
                "litellm_team_max_budget_metric",
                "Maximum budget set for team",
                labelnames=self.get_labels_for_metric("litellm_team_max_budget_metric"),
            )

            # Team Budget Reset At
            self.litellm_team_budget_remaining_hours_metric = self._gauge_factory(
                "litellm_team_budget_remaining_hours_metric",
                "Remaining days for team budget to be reset",
                labelnames=self.get_labels_for_metric("litellm_team_budget_remaining_hours_metric"),
            )

            # Number of members in a team
            self.litellm_team_members_metric = self._gauge_factory(
                "litellm_team_members_metric",
                "Number of members in a team",
                labelnames=self.get_labels_for_metric("litellm_team_members_metric"),
            )

            # Remaining Budget for Org
            self.litellm_remaining_org_budget_metric = self._gauge_factory(
                "litellm_remaining_org_budget_metric",
                "Remaining budget for org",
                labelnames=self.get_labels_for_metric("litellm_remaining_org_budget_metric"),
            )

            # Max Budget for Org
            self.litellm_org_max_budget_metric = self._gauge_factory(
                "litellm_org_max_budget_metric",
                "Maximum budget set for org",
                labelnames=self.get_labels_for_metric("litellm_org_max_budget_metric"),
            )

            # Org Budget Reset At
            self.litellm_org_budget_remaining_hours_metric = self._gauge_factory(
                "litellm_org_budget_remaining_hours_metric",
                "Remaining hours for org budget to be reset",
                labelnames=self.get_labels_for_metric("litellm_org_budget_remaining_hours_metric"),
            )

            # Remaining Budget for API Key
            self.litellm_remaining_api_key_budget_metric = self._gauge_factory(
                "litellm_remaining_api_key_budget_metric",
                "Remaining budget for api key",
                labelnames=self.get_labels_for_metric("litellm_remaining_api_key_budget_metric"),
            )

            # Max Budget for API Key
            self.litellm_api_key_max_budget_metric = self._gauge_factory(
                "litellm_api_key_max_budget_metric",
                "Maximum budget set for api key",
                labelnames=self.get_labels_for_metric("litellm_api_key_max_budget_metric"),
            )

            self.litellm_api_key_budget_remaining_hours_metric = self._gauge_factory(
                "litellm_api_key_budget_remaining_hours_metric",
                "Remaining hours for api key budget to be reset",
                labelnames=self.get_labels_for_metric("litellm_api_key_budget_remaining_hours_metric"),
            )

            # Remaining Budget for User
            self.litellm_remaining_user_budget_metric = self._gauge_factory(
                "litellm_remaining_user_budget_metric",
                "Remaining budget for user",
                labelnames=self.get_labels_for_metric("litellm_remaining_user_budget_metric"),
            )

            # Max Budget for User
            self.litellm_user_max_budget_metric = self._gauge_factory(
                "litellm_user_max_budget_metric",
                "Maximum budget set for user",
                labelnames=self.get_labels_for_metric("litellm_user_max_budget_metric"),
            )

            self.litellm_user_budget_remaining_hours_metric = self._gauge_factory(
                "litellm_user_budget_remaining_hours_metric",
                "Remaining hours for user budget to be reset",
                labelnames=self.get_labels_for_metric("litellm_user_budget_remaining_hours_metric"),
            )

            self.litellm_remaining_customer_budget_metric = self._gauge_factory(
                "litellm_remaining_customer_budget_metric",
                "Remaining budget for customer (end user)",
                labelnames=self.get_labels_for_metric("litellm_remaining_customer_budget_metric"),
            )

            self.litellm_customer_max_budget_metric = self._gauge_factory(
                "litellm_customer_max_budget_metric",
                "Maximum budget set for customer (end user)",
                labelnames=self.get_labels_for_metric("litellm_customer_max_budget_metric"),
            )

            self.litellm_customer_budget_remaining_hours_metric = self._gauge_factory(
                "litellm_customer_budget_remaining_hours_metric",
                "Remaining hours for customer (end user) budget to be reset",
                labelnames=self.get_labels_for_metric("litellm_customer_budget_remaining_hours_metric"),
            )

            ########################################
            # LiteLLM Virtual API KEY metrics
            ########################################

            # Remaining MODEL RPM limit for API Key
            self.litellm_remaining_api_key_requests_for_model = self._gauge_factory(
                "litellm_remaining_api_key_requests_for_model",
                "Remaining Requests API Key can make for model (model based rpm limit on key)",
                labelnames=self.get_labels_for_metric("litellm_remaining_api_key_requests_for_model"),
            )

            # Remaining MODEL TPM limit for API Key
            self.litellm_remaining_api_key_tokens_for_model = self._gauge_factory(
                "litellm_remaining_api_key_tokens_for_model",
                "Remaining Tokens API Key can make for model (model based tpm limit on key)",
                labelnames=self.get_labels_for_metric("litellm_remaining_api_key_tokens_for_model"),
            )

            self.litellm_api_key_rate_limit_allowed_metric = self._gauge_factory(
                "litellm_api_key_rate_limit_allowed_metric",
                "Configured rate limit for the API Key in the current window (rpm_limit / tpm_limit), by rate_limit_type",
                labelnames=self.get_labels_for_metric("litellm_api_key_rate_limit_allowed_metric"),
            )

            self.litellm_api_key_rate_limit_used_metric = self._gauge_factory(
                "litellm_api_key_rate_limit_used_metric",
                "Requests or tokens the API Key has consumed in the current rate limit window, by rate_limit_type",
                labelnames=self.get_labels_for_metric("litellm_api_key_rate_limit_used_metric"),
            )

            self.litellm_team_rate_limit_allowed_metric = self._gauge_factory(
                "litellm_team_rate_limit_allowed_metric",
                "Configured rate limit for the Team in the current window (team rpm_limit / tpm_limit), by rate_limit_type",
                labelnames=self.get_labels_for_metric("litellm_team_rate_limit_allowed_metric"),
            )

            self.litellm_team_rate_limit_used_metric = self._gauge_factory(
                "litellm_team_rate_limit_used_metric",
                "Requests or tokens the Team has consumed in the current rate limit window, by rate_limit_type",
                labelnames=self.get_labels_for_metric("litellm_team_rate_limit_used_metric"),
            )

            ########################################
            # LLM API Deployment Metrics / analytics
            ########################################

            # Remaining Rate Limit for model
            self.litellm_remaining_requests_metric = self._gauge_factory(
                "litellm_remaining_requests_metric",
                "LLM Deployment Analytics - remaining requests for model, returned from LLM API Provider",
                labelnames=self.get_labels_for_metric("litellm_remaining_requests_metric"),
            )

            self.litellm_remaining_tokens_metric = self._gauge_factory(
                "litellm_remaining_tokens_metric",
                "remaining tokens for model, returned from LLM API Provider",
                labelnames=self.get_labels_for_metric("litellm_remaining_tokens_metric"),
            )

            self.litellm_overhead_latency_metric = self._histogram_factory(
                "litellm_overhead_latency_metric",
                "Latency overhead (milliseconds) added by LiteLLM processing",
                labelnames=self.get_labels_for_metric("litellm_overhead_latency_metric"),
                buckets=self.latency_buckets,
            )

            self.litellm_overhead_with_guardrails_latency_metric = self._histogram_factory(
                "litellm_overhead_with_guardrails_latency_metric",
                "Total internal latency (seconds) added by LiteLLM, including "
                "pre/post-call guardrails (excludes the LLM API call)",
                labelnames=self.get_labels_for_metric("litellm_overhead_with_guardrails_latency_metric"),
                buckets=self.latency_buckets,
            )

            # Request queue time metric
            self.litellm_request_queue_time_metric = self._histogram_factory(
                "litellm_request_queue_time_seconds",
                "Time (seconds) from request arrival at the proxy to the start of pre-call "
                "processing -- includes authentication and any ASGI-level queueing",
                labelnames=self.get_labels_for_metric("litellm_request_queue_time_seconds"),
                buckets=self.latency_buckets,
            )

            # Guardrail metrics
            self.litellm_guardrail_latency_metric = self._histogram_factory(
                "litellm_guardrail_latency_seconds",
                "Latency (seconds) for guardrail execution",
                labelnames=["guardrail_name", "status", "error_type", "hook_type"],
                buckets=self.latency_buckets,
            )

            self.litellm_guardrail_errors_total = self._counter_factory(
                "litellm_guardrail_errors_total",
                "Total number of errors encountered during guardrail execution",
                labelnames=["guardrail_name", "error_type", "hook_type"],
            )

            self.litellm_guardrail_requests_total = self._counter_factory(
                "litellm_guardrail_requests_total",
                "Total number of guardrail invocations",
                labelnames=["guardrail_name", "status", "hook_type"],
            )
            # llm api provider budget metrics
            self.litellm_provider_remaining_budget_metric = self._gauge_factory(
                "litellm_provider_remaining_budget_metric",
                "Remaining budget for provider - used when you set provider budget limits",
                labelnames=["api_provider"],
            )

            # Metric for deployment state
            self.litellm_deployment_state = self._gauge_factory(
                "litellm_deployment_state",
                "LLM Deployment Analytics - The state of the deployment: 0 = healthy, 1 = partial outage, 2 = complete outage",
                labelnames=self.get_labels_for_metric("litellm_deployment_state"),
            )

            self.litellm_deployment_tpm_limit = self._gauge_factory(
                "litellm_deployment_tpm_limit",
                "Deployment TPM limit found in config",
                labelnames=self.get_labels_for_metric("litellm_deployment_tpm_limit"),
            )

            self.litellm_deployment_rpm_limit = self._gauge_factory(
                "litellm_deployment_rpm_limit",
                "Deployment RPM limit found in config",
                labelnames=self.get_labels_for_metric("litellm_deployment_rpm_limit"),
            )

            self.litellm_deployment_cooled_down = self._counter_factory(
                "litellm_deployment_cooled_down",
                "LLM Deployment Analytics - Number of times a deployment has been cooled down by LiteLLM load balancing logic. exception_status is the status of the exception that caused the deployment to be cooled down",
                # labelnames=_logged_llm_labels + [EXCEPTION_STATUS],
                labelnames=self.get_labels_for_metric("litellm_deployment_cooled_down"),
            )

            self.litellm_deployment_success_responses = self._counter_factory(
                name="litellm_deployment_success_responses",
                documentation="LLM Deployment Analytics - Total number of successful LLM API calls via litellm",
                labelnames=self.get_labels_for_metric("litellm_deployment_success_responses"),
            )
            self.litellm_deployment_failure_responses = self._counter_factory(
                name="litellm_deployment_failure_responses",
                documentation="LLM Deployment Analytics - Total number of failed LLM API calls for a specific LLM deploymeny. exception_status is the status of the exception from the llm api",
                labelnames=self.get_labels_for_metric("litellm_deployment_failure_responses"),
            )

            self.litellm_deployment_total_requests = self._counter_factory(
                name="litellm_deployment_total_requests",
                documentation="LLM Deployment Analytics - Total number of LLM API calls via litellm - success + failure",
                labelnames=self.get_labels_for_metric("litellm_deployment_total_requests"),
            )

            # Deployment Latency tracking
            self.litellm_deployment_latency_per_output_token = self._histogram_factory(
                name="litellm_deployment_latency_per_output_token",
                documentation="LLM Deployment Analytics - Latency per output token",
                labelnames=self.get_labels_for_metric("litellm_deployment_latency_per_output_token"),
            )

            self.litellm_deployment_successful_fallbacks = self._counter_factory(
                "litellm_deployment_successful_fallbacks",
                "LLM Deployment Analytics - Number of successful fallback requests from primary model -> fallback model",
                self.get_labels_for_metric("litellm_deployment_successful_fallbacks"),
            )

            self.litellm_deployment_failed_fallbacks = self._counter_factory(
                "litellm_deployment_failed_fallbacks",
                "LLM Deployment Analytics - Number of failed fallback requests from primary model -> fallback model",
                self.get_labels_for_metric("litellm_deployment_failed_fallbacks"),
            )

            # Callback Logging Failure Metrics
            self.litellm_callback_logging_failures_metric = self._counter_factory(
                name="litellm_callback_logging_failures_metric",
                documentation="Total number of failures when emitting logs to callbacks (e.g. s3_v2, langfuse, etc)",
                labelnames=["callback_name"],
            )

            self.litellm_llm_api_failed_requests_metric = self._counter_factory(
                name="litellm_llm_api_failed_requests_metric",
                documentation="deprecated - use litellm_proxy_failed_requests_metric",
                labelnames=self.get_labels_for_metric("litellm_llm_api_failed_requests_metric"),
            )

            self.litellm_requests_metric = self._counter_factory(
                name="litellm_requests_metric",
                documentation="deprecated - use litellm_proxy_total_requests_metric. Total number of LLM calls to litellm - track total per API Key, team, user",
                labelnames=self.get_labels_for_metric("litellm_requests_metric"),
            )

            # Cache metrics
            self.litellm_cache_hits_metric = self._counter_factory(
                name="litellm_cache_hits_metric",
                documentation="Total number of LiteLLM cache hits",
                labelnames=self.get_labels_for_metric("litellm_cache_hits_metric"),
            )

            self.litellm_cache_misses_metric = self._counter_factory(
                name="litellm_cache_misses_metric",
                documentation="Total number of LiteLLM cache misses",
                labelnames=self.get_labels_for_metric("litellm_cache_misses_metric"),
            )

            self.litellm_cached_tokens_metric = self._counter_factory(
                name="litellm_cached_tokens_metric",
                documentation="Total tokens served from LiteLLM cache",
                labelnames=self.get_labels_for_metric("litellm_cached_tokens_metric"),
            )

            # Provider prompt-caching metrics
            self.litellm_provider_cache_read_input_tokens_metric = self._counter_factory(
                name="litellm_provider_cache_read_input_tokens_metric",
                documentation="Total prompt/input tokens read from provider prompt cache (e.g. OpenAI/Anthropic/Gemini/Bedrock)",
                labelnames=self.get_labels_for_metric("litellm_provider_cache_read_input_tokens_metric"),
            )

            self.litellm_provider_cache_creation_input_tokens_metric = self._counter_factory(
                name="litellm_provider_cache_creation_input_tokens_metric",
                documentation="Total prompt/input tokens written to provider prompt cache (e.g. Anthropic/Bedrock)",
                labelnames=self.get_labels_for_metric("litellm_provider_cache_creation_input_tokens_metric"),
            )

            # User and Team count metrics
            self.litellm_total_users_metric = self._gauge_factory(
                "litellm_total_users",
                "Total number of users in LiteLLM",
                labelnames=[],
            )

            self.litellm_active_users_metric = self._gauge_factory(
                "litellm_active_users",
                "Number of billable users in LiteLLM (excludes SCIM-deactivated users)",
                labelnames=[],
            )

            self.litellm_teams_count_metric = self._gauge_factory(
                "litellm_teams_count",
                "Total number of teams in LiteLLM",
                labelnames=[],
            )

            ########################################
            # Managed Batch Metrics
            ########################################
            self.litellm_managed_batch_created_total = self._counter_factory(
                name="litellm_managed_batch_created_total",
                documentation="Total number of managed batches created",
                labelnames=[
                    "model",
                    "api_provider",
                    "user",
                    "user_email",
                    "api_key_alias",
                ],
            )

            self.litellm_managed_file_size_bytes = self._gauge_factory(
                "litellm_managed_file_size_bytes",
                "Size of the most recent managed batch file in bytes (last-seen value per label combination)",
                labelnames=["purpose", "file_type", "model", "api_provider", "user"],
            )

            self.litellm_managed_batch_duration_seconds = self._histogram_factory(
                "litellm_managed_batch_duration_seconds",
                "Duration of completed managed batches in seconds (completed_at - created_at)",
                labelnames=["model", "api_provider"],
                buckets=BATCH_DURATION_BUCKETS,
            )

            self.litellm_managed_file_created_total = self._counter_factory(
                name="litellm_managed_file_created_total",
                documentation="Total number of managed files created",
                labelnames=[
                    "model",
                    "api_provider",
                    "user",
                    "user_email",
                    "api_key_alias",
                ],
            )

            self.litellm_managed_file_deleted_total = self._counter_factory(
                name="litellm_managed_file_deleted_total",
                documentation="Total number of managed file deletions (success or blocked)",
                labelnames=["result"],
            )

            self.litellm_check_batch_cost_jobs_polled = self._gauge_factory(
                "litellm_check_batch_cost_jobs_polled",
                "Number of unprocessed batches found by the last CheckBatchCost poll",
                labelnames=[],
            )

            self.litellm_check_batch_cost_jobs_processed_total = self._counter_factory(
                name="litellm_check_batch_cost_jobs_processed_total",
                documentation="Total number of batches successfully cost-tracked by CheckBatchCost",
                labelnames=["model", "api_provider"],
            )

            self.litellm_check_batch_cost_errors_total = self._counter_factory(
                name="litellm_check_batch_cost_errors_total",
                documentation="Total number of errors in CheckBatchCost by error type",
                labelnames=["error_type"],
            )

            self.litellm_check_batch_cost_last_run_timestamp = self._gauge_factory(
                "litellm_check_batch_cost_last_run_timestamp",
                "Unix timestamp of the last CheckBatchCost job run",
                labelnames=[],
            )

            ########################################
            # MCP Tool Call Metrics
            ########################################
            self.litellm_mcp_tool_calls_total = self._counter_factory(
                name="litellm_mcp_tool_calls_total",
                documentation="Total MCP tool calls, segmented by tool and server name",
                labelnames=self.get_labels_for_metric("litellm_mcp_tool_calls_total"),
            )

            self.litellm_mcp_tool_call_spend_metric = self._counter_factory(
                name="litellm_mcp_tool_call_spend_metric",
                documentation="Total spend on MCP tool calls, segmented by tool and server name",
                labelnames=self.get_labels_for_metric("litellm_mcp_tool_call_spend_metric"),
            )

        except Exception as e:
            print_verbose(f"Got exception on init prometheus client {e}")
            raise e

    def _parse_prometheus_config(self) -> dict[str, list[str]]:
        """Parse prometheus metrics configuration for label filtering and enabled metrics"""
        import litellm
        from litellm.types.integrations.prometheus import PrometheusMetricsConfig

        config: Final = litellm.prometheus_metrics_config

        # If no config is provided, return empty dict (no filtering)
        if not config:
            return {}

        verbose_logger.debug("prometheus config: %s", config)

        # Parse and validate all configuration groups
        parsed_configs: Final = []
        self.enabled_metrics = set()

        for group_config in config:
            if isinstance(group_config, dict):
                parsed_config = PrometheusMetricsConfig(**group_config)
            else:
                parsed_config = group_config

            parsed_configs.append(parsed_config)
            self.enabled_metrics.update(parsed_config.metrics)

        # Validate all configurations
        validation_results: Final = self._validate_all_configurations(parsed_configs)

        if validation_results.has_errors:
            self._pretty_print_validation_errors(validation_results)
            error_message = "Configuration validation failed:\n" + "\n".join(validation_results.all_error_messages)
            raise ValueError(error_message)

        # Build label filters from valid configurations
        label_filters: Final = self._build_label_filters(parsed_configs)

        # Pretty print the processed configuration
        self._pretty_print_prometheus_config(label_filters)
        return label_filters

    def _parse_exclude_config(self) -> tuple[frozenset[str], frozenset[str]]:
        """Parse and validate the global ``exclude_metrics`` / ``exclude_labels`` settings."""
        from typing import get_args

        import litellm

        exclude_metrics: Final = frozenset(litellm.prometheus_exclude_metrics or ())
        exclude_labels: Final = frozenset(litellm.prometheus_exclude_labels or ())

        valid_metrics: Final = frozenset(get_args(DEFINED_PROMETHEUS_METRICS))
        invalid_metrics: Final = sorted(exclude_metrics - valid_metrics)

        valid_labels: Final = self._all_defined_labels()
        invalid_labels: Final = sorted(exclude_labels - valid_labels)

        errors: Final = (
            *(f"Invalid metric name in prometheus_exclude_metrics: {metric}" for metric in invalid_metrics),
            *(f"Invalid label name in prometheus_exclude_labels: {label}" for label in invalid_labels),
        )
        if errors:
            raise ValueError("Prometheus exclude configuration validation failed:\n" + "\n".join(errors))

        return exclude_metrics, exclude_labels

    @staticmethod
    def _all_defined_labels() -> frozenset[str]:
        """Every label a metric can emit: enum labels, hard-coded labels, and configured custom labels / tags."""
        import litellm

        builtin_labels: Final = frozenset(label.value for label in UserAPIKeyLabelNames)
        custom_metadata_labels: Final = frozenset(
            _sanitize_prometheus_label_name(label) for label in litellm.custom_prometheus_metadata_labels
        )
        custom_tag_labels: Final = frozenset(
            _sanitize_prometheus_label_name(f"tag_{tag}") for tag in litellm.custom_prometheus_tags
        )
        return builtin_labels | _NON_ENUM_METRIC_LABELS | custom_metadata_labels | custom_tag_labels

    def _validate_all_configurations(self, parsed_configs: list) -> ValidationResults:
        """Validate all metric configurations and return collected errors"""
        metric_errors: Final = []
        label_errors: Final = []

        for config in parsed_configs:
            for metric_name in config.metrics:
                # Validate metric name
                metric_error = self._validate_single_metric_name(metric_name)
                if metric_error:
                    metric_errors.append(metric_error)
                    continue  # Skip label validation if metric name is invalid

                # Validate labels if provided
                if config.include_labels:
                    label_error = self._validate_single_metric_labels(metric_name, config.include_labels)
                    if label_error:
                        label_errors.append(label_error)

        return ValidationResults(metric_errors=metric_errors, label_errors=label_errors)

    def _validate_single_metric_name(self, metric_name: str) -> MetricValidationError | None:
        """Validate a single metric name"""
        from typing import get_args

        if metric_name not in set(get_args(DEFINED_PROMETHEUS_METRICS)):
            return MetricValidationError(
                metric_name=metric_name,
                valid_metrics=get_args(DEFINED_PROMETHEUS_METRICS),
            )
        return None

    def _validate_single_metric_labels(self, metric_name: str, labels: list[str]) -> LabelValidationError | None:
        """Validate labels for a single metric"""
        from typing import cast

        # Get valid labels for this metric from PrometheusMetricLabels
        valid_labels: Final = PrometheusMetricLabels.get_labels(cast(DEFINED_PROMETHEUS_METRICS, metric_name))

        # Find invalid labels
        invalid_labels: Final = [label for label in labels if label not in valid_labels]

        if invalid_labels:
            return LabelValidationError(
                metric_name=metric_name,
                invalid_labels=invalid_labels,
                valid_labels=valid_labels,
            )
        return None

    def _build_label_filters(self, parsed_configs: list) -> dict[str, list[str]]:
        """Build label filters from validated configurations"""
        label_filters: Final = {}

        for config in parsed_configs:
            for metric_name in config.metrics:
                if config.include_labels:
                    # Only add if metric name is valid (validation already passed)
                    if self._validate_single_metric_name(metric_name) is None:
                        label_filters[metric_name] = config.include_labels

        return label_filters

    def _validate_configured_metric_labels(self, metric_name: str, labels: list[str]):
        """
        Ensure that all the configured labels are valid for the metric

        Raises ValueError if the metric labels are invalid and pretty prints the error
        """
        label_error: Final = self._validate_single_metric_labels(metric_name, labels)
        if label_error:
            self._pretty_print_invalid_labels_error(
                metric_name=label_error.metric_name,
                invalid_labels=label_error.invalid_labels,
                valid_labels=label_error.valid_labels,
            )
            raise ValueError(label_error.message)

        return True

    #########################################################
    # Pretty print functions
    #########################################################

    def _pretty_print_validation_errors(self, validation_results: ValidationResults) -> None:
        """Pretty print all validation errors using rich"""
        try:
            from rich.console import Console
            from rich.panel import Panel
            from rich.table import Table
            from rich.text import Text

            console: Final = Console()

            # Create error panel title
            title: Final = Text("🚨🚨 Configuration Validation Errors", style="bold red")

            # Print main error panel
            console.print("\n")
            console.print(Panel(title, border_style="red"))

            # Show invalid metric names if any
            if validation_results.metric_errors:
                invalid_metrics: Final = [e.metric_name for e in validation_results.metric_errors]
                valid_metrics = validation_results.metric_errors[0].valid_metrics  # All should have same valid metrics

                metrics_error_text: Final = Text(
                    f"Invalid Metric Names: {', '.join(invalid_metrics)}",
                    style="bold red",
                )
                console.print(Panel(metrics_error_text, border_style="red"))

                metrics_table: Final = Table(
                    title="📊 Valid Metric Names",
                    show_header=True,
                    header_style="bold green",
                    title_justify="left",
                    border_style="green",
                )
                metrics_table.add_column("Available Metrics", style="cyan", no_wrap=True)

                for metric in sorted(valid_metrics):
                    metrics_table.add_row(metric)

                console.print(metrics_table)

            # Show invalid labels if any
            if validation_results.label_errors:
                for error in validation_results.label_errors:
                    labels_error_text = Text(
                        f"Invalid Labels for '{error.metric_name}': {', '.join(error.invalid_labels)}",
                        style="bold red",
                    )
                    console.print(Panel(labels_error_text, border_style="red"))

                    labels_table = Table(
                        title=f"🏷️ Valid Labels for '{error.metric_name}'",
                        show_header=True,
                        header_style="bold green",
                        title_justify="left",
                        border_style="green",
                    )
                    labels_table.add_column("Valid Labels", style="cyan", no_wrap=True)

                    for label in sorted(error.valid_labels):
                        labels_table.add_row(label)

                    console.print(labels_table)

            console.print("\n")

        except ImportError:
            # Fallback to simple logging if rich is not available
            for metric_error in validation_results.metric_errors:
                verbose_logger.error(metric_error.message)
            for label_error in validation_results.label_errors:
                verbose_logger.error(label_error.message)

    def _pretty_print_invalid_labels_error(
        self, metric_name: str, invalid_labels: list[str], valid_labels: list[str]
    ) -> None:
        """Pretty print error message for invalid labels using rich"""
        try:
            from rich.console import Console
            from rich.panel import Panel
            from rich.table import Table
            from rich.text import Text

            console: Final = Console()

            # Create error panel title
            title: Final = Text(
                f"🚨🚨 Invalid Labels for Metric: '{metric_name}'\nInvalid labels: {', '.join(invalid_labels)}\nPlease specify only valid labels below",
                style="bold red",
            )

            # Create valid labels table
            labels_table: Final = Table(
                title="🏷️ Valid Labels for this Metric",
                show_header=True,
                header_style="bold green",
                title_justify="left",
                border_style="green",
            )
            labels_table.add_column("Valid Labels", style="cyan", no_wrap=True)

            for label in sorted(valid_labels):
                labels_table.add_row(label)

            # Print everything in a nice panel
            console.print("\n")
            console.print(Panel(title, border_style="red"))
            console.print(labels_table)
            console.print("\n")

        except ImportError:
            # Fallback to simple logging if rich is not available
            verbose_logger.error(
                "Invalid labels for metric '%s': %s. Valid labels: %s",
                metric_name,
                invalid_labels,
                sorted(valid_labels),
            )

    def _pretty_print_invalid_metric_error(self, invalid_metric_name: str, valid_metrics: tuple) -> None:
        """Pretty print error message for invalid metric name using rich"""
        try:
            from rich.console import Console
            from rich.panel import Panel
            from rich.table import Table
            from rich.text import Text

            console: Final = Console()

            # Create error panel title
            title: Final = Text(
                f"🚨🚨 Invalid Metric Name: '{invalid_metric_name}'\nPlease specify one of the allowed metrics below",
                style="bold red",
            )

            # Create valid metrics table
            metrics_table: Final = Table(
                title="📊 Valid Metric Names",
                show_header=True,
                header_style="bold green",
                title_justify="left",
                border_style="green",
            )
            metrics_table.add_column("Available Metrics", style="cyan", no_wrap=True)

            for metric in sorted(valid_metrics):
                metrics_table.add_row(metric)

            # Print everything in a nice panel
            console.print("\n")
            console.print(Panel(title, border_style="red"))
            console.print(metrics_table)
            console.print("\n")

        except ImportError:
            # Fallback to simple logging if rich is not available
            verbose_logger.error(
                "Invalid metric name: %s. Valid metrics: %s", invalid_metric_name, sorted(valid_metrics)
            )

    #########################################################
    # End of pretty print functions
    #########################################################

    def _valid_metric_name(self, metric_name: str):
        """
        Raises ValueError if the metric name is invalid and pretty prints the error
        """
        error: Final = self._validate_single_metric_name(metric_name)
        if error:
            self._pretty_print_invalid_metric_error(
                invalid_metric_name=error.metric_name, valid_metrics=error.valid_metrics
            )
            raise ValueError(error.message)

    def _pretty_print_prometheus_config(self, label_filters: dict[str, list[str]]) -> None:
        """Pretty print the processed prometheus configuration using rich"""
        try:
            from rich.console import Console
            from rich.panel import Panel
            from rich.table import Table
            from rich.text import Text

            console: Final = Console()

            # Create main panel title
            title: Final = Text("Prometheus Configuration Processed", style="bold blue")

            # Create enabled metrics table
            metrics_table: Final = Table(
                title="📊 Enabled Metrics",
                show_header=True,
                header_style="bold magenta",
                title_justify="left",
            )
            metrics_table.add_column("Metric Name", style="cyan", no_wrap=True)

            if hasattr(self, "enabled_metrics") and self.enabled_metrics:
                for metric in sorted(self.enabled_metrics):
                    metrics_table.add_row(metric)
            else:
                metrics_table.add_row("[yellow]All metrics enabled (no filter applied)[/yellow]")

            # Create label filters table
            labels_table: Final = Table(
                title="🏷️  Label Filters",
                show_header=True,
                header_style="bold green",
                title_justify="left",
            )
            labels_table.add_column("Metric Name", style="cyan", no_wrap=True)
            labels_table.add_column("Allowed Labels", style="yellow")

            if label_filters:
                for metric_name, labels in sorted(label_filters.items()):
                    labels_str = ", ".join(labels) if labels else "[dim]No labels specified[/dim]"
                    labels_table.add_row(metric_name, labels_str)
            else:
                labels_table.add_row(
                    "[yellow]No label filtering applied[/yellow]",
                    "[dim]All default labels will be used[/dim]",
                )

            # Print everything in a nice panel
            console.print("\n")
            console.print(Panel(title, border_style="blue"))
            console.print(metrics_table)
            console.print(labels_table)
            console.print("\n")

        except ImportError:
            # Fallback to simple logging if rich is not available
            verbose_logger.info(
                "Enabled metrics: %s",
                sorted(self.enabled_metrics) if hasattr(self, "enabled_metrics") else "All metrics",
            )
            verbose_logger.info("Label filters: %s", label_filters)

    def _is_metric_enabled(self, metric_name: str) -> bool:
        """Check if a metric is enabled based on configuration"""
        if metric_name in self.exclude_metrics:
            return False

        # If no specific configuration is provided, enable all metrics (default behavior)
        if not hasattr(self, "enabled_metrics"):
            return True

        # If enabled_metrics is empty, enable all metrics
        if not self.enabled_metrics:
            return True

        return metric_name in self.enabled_metrics

    def _create_metric_factory(self, metric_class):
        """Create a factory function that returns either a real metric or a no-op metric"""

        def factory(*args, **kwargs):
            # Extract metric name from the first argument or 'name' keyword argument
            metric_name: Final = args[0] if args else kwargs.get("name", "")

            if not self._is_metric_enabled(metric_name):
                return NoOpMetric()

            original_labelnames: Final = tuple(kwargs.get("labelnames") or ())
            if not (frozenset(original_labelnames) & self.exclude_labels):
                return metric_class(*args, **kwargs)

            kept: Final = tuple(name for name in original_labelnames if name not in self.exclude_labels)
            kept_kwargs: Final = {**kwargs, "labelnames": kept}  # mutable-ok: ** needs a mapping to override labelnames
            real_metric: Final = metric_class(*args, **kept_kwargs)
            return _ExcludedLabelMetric(real_metric, original_labelnames, self.exclude_labels)

        return factory

    def get_labels_for_metric(self, metric_name: DEFINED_PROMETHEUS_METRICS) -> list[str]:
        """
        Get the labels for a metric, filtered if configured.

        The result is cached on the instance so the label set used to
        construct each Prometheus metric at ``__init__`` time stays in lock
        step with the label set passed to ``counter.labels(...)`` at
        runtime, even if the underlying module-level toggles consulted by
        :meth:`PrometheusMetricLabels.get_labels` (e.g.
        ``litellm.prometheus_emit_rate_limit_labels``,
        ``litellm.prometheus_emit_stream_label``) are flipped after the
        logger has been created.
        """
        cached: Final = self._cached_metric_labels.get(metric_name)
        if cached is not None:
            return cached

        # Get default labels for this metric from PrometheusMetricLabels
        default_labels: Final = PrometheusMetricLabels.get_labels(metric_name)

        resolved_labels: Final = [
            label
            for label in default_labels
            if label not in self.exclude_labels
            and (metric_name not in self.label_filters or label in self.label_filters[metric_name])
        ]

        self._cached_metric_labels[metric_name] = resolved_labels
        return resolved_labels

    @staticmethod
    def _guardrail_is_additive(info: StandardLoggingGuardrailInformation) -> bool:
        mode: Final = info.get("guardrail_mode")
        modes: Final = mode if isinstance(mode, list) else [mode]
        mode_values: Final = frozenset(
            m.value if isinstance(m, GuardrailEventHooks) else m for m in modes if isinstance(m, str)
        )
        return bool(mode_values) and mode_values <= PrometheusLogger._ADDITIVE_GUARDRAIL_MODES

    @staticmethod
    def _get_guardrail_overhead_seconds(
        standard_logging_payload: StandardLoggingPayload,
    ) -> float:
        """Seconds of additive guardrail time (pre/post-call only) on the payload.

        during_call guardrails run concurrently with the LLM call, so their
        wall-clock overlaps the provider call and is not additive overhead;
        logging_only and MCP modes never block the user-facing response. A
        guardrail counts only when every mode it carries is pre/post-call, so a
        mixed list such as ["pre_call", "during_call"] is excluded.

        guardrail_information is typed as a list, but some guardrails assign a
        single dict directly, so normalize that shape to a one-item list.
        """
        guardrail_information: Final = standard_logging_payload.get("guardrail_information")
        entries: Final[list[StandardLoggingGuardrailInformation]] = (
            [cast("StandardLoggingGuardrailInformation", guardrail_information)]
            if isinstance(guardrail_information, dict)
            else guardrail_information or []
        )
        return sum(
            (float(info.get("duration") or 0.0) for info in entries if PrometheusLogger._guardrail_is_additive(info)),
            0.0,
        )

    def _set_overhead_with_guardrails_metric(
        self,
        standard_logging_payload: StandardLoggingPayload,
        enum_values: UserAPIKeyLabelValues,
        label_context: PrometheusLabelFactoryContext | None = None,
    ) -> None:
        """Record litellm_overhead_with_guardrails_latency_metric (seconds): SDK overhead +
        pre/post-call guardrail time. Recorded outside the SDK-overhead gate so
        guardrail-only overhead is still captured when litellm_overhead_time_ms
        is 0 or absent.
        """
        litellm_overhead_time_ms: Final = standard_logging_payload["hidden_params"].get("litellm_overhead_time_ms")
        guardrail_overhead_seconds: Final = self._get_guardrail_overhead_seconds(standard_logging_payload)
        if litellm_overhead_time_ms is None and guardrail_overhead_seconds <= 0:
            return
        labels: Final = prometheus_label_factory(
            supported_enum_labels=self.get_labels_for_metric(
                metric_name="litellm_overhead_with_guardrails_latency_metric"
            ),
            enum_values=enum_values,
            label_context=label_context,
        )
        self.litellm_overhead_with_guardrails_latency_metric.labels(**labels).observe(
            ((litellm_overhead_time_ms or 0.0) / 1000) + guardrail_overhead_seconds
        )

    def _track_end_user_metric_series(
        self,
        metric: Any,
        metric_name: DEFINED_PROMETHEUS_METRICS,
        labels: Mapping[str, str | None],
    ) -> None:
        """
        Cap the cardinality of metrics that include the ``end_user`` label.

        Called *after* ``metric.labels(...).inc()/observe()`` so the emission is
        recorded in prometheus-client's child map before any eviction runs.
        Series that get evicted before the next scrape lose updates accrued
        since the last scrape — this is inherent to any cardinality cap.
        """
        labelnames: Final = self.get_labels_for_metric(metric_name)
        if UserAPIKeyLabelNames.END_USER.value not in labelnames:
            return
        if labels.get(UserAPIKeyLabelNames.END_USER.value) is None:
            return

        max_series: Final = litellm.prometheus_end_user_metrics_max_series_per_metric
        ttl_seconds: Final = litellm.prometheus_end_user_metrics_ttl_seconds
        if max_series is None and ttl_seconds is None:
            return

        self._bounded_prometheus_series_tracker.track_series(
            metric=metric,
            metric_name=metric_name,
            label_values=tuple(labels.get(label) for label in labelnames),
            max_series=max_series,
            ttl_seconds=ttl_seconds,
            cleanup_interval_seconds=litellm.prometheus_end_user_metrics_cleanup_interval_seconds,
        )

    def _inc_labeled_counter(
        self,
        counter: Any,
        metric_name: DEFINED_PROMETHEUS_METRICS,
        enum_values: UserAPIKeyLabelValues,
        label_context: PrometheusLabelFactoryContext | None = None,
        amount: float = 1.0,
    ) -> None:
        _labels: Final = prometheus_label_factory(
            supported_enum_labels=self.get_labels_for_metric(metric_name=metric_name),
            enum_values=enum_values,
            label_context=label_context,
        )
        counter.labels(**_labels).inc(amount)
        self._track_end_user_metric_series(counter, metric_name, _labels)

    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
        # Define prometheus client
        verbose_logger.debug(
            "prometheus Logging - Enters success logging function (kwargs keys: %s)",
            list(kwargs.keys()) if isinstance(kwargs, dict) else type(kwargs).__name__,
        )

        # unpack kwargs
        standard_logging_payload: Final[StandardLoggingPayload | None] = kwargs.get("standard_logging_object")

        if standard_logging_payload is None or not isinstance(standard_logging_payload, dict):
            raise ValueError(f"standard_logging_object is required, got={standard_logging_payload}")

        if self._should_skip_metrics_for_invalid_key(kwargs=kwargs, standard_logging_payload=standard_logging_payload):
            return

        model: Final = kwargs.get("model", "")
        litellm_params: Final = kwargs.get("litellm_params", {}) or {}
        _metadata: Final = litellm_params.get("metadata") or {}
        get_end_user_id_for_cost_tracking: Final = _get_cached_end_user_id_for_cost_tracking()

        end_user_id: Final = get_end_user_id_for_cost_tracking(litellm_params, service_type="prometheus")
        user_id: Final = standard_logging_payload["metadata"]["user_api_key_user_id"]
        user_api_key = standard_logging_payload["metadata"]["user_api_key_hash"]
        user_api_key_alias: Final = standard_logging_payload["metadata"]["user_api_key_alias"]
        user_api_team: Final = standard_logging_payload["metadata"]["user_api_key_team_id"]
        user_api_team_alias: Final = standard_logging_payload["metadata"]["user_api_key_team_alias"]
        user_api_key_org_id: Final = standard_logging_payload["metadata"].get("user_api_key_org_id")
        user_api_key_org_alias: Final = standard_logging_payload["metadata"].get("user_api_key_org_alias")
        output_tokens: Final = standard_logging_payload["completion_tokens"]
        tokens_used: Final = standard_logging_payload["total_tokens"]
        response_cost: Final = standard_logging_payload["response_cost"]
        combined_metadata: Final = _get_combined_custom_metadata_from_standard_logging_payload(
            standard_logging_payload=standard_logging_payload
        )
        if standard_logging_payload is not None and isinstance(standard_logging_payload, dict):
            _tags = standard_logging_payload["request_tags"]
        else:
            _tags = []

        print_verbose(
            f"inside track_prometheus_metrics, model {model}, response_cost {response_cost}, tokens_used {tokens_used}, end_user_id {end_user_id}, user_api_key {user_api_key}"
        )

        enum_values: Final = UserAPIKeyLabelValues(
            end_user=end_user_id,
            hashed_api_key=user_api_key,
            api_key_alias=user_api_key_alias,
            requested_model=standard_logging_payload["model_group"],
            model_group=standard_logging_payload["model_group"],
            team=user_api_team,
            team_alias=user_api_team_alias,
            org_id=user_api_key_org_id,
            org_alias=user_api_key_org_alias,
            user=user_id,
            user_email=standard_logging_payload["metadata"]["user_api_key_user_email"],
            status_code="200",
            model=model,
            litellm_model_name=model,
            tags=_tags,
            model_id=standard_logging_payload["model_id"],
            api_base=standard_logging_payload["api_base"],
            api_provider=standard_logging_payload["custom_llm_provider"],
            exception_status=None,
            exception_class=None,
            custom_metadata_labels=get_custom_labels_from_metadata(metadata=combined_metadata),
            route=standard_logging_payload["metadata"].get("user_api_key_request_route"),
            client_ip=standard_logging_payload["metadata"].get("requester_ip_address"),
            user_agent=standard_logging_payload["metadata"].get("user_agent"),
            stream=(str(standard_logging_payload.get("stream")) if litellm.prometheus_emit_stream_label else None),
            service_tier=get_service_tier_from_standard_logging_payload(standard_logging_payload),
        )

        if user_api_key is not None and isinstance(user_api_key, str) and user_api_key.startswith("sk-"):
            from litellm.proxy.utils import hash_token

            user_api_key = hash_token(user_api_key)

        label_context: Final = PrometheusLabelFactoryContext(enum_values)  # amortized per request.

        # increment total LLM requests and spend metric
        self._increment_top_level_request_and_spend_metrics(
            end_user_id=end_user_id,
            user_api_key=user_api_key,
            user_api_key_alias=user_api_key_alias,
            model=model,
            user_api_team=user_api_team,
            user_api_team_alias=user_api_team_alias,
            user_id=user_id,
            response_cost=response_cost,
            enum_values=enum_values,
            label_context=label_context,
        )

        # input, output, total token metrics
        self._increment_token_metrics(
            # why type ignore below?
            # 1. We just checked if isinstance(standard_logging_payload, dict). Pyright complains.
            # 2. Pyright does not allow us to run isinstance(standard_logging_payload, StandardLoggingPayload) <- this would be ideal
            standard_logging_payload=standard_logging_payload,
            end_user_id=end_user_id,
            user_api_key=user_api_key,
            user_api_key_alias=user_api_key_alias,
            model=model,
            user_api_team=user_api_team,
            user_api_team_alias=user_api_team_alias,
            user_id=user_id,
            enum_values=enum_values,
            label_context=label_context,
        )

        # remaining budget metrics
        await self._increment_remaining_budget_metrics(
            user_api_team=user_api_team,
            user_api_team_alias=user_api_team_alias,
            user_api_key=user_api_key,
            user_api_key_alias=user_api_key_alias,
            litellm_params=litellm_params,
            response_cost=response_cost,
            user_id=user_id,
            user_api_key_org_id=user_api_key_org_id,
            end_user_id=end_user_id,
        )

        # set proxy virtual key rpm/tpm metrics
        self._set_virtual_key_rate_limit_metrics(
            user_api_key=user_api_key,
            user_api_key_alias=user_api_key_alias,
            kwargs=kwargs,
            metadata=_metadata,
            model_id=enum_values.model_id,
        )

        self._set_key_and_team_rate_limit_metrics(
            standard_logging_payload=standard_logging_payload,  # pyright: ignore[reportArgumentType]  # isinstance(dict) above narrows the TypedDict to dict[Unknown, Unknown]
            enum_values=enum_values,
        )

        # set latency metrics
        self._set_latency_metrics(
            kwargs=kwargs,
            model=model,
            user_api_key=user_api_key,
            user_api_key_alias=user_api_key_alias,
            user_api_team=user_api_team,
            user_api_team_alias=user_api_team_alias,
            # why type ignore below?
            # 1. We just checked if isinstance(standard_logging_payload, dict). Pyright complains.
            # 2. Pyright does not allow us to run isinstance(standard_logging_payload, StandardLoggingPayload) <- this would be ideal
            enum_values=enum_values,
            label_context=label_context,
            input_sequence_length=(
                self._get_input_sequence_length(standard_logging_payload, kwargs, response_obj)
                if self._emit_input_sequence_length_label
                else None
            ),
        )

        # set x-ratelimit headers
        self.set_llm_deployment_success_metrics(
            kwargs,
            start_time,
            end_time,
            enum_values,
            output_tokens,
            label_context=label_context,
        )

        # Provider-agnostic fallback: providers like Bedrock and Vertex don't return
        # x-ratelimit-remaining-* headers, so the gauges above only fire for OpenAI /
        # Anthropic / Azure. When the proxy router has tpm/rpm configured for the
        # model_group, derive remaining from configured-limit minus current usage so
        # the same metric is populated for any provider.
        await self._async_set_router_remaining_metrics(
            standard_logging_payload=standard_logging_payload,
            enum_values=enum_values,
            label_context=label_context,
        )

        # cache metrics
        self._increment_cache_metrics(
            standard_logging_payload=standard_logging_payload,
            enum_values=enum_values,
            label_context=label_context,
        )

        self._increment_media_generation_metrics(
            standard_logging_payload=standard_logging_payload,
            enum_values=enum_values,
            label_context=label_context,
        )

        # MCP tool call metrics
        self._increment_mcp_tool_call_metrics(
            standard_logging_payload=standard_logging_payload,
            enum_values=enum_values,
            response_cost=response_cost,
        )

        # increment litellm_proxy_total_requests_metric for all successful requests
        # (both streaming and non-streaming) in this single location to prevent
        # double-counting that occurs when async_post_call_success_hook also increments
        PrometheusLogger._inc_labeled_counter(
            self,
            self.litellm_proxy_total_requests_metric,
            "litellm_proxy_total_requests_metric",
            enum_values,
            label_context=label_context,
        )

    def _increment_token_metrics(
        self,
        standard_logging_payload: StandardLoggingPayload,
        end_user_id: str | None,
        user_api_key: str | None,
        user_api_key_alias: str | None,
        model: str | None,
        user_api_team: str | None,
        user_api_team_alias: str | None,
        user_id: str | None,
        enum_values: UserAPIKeyLabelValues,
        label_context: PrometheusLabelFactoryContext | None = None,
    ):
        verbose_logger.debug("prometheus Logging - Enters token metrics function")
        # token metrics

        if standard_logging_payload is not None and isinstance(standard_logging_payload, dict):
            _tags: Final = standard_logging_payload["request_tags"]

        PrometheusLogger._inc_labeled_counter(
            self,
            self.litellm_tokens_metric,
            "litellm_total_tokens_metric",
            enum_values,
            label_context=label_context,
            amount=float(standard_logging_payload["total_tokens"]),
        )
        PrometheusLogger._inc_labeled_counter(
            self,
            self.litellm_input_tokens_metric,
            "litellm_input_tokens_metric",
            enum_values,
            label_context=label_context,
            amount=float(standard_logging_payload["prompt_tokens"]),
        )
        PrometheusLogger._inc_labeled_counter(
            self,
            self.litellm_output_tokens_metric,
            "litellm_output_tokens_metric",
            enum_values,
            label_context=label_context,
            amount=float(standard_logging_payload["completion_tokens"]),
        )

        # Token-type detail metrics — sparse, only emitted when the provider
        # reports a non-zero value in usage.prompt_tokens_details /
        # usage.completion_tokens_details.
        self._increment_token_detail_metrics(
            standard_logging_payload=standard_logging_payload,
            enum_values=enum_values,
            label_context=label_context,
        )

    def _increment_token_detail_metrics(
        self,
        standard_logging_payload: StandardLoggingPayload,
        enum_values: UserAPIKeyLabelValues,
        label_context: PrometheusLabelFactoryContext | None = None,
    ) -> None:
        """
        Increment per-token-type counters from the Usage object that providers
        attach to the request. The Usage dict is plumbed onto
        ``standard_logging_payload["metadata"]["usage_object"]`` by
        ``get_standard_logging_object_payload``.

        Each counter is only incremented when the underlying value is > 0, so
        scrape output stays sparse for providers that don't report these
        details (most non-OpenAI/Anthropic models).
        """
        metadata: Final = standard_logging_payload.get("metadata") or {}
        usage_object: Final = metadata.get("usage_object") if isinstance(metadata, dict) else None
        if not isinstance(usage_object, dict):
            return

        prompt_details: Final = usage_object.get("prompt_tokens_details") or {}
        completion_details: Final = usage_object.get("completion_tokens_details") or {}

        cache_creation_detail_tokens: Final = PrometheusLogger._resolve_cache_write_tokens(prompt_details)

        detail_metrics: Final[list[tuple[_MetricLike, DEFINED_PROMETHEUS_METRICS, object]]] = [
            (
                self.litellm_input_cached_tokens_metric,
                "litellm_input_cached_tokens_metric",
                (prompt_details.get("cached_tokens") if isinstance(prompt_details, dict) else None),
            ),
            (
                self.litellm_input_cache_creation_tokens_metric,
                "litellm_input_cache_creation_tokens_metric",
                cache_creation_detail_tokens,
            ),
            (
                self.litellm_input_audio_tokens_metric,
                "litellm_input_audio_tokens_metric",
                (prompt_details.get("audio_tokens") if isinstance(prompt_details, dict) else None),
            ),
            (
                self.litellm_output_reasoning_tokens_metric,
                "litellm_output_reasoning_tokens_metric",
                (completion_details.get("reasoning_tokens") if isinstance(completion_details, dict) else None),
            ),
            (
                self.litellm_output_audio_tokens_metric,
                "litellm_output_audio_tokens_metric",
                (completion_details.get("audio_tokens") if isinstance(completion_details, dict) else None),
            ),
        ]

        PrometheusLogger._inc_sparse_usage_counters(
            self,
            detail_metrics,
            enum_values=enum_values,
            label_context=label_context,
        )

    def _increment_media_generation_metrics(
        self,
        standard_logging_payload: StandardLoggingPayload,
        enum_values: UserAPIKeyLabelValues,
        label_context: PrometheusLabelFactoryContext | None = None,
    ) -> None:
        """
        Increment video-seconds and images-generated counters from
        ``standard_logging_payload["metadata"]["usage_object"]``. Video
        providers report ``duration_seconds`` there; image generation calls
        report ``output_image_count``. Both are sparse: only emitted when the
        value is present and > 0, so token-only call types are unaffected.
        """
        metadata: Final = standard_logging_payload.get("metadata") or {}
        usage_object: Final = metadata.get("usage_object") if isinstance(metadata, dict) else None
        if not isinstance(usage_object, dict):
            return

        media_metrics: Final[list[tuple[_MetricLike, DEFINED_PROMETHEUS_METRICS, object]]] = [
            (
                self.litellm_video_duration_seconds_metric,
                "litellm_video_duration_seconds_metric",
                usage_object.get("duration_seconds"),
            ),
            (
                self.litellm_images_generated_metric,
                "litellm_images_generated_metric",
                usage_object.get("output_image_count"),
            ),
        ]

        PrometheusLogger._inc_sparse_usage_counters(
            self,
            media_metrics,
            enum_values=enum_values,
            label_context=label_context,
        )

    def _inc_sparse_usage_counters(
        self,
        counters_with_values: Sequence[tuple[_MetricLike, DEFINED_PROMETHEUS_METRICS, object]],
        enum_values: UserAPIKeyLabelValues,
        label_context: PrometheusLabelFactoryContext | None = None,
    ) -> None:
        """
        Increment each ``(counter, metric_name, value)`` entry whose value is
        a positive number. Non-numeric values (including booleans from
        malformed provider usage dicts) and values <= 0 are skipped, keeping
        scrape output sparse.
        """
        for counter, metric_name, value in counters_with_values:
            if isinstance(value, bool) or not isinstance(value, (int, float)) or value <= 0:
                continue
            PrometheusLogger._inc_labeled_counter(
                self,
                counter,
                metric_name,
                enum_values,
                label_context=label_context,
                amount=float(value),
            )

    def _increment_cache_metrics(
        self,
        standard_logging_payload: StandardLoggingPayload,
        enum_values: UserAPIKeyLabelValues,
        label_context: PrometheusLabelFactoryContext | None = None,
    ):
        """
        Increment cache-related Prometheus metrics based on cache hit/miss status.

        Args:
            standard_logging_payload: Contains cache_hit field (True/False/None)
            enum_values: Label values for Prometheus metrics
        """
        cache_hit: Final = standard_logging_payload.get("cache_hit")

        if cache_hit is None:
            # Historically these metrics only tracked LiteLLM caching.
            # Provider prompt-caching metrics are still emitted below.
            pass
        elif cache_hit is True:
            # Increment cache hits counter
            PrometheusLogger._inc_labeled_counter(
                self,
                self.litellm_cache_hits_metric,
                "litellm_cache_hits_metric",
                enum_values,
                label_context=label_context,
            )

            # Increment cached tokens counter
            total_tokens: Final = standard_logging_payload.get("total_tokens", 0)
            if total_tokens > 0:
                PrometheusLogger._inc_labeled_counter(
                    self,
                    self.litellm_cached_tokens_metric,
                    "litellm_cached_tokens_metric",
                    enum_values,
                    label_context=label_context,
                    amount=float(total_tokens),
                )
        else:
            # cache_hit is False - increment cache misses counter
            PrometheusLogger._inc_labeled_counter(
                self,
                self.litellm_cache_misses_metric,
                "litellm_cache_misses_metric",
                enum_values,
                label_context=label_context,
            )

        # Provider prompt caching metrics are independent of LiteLLM cache_hit.
        usage_obj: Final = (standard_logging_payload.get("metadata", {}) or {}).get("usage_object")
        if isinstance(usage_obj, dict):
            (
                provider_cache_read_tokens,
                provider_cache_creation_tokens,
            ) = PrometheusLogger._resolve_provider_cache_tokens(usage_obj)

            if provider_cache_read_tokens > 0:
                PrometheusLogger._inc_labeled_counter(
                    self,
                    self.litellm_provider_cache_read_input_tokens_metric,
                    "litellm_provider_cache_read_input_tokens_metric",
                    enum_values,
                    label_context=label_context,
                    amount=float(provider_cache_read_tokens),
                )

            if provider_cache_creation_tokens > 0:
                PrometheusLogger._inc_labeled_counter(
                    self,
                    self.litellm_provider_cache_creation_input_tokens_metric,
                    "litellm_provider_cache_creation_input_tokens_metric",
                    enum_values,
                    label_context=label_context,
                    amount=float(provider_cache_creation_tokens),
                )

    @staticmethod
    def _resolve_provider_cache_tokens(usage_obj: Mapping[str, object]) -> tuple[int, int]:
        # Prefer explicit provider cache fields when available.
        _read: Final = usage_obj.get("cache_read_input_tokens")
        _write: Final = usage_obj.get("cache_creation_input_tokens")

        provider_cache_read_tokens = _read if isinstance(_read, int) else 0
        provider_cache_creation_tokens = _write if isinstance(_write, int) else 0

        # Fallback to prompt_tokens_details (common normalization point).
        # Only fallback when the explicit field is genuinely absent (None).
        prompt_details: Final = usage_obj.get("prompt_tokens_details")
        if _read is None and isinstance(prompt_details, dict):
            cached_tokens: Final = prompt_details.get("cached_tokens")
            if isinstance(cached_tokens, int):
                provider_cache_read_tokens = cached_tokens

        if _write is None:
            write_tokens: Final = PrometheusLogger._resolve_cache_write_tokens(prompt_details)
            if write_tokens is not None:
                provider_cache_creation_tokens = write_tokens

        return provider_cache_read_tokens, provider_cache_creation_tokens

    @staticmethod
    def _resolve_cache_write_tokens(prompt_details: object) -> int | None:
        if not isinstance(prompt_details, dict):
            return None
        for key in ("cache_write_tokens", "cache_creation_tokens"):
            value = prompt_details.get(key)
            if isinstance(value, int) and not isinstance(value, bool):
                return value
        return None

    def _increment_mcp_tool_call_metrics(
        self,
        standard_logging_payload: StandardLoggingPayload,
        enum_values: UserAPIKeyLabelValues,
        response_cost: float,
    ) -> None:
        metadata: Final = standard_logging_payload.get("metadata")
        if not isinstance(metadata, dict):
            return
        mcp_meta: Final = metadata.get("mcp_tool_call_metadata")
        if not isinstance(mcp_meta, dict):
            return

        mcp_enum_values: Final = UserAPIKeyLabelValues(
            mcp_tool_name=mcp_meta.get("name"),
            mcp_server_name=mcp_meta.get("mcp_server_name"),
            hashed_api_key=enum_values.hashed_api_key,
            api_key_alias=enum_values.api_key_alias,
            team=enum_values.team,
            team_alias=enum_values.team_alias,
            user=enum_values.user,
            end_user=enum_values.end_user,
        )
        mcp_label_context: Final = PrometheusLabelFactoryContext(mcp_enum_values)

        PrometheusLogger._inc_labeled_counter(
            self,
            self.litellm_mcp_tool_calls_total,
            "litellm_mcp_tool_calls_total",
            mcp_enum_values,
            label_context=mcp_label_context,
        )

        if response_cost > 0:
            PrometheusLogger._inc_labeled_counter(
                self,
                self.litellm_mcp_tool_call_spend_metric,
                "litellm_mcp_tool_call_spend_metric",
                mcp_enum_values,
                label_context=mcp_label_context,
                amount=response_cost,
            )

    async def _increment_remaining_budget_metrics(
        self,
        user_api_team: str | None,
        user_api_team_alias: str | None,
        user_api_key: str | None,
        user_api_key_alias: str | None,
        litellm_params: dict,
        response_cost: float,
        user_id: str | None = None,
        user_api_key_org_id: str | None = None,
        end_user_id: str | None = None,
    ):
        if (
            isinstance(self.litellm_remaining_team_budget_metric, NoOpMetric)
            and isinstance(self.litellm_remaining_api_key_budget_metric, NoOpMetric)
            and isinstance(self.litellm_remaining_user_budget_metric, NoOpMetric)
            and isinstance(self.litellm_remaining_org_budget_metric, NoOpMetric)
            and self._customer_budget_gauges_are_noop()
        ):
            return

        _metadata: Final = litellm_params.get("metadata") or {}
        _team_spend: Final = _metadata.get("user_api_key_team_spend", None)
        _team_max_budget: Final = _metadata.get("user_api_key_team_max_budget", None)

        _api_key_spend: Final = _metadata.get("user_api_key_spend", None)
        _api_key_max_budget: Final = _metadata.get("user_api_key_max_budget", None)

        _user_spend: Final = _metadata.get("user_api_key_user_spend", None)
        _user_max_budget: Final = _metadata.get("user_api_key_user_max_budget", None)
        _user_email: Final = _metadata.get("user_api_key_user_email", None)
        _org_alias: Final = _metadata.get("user_api_key_org_alias", None)

        # Bound the per-request budget-metric emission so that slow Redis/DB
        # lookups under load cannot consume the whole LoggingWorker watchdog
        # (LOGGING_WORKER_MAX_TIME_PER_COROUTINE, default 20s) and get the entire
        # success-logging event cancelled. Budget gauges are also refreshed by the
        # periodic cron every PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES,
        # so dropping one slow per-request emission only loses sub-cron real-time
        # detail, not correctness.
        budget_metrics_timeout: Final = _get_budget_metrics_per_request_timeout()
        gather_coro: Final = asyncio.gather(
            self._set_api_key_budget_metrics_after_api_request(
                user_api_key=user_api_key,
                user_api_key_alias=user_api_key_alias,
                response_cost=response_cost,
                key_max_budget=_api_key_max_budget,
                key_spend=_api_key_spend,
                carried=KeyBudgetSnapshot.from_metadata(_metadata),
            ),
            self._set_team_budget_metrics_after_api_request(
                user_api_team=user_api_team,
                user_api_team_alias=user_api_team_alias,
                team_spend=_team_spend,
                team_max_budget=_team_max_budget,
                response_cost=response_cost,
                carried=TeamBudgetSnapshot.from_metadata(_metadata),
            ),
            self._set_user_budget_metrics_after_api_request(
                user_id=user_id,
                user_spend=_user_spend,
                user_max_budget=_user_max_budget,
                response_cost=response_cost,
                carried=UserBudgetSnapshot.from_metadata(_metadata),
                user_email=_user_email if isinstance(_user_email, str) else None,
            ),
            self._set_org_budget_metrics_after_api_request(
                org_id=user_api_key_org_id,
                response_cost=response_cost,
                carried=OrgBudgetSnapshot.from_metadata(_metadata),
                org_alias=_org_alias if isinstance(_org_alias, str) else None,
            ),
            self._set_customer_budget_metrics_after_api_request(
                end_user_id=end_user_id,
                response_cost=response_cost,
            ),
            return_exceptions=True,
        )
        try:
            results: Final = await asyncio.wait_for(gather_coro, timeout=budget_metrics_timeout)
        except asyncio.TimeoutError:
            verbose_logger.debug(
                "[Non-Blocking] Prometheus: per-request budget metric emission "
                "exceeded %ss under load; skipping (values are refreshed by the "
                "periodic budget-metrics cron job).",
                budget_metrics_timeout,
            )
            return
        for i, r in enumerate(results):
            if isinstance(r, Exception):
                verbose_logger.debug(
                    "[Non-Blocking] Prometheus: Budget metric lookup %s failed: %s",
                    ("key", "team", "user", "org", "customer")[i],
                    r,
                )

    def _increment_top_level_request_and_spend_metrics(
        self,
        end_user_id: str | None,
        user_api_key: str | None,
        user_api_key_alias: str | None,
        model: str | None,
        user_api_team: str | None,
        user_api_team_alias: str | None,
        user_id: str | None,
        response_cost: float,
        enum_values: UserAPIKeyLabelValues,
        label_context: PrometheusLabelFactoryContext | None = None,
    ):
        PrometheusLogger._inc_labeled_counter(
            self,
            self.litellm_requests_metric,
            "litellm_requests_metric",
            enum_values,
            label_context=label_context,
        )
        PrometheusLogger._inc_labeled_counter(
            self,
            self.litellm_spend_metric,
            "litellm_spend_metric",
            enum_values,
            label_context=label_context,
            amount=float(response_cost),
        )

    @staticmethod
    def _get_remaining_from_v3_rate_limit_headers(
        standard_logging_payload: StandardLoggingPayload | None,
        rate_limit_type: Literal["requests", "tokens"],
    ) -> int | None:
        """
        Read the per-(key, model) remaining value emitted by the v3 rate
        limiter (``parallel_request_limiter_v3.py``), which writes
        ``x-ratelimit-model_per_key-remaining-{requests,tokens}`` into
        ``standard_logging_object.hidden_params.additional_headers`` instead
        of the ``litellm-key-remaining-*`` metadata keys the legacy limiter
        sets. The header carries no model group; it always refers to this
        request's model group, which is what the gauges are labeled with.
        Values are written in-process as plain ints (never HTTP-serialized
        strings), so anything else is rejected rather than coerced.
        """
        if standard_logging_payload is None:
            return None
        return PrometheusLogger._get_int_from_v3_rate_limit_headers(
            standard_logging_payload=standard_logging_payload,
            header_name=f"x-ratelimit-model_per_key-remaining-{rate_limit_type}",
        )

    @staticmethod
    def _get_int_from_v3_rate_limit_headers(
        standard_logging_payload: StandardLoggingPayload,
        header_name: str,
    ) -> int | None:
        hidden_params: Final = standard_logging_payload.get("hidden_params")
        if hidden_params is None:
            return None
        additional_headers: Final[Mapping[str, object] | None] = hidden_params.get("additional_headers")
        if additional_headers is None:
            return None
        value: Final = additional_headers.get(header_name)
        if isinstance(value, bool) or not isinstance(value, int):
            return None
        return value

    def _set_key_and_team_rate_limit_metrics(
        self,
        standard_logging_payload: StandardLoggingPayload,
        enum_values: UserAPIKeyLabelValues,
    ) -> None:
        """
        Export the key-level and team-level RPM / TPM limit and current window
        usage from the ``x-ratelimit-{api_key,team}-{limit,remaining}-*``
        headers the v3 rate limiter mirrors into the logging payload. The
        limiter already read these counters (from Redis when configured) on
        the request path, so no extra store lookup happens here. Descriptors
        without a configured limit emit no header, so their series is removed
        rather than left at the value from before the limit was dropped.
        """
        descriptor_gauges: Final[
            tuple[tuple[Literal["api_key", "team"], DEFINED_PROMETHEUS_METRICS, Gauge, Gauge], ...]
        ] = (
            (
                "api_key",
                "litellm_api_key_rate_limit_allowed_metric",
                self.litellm_api_key_rate_limit_allowed_metric,
                self.litellm_api_key_rate_limit_used_metric,
            ),
            (
                "team",
                "litellm_team_rate_limit_allowed_metric",
                self.litellm_team_rate_limit_allowed_metric,
                self.litellm_team_rate_limit_used_metric,
            ),
        )
        for descriptor_key, metric_name, allowed_gauge, used_gauge in descriptor_gauges:
            for rate_limit_type in ("requests", "tokens"):
                self._set_rate_limit_allowed_and_used_gauges(
                    standard_logging_payload=standard_logging_payload,
                    enum_values=enum_values,
                    descriptor_key=descriptor_key,
                    metric_name=metric_name,
                    allowed_gauge=allowed_gauge,
                    used_gauge=used_gauge,
                    rate_limit_type=rate_limit_type,
                )

    def _set_rate_limit_allowed_and_used_gauges(
        self,
        standard_logging_payload: StandardLoggingPayload,
        enum_values: UserAPIKeyLabelValues,
        descriptor_key: Literal["api_key", "team"],
        metric_name: DEFINED_PROMETHEUS_METRICS,
        allowed_gauge: Gauge,
        used_gauge: Gauge,
        rate_limit_type: Literal["requests", "tokens"],
    ) -> None:
        limit: Final = self._get_int_from_v3_rate_limit_headers(
            standard_logging_payload=standard_logging_payload,
            header_name=f"x-ratelimit-{descriptor_key}-limit-{rate_limit_type}",
        )
        remaining: Final = self._get_int_from_v3_rate_limit_headers(
            standard_logging_payload=standard_logging_payload,
            header_name=f"x-ratelimit-{descriptor_key}-remaining-{rate_limit_type}",
        )
        labelled_values: Final = replace(enum_values, rate_limit_type=rate_limit_type)
        labelnames: Final = self.get_labels_for_metric(metric_name)
        labels: Final = prometheus_label_factory(
            supported_enum_labels=labelnames,
            enum_values=labelled_values,
            label_context=PrometheusLabelFactoryContext(labelled_values),
        )
        if limit is None or remaining is None:
            label_values: Final = tuple(labels.get(label) for label in labelnames)
            self._bounded_prometheus_series_tracker.remove_series(allowed_gauge, label_values)
            self._bounded_prometheus_series_tracker.remove_series(used_gauge, label_values)
            return
        allowed_gauge.labels(**labels).set(limit)
        used_gauge.labels(**labels).set(limit - remaining)

    def _set_virtual_key_rate_limit_metrics(
        self,
        user_api_key: str | None,
        user_api_key_alias: str | None,
        kwargs: dict,
        metadata: dict,
        model_id: str | None = None,
    ):
        from litellm.proxy.common_utils.callback_utils import (
            get_model_group_from_litellm_kwargs,
        )

        # Set remaining rpm/tpm for API Key + model
        # see parallel_request_limiter.py - variables are set there
        model_group: Final = get_model_group_from_litellm_kwargs(kwargs)
        remaining_requests_variable_name: Final = f"litellm-key-remaining-requests-{model_group}"
        remaining_tokens_variable_name: Final = f"litellm-key-remaining-tokens-{model_group}"
        standard_logging_payload: Final[StandardLoggingPayload | None] = kwargs.get("standard_logging_object")

        remaining_requests = metadata.get(remaining_requests_variable_name)
        if remaining_requests is None:
            remaining_requests = self._get_remaining_from_v3_rate_limit_headers(
                standard_logging_payload=standard_logging_payload, rate_limit_type="requests"
            )
        if remaining_requests is None:
            remaining_requests = sys.maxsize
        remaining_tokens = metadata.get(remaining_tokens_variable_name)
        if remaining_tokens is None:
            remaining_tokens = self._get_remaining_from_v3_rate_limit_headers(
                standard_logging_payload=standard_logging_payload, rate_limit_type="tokens"
            )
        if remaining_tokens is None:
            remaining_tokens = sys.maxsize

        enum_values: Final = UserAPIKeyLabelValues(
            hashed_api_key=user_api_key,
            api_key_alias=user_api_key_alias,
            model=model_group,
            model_id=model_id,
            custom_metadata_labels=get_custom_labels_from_metadata(
                metadata=_get_combined_custom_metadata_from_standard_logging_payload(
                    standard_logging_payload=kwargs.get("standard_logging_object")
                )
            ),
        )
        label_context: Final = PrometheusLabelFactoryContext(enum_values)
        requests_labels: Final = prometheus_label_factory(
            supported_enum_labels=self.get_labels_for_metric("litellm_remaining_api_key_requests_for_model"),
            enum_values=enum_values,
            label_context=label_context,
        )
        self.litellm_remaining_api_key_requests_for_model.labels(**requests_labels).set(remaining_requests)

        tokens_labels: Final = prometheus_label_factory(
            supported_enum_labels=self.get_labels_for_metric("litellm_remaining_api_key_tokens_for_model"),
            enum_values=enum_values,
            label_context=label_context,
        )
        self.litellm_remaining_api_key_tokens_for_model.labels(**tokens_labels).set(remaining_tokens)

    @staticmethod
    def _get_input_sequence_length(
        standard_logging_payload: StandardLoggingPayload,
        kwargs: Mapping[str, object],
        response_obj: object,
    ) -> str:
        prompt_tokens: Final = standard_logging_payload.get("prompt_tokens")
        if prompt_tokens:
            return get_input_sequence_length_bucket(prompt_tokens)
        combined_usage: Final = kwargs.get("combined_usage_object")
        if (
            combined_usage is not None
            and getattr(kwargs.get("_litellm_upstream_reported_usage"), "total_tokens", None) is not None
        ):
            return get_input_sequence_length_bucket(None)
        reported_usage: Final = (
            response_obj.get("usage") if isinstance(response_obj, dict) else getattr(response_obj, "usage", None)
        )
        if reported_usage is None and combined_usage is None:
            return get_input_sequence_length_bucket(None)
        usage_metadata: Final = standard_logging_payload["metadata"].get("usage_object")
        if isinstance(usage_metadata, Mapping):
            return get_input_sequence_length_bucket(usage_metadata.get("prompt_tokens"))
        if combined_usage is None and isinstance(response_obj, dict):
            from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup

            normalized_usage: Final[Mapping[str, object]] = StandardLoggingPayloadSetup.get_usage_as_dict(response_obj)
            return get_input_sequence_length_bucket(normalized_usage.get("prompt_tokens"))
        return get_input_sequence_length_bucket(prompt_tokens)

    def _set_latency_metrics(
        self,
        kwargs: dict,
        model: str | None,
        user_api_key: str | None,
        user_api_key_alias: str | None,
        user_api_team: str | None,
        user_api_team_alias: str | None,
        enum_values: UserAPIKeyLabelValues,
        label_context: PrometheusLabelFactoryContext | None = None,
        input_sequence_length: str | None = None,
    ):
        latency_enum_values: Final = (
            replace(enum_values, input_sequence_length=input_sequence_length)
            if input_sequence_length is not None
            else enum_values
        )
        latency_label_context: Final = (
            PrometheusLabelFactoryContext(latency_enum_values) if input_sequence_length is not None else label_context
        )
        # latency metrics
        end_time: Final[datetime] = kwargs.get("end_time") or datetime.now()
        start_time: Final[datetime | None] = kwargs.get("start_time")
        api_call_start_time: Final = kwargs.get("api_call_start_time", None)
        completion_start_time: Final = kwargs.get("completion_start_time", None)
        time_to_first_token_seconds: Final = self._safe_duration_seconds(
            start_time=api_call_start_time,
            end_time=completion_start_time,
        )
        if (
            time_to_first_token_seconds is not None
            and kwargs.get("stream", False) is True  # only emit for streaming requests
        ):
            _ttft_labels: Final = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(
                    metric_name="litellm_llm_api_time_to_first_token_metric"
                ),
                enum_values=latency_enum_values,
                label_context=latency_label_context,
            )
            self.litellm_llm_api_time_to_first_token_metric.labels(**_ttft_labels).observe(time_to_first_token_seconds)
            self._track_end_user_metric_series(
                self.litellm_llm_api_time_to_first_token_metric,
                "litellm_llm_api_time_to_first_token_metric",
                _ttft_labels,
            )
        else:
            verbose_logger.debug(
                "Time to first token metric not emitted, stream option in model_parameters is not True"
            )

        api_call_total_time_seconds: Final = self._safe_duration_seconds(
            start_time=api_call_start_time,
            end_time=end_time,
        )
        if api_call_total_time_seconds is not None:
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_llm_api_latency_metric"),
                enum_values=latency_enum_values,
                label_context=latency_label_context,
            )
            self.litellm_llm_api_latency_metric.labels(**_labels).observe(api_call_total_time_seconds)
            self._track_end_user_metric_series(
                self.litellm_llm_api_latency_metric,
                "litellm_llm_api_latency_metric",
                _labels,
            )

        # request queue time (time from arrival to processing start) -- read first so
        # it can be folded into the total-latency metric below. start_time/end_time
        # only span from after auth completes, so without this the "total" latency
        # metric silently excludes auth and pre-call hook time.
        _litellm_params: Final = kwargs.get("litellm_params", {}) or {}
        queue_time_seconds: Final = (_litellm_params.get("metadata") or {}).get("queue_time_seconds")

        # total request latency: true end-to-end, from request arrival (queue_time_seconds,
        # when available) through the end of processing.
        total_time_seconds: Final = self._safe_duration_seconds(
            start_time=start_time,
            end_time=end_time,
        )
        if total_time_seconds is not None:
            _observed_total_time_seconds: Final = (
                total_time_seconds + queue_time_seconds
                if queue_time_seconds is not None and queue_time_seconds >= 0
                else total_time_seconds
            )
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_request_total_latency_metric"),
                enum_values=latency_enum_values,
                label_context=latency_label_context,
            )
            self.litellm_request_total_latency_metric.labels(**_labels).observe(_observed_total_time_seconds)
            self._track_end_user_metric_series(
                self.litellm_request_total_latency_metric,
                "litellm_request_total_latency_metric",
                _labels,
            )

        if queue_time_seconds is not None and queue_time_seconds >= 0:
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_request_queue_time_seconds"),
                enum_values=enum_values,
                label_context=label_context,
            )
            self.litellm_request_queue_time_metric.labels(**_labels).observe(queue_time_seconds)
            self._track_end_user_metric_series(
                self.litellm_request_queue_time_metric,
                "litellm_request_queue_time_seconds",
                _labels,
            )

    async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
        verbose_logger.debug(
            "prometheus Logging - Enters failure logging function (kwargs keys: %s)",
            list(kwargs.keys()) if isinstance(kwargs, dict) else type(kwargs).__name__,
        )

        standard_logging_payload: Final[StandardLoggingPayload] = kwargs.get("standard_logging_object", {})

        if self._should_skip_metrics_for_invalid_key(kwargs=kwargs, standard_logging_payload=standard_logging_payload):
            return

        model: Final = kwargs.get("model", "")

        litellm_params: Final = kwargs.get("litellm_params", {}) or {}
        get_end_user_id_for_cost_tracking: Final = _get_cached_end_user_id_for_cost_tracking()

        end_user_id: Final = get_end_user_id_for_cost_tracking(litellm_params, service_type="prometheus")
        user_id: Final = standard_logging_payload["metadata"]["user_api_key_user_id"]
        user_api_key: Final = standard_logging_payload["metadata"]["user_api_key_hash"]
        user_api_key_alias: Final = standard_logging_payload["metadata"]["user_api_key_alias"]
        user_api_team: Final = standard_logging_payload["metadata"]["user_api_key_team_id"]
        user_api_team_alias: Final = standard_logging_payload["metadata"]["user_api_key_team_alias"]
        user_api_key_org_id: Final = standard_logging_payload["metadata"].get("user_api_key_org_id")

        try:
            enum_values: Final = UserAPIKeyLabelValues(
                end_user=end_user_id,
                hashed_api_key=user_api_key,
                api_key_alias=user_api_key_alias,
                model=model,
                team=user_api_team,
                team_alias=user_api_team_alias,
                user=user_id,
                model_id=standard_logging_payload.get("model_id", ""),
                custom_metadata_labels=get_custom_labels_from_metadata(
                    metadata=_get_combined_custom_metadata_from_standard_logging_payload(
                        standard_logging_payload=standard_logging_payload
                    )
                ),
            )
            PrometheusLogger._inc_labeled_counter(
                self,
                self.litellm_llm_api_failed_requests_metric,
                "litellm_llm_api_failed_requests_metric",
                enum_values,
            )
            self.set_llm_deployment_failure_metrics(kwargs)
            await self._set_org_budget_metrics_after_api_request(
                org_id=user_api_key_org_id,
                response_cost=0,
            )
        except Exception as e:
            verbose_logger.exception("prometheus Layer Error(): Exception occured - %s", e)

    def _extract_status_code(
        self,
        kwargs: dict | None = None,
        enum_values: UserAPIKeyLabelValues | None = None,
        exception: Exception | None = None,
    ) -> int | None:
        """
        Extract HTTP status code from various input formats for validation.

        This is a centralized helper to extract status code from different
        callback function signatures. Handles both ProxyException (uses 'code')
        and standard exceptions (uses 'status_code').

        Args:
            kwargs: Dictionary potentially containing 'exception' key
            enum_values: Object with 'status_code' attribute
            exception: Exception object to extract status code from directly

        Returns:
            Status code as integer if found, None otherwise
        """
        status_code: int | None = None

        # Try from enum_values first (most common in our callbacks)
        if enum_values and hasattr(enum_values, "status_code") and enum_values.status_code:
            try:
                status_code = int(enum_values.status_code)
            except (ValueError, TypeError):
                pass

        if not status_code and exception:
            # ProxyException uses 'code' attribute, other exceptions may use 'status_code'
            status_code = getattr(exception, "status_code", None) or getattr(exception, "code", None)
            if status_code is not None:
                try:
                    status_code = int(status_code)
                except (ValueError, TypeError):
                    status_code = None

        if not status_code and kwargs:
            exception_in_kwargs: Final = kwargs.get("exception")
            if exception_in_kwargs:
                status_code = getattr(exception_in_kwargs, "status_code", None) or getattr(
                    exception_in_kwargs, "code", None
                )
                if status_code is not None:
                    try:
                        status_code = int(status_code)
                    except (ValueError, TypeError):
                        status_code = None

        return status_code

    def _is_invalid_api_key_request(
        self,
        status_code: int | None,
        exception: Exception | None = None,
    ) -> bool:
        """
        Determine if a request has an invalid API key based on status code and exception.

        This method prevents invalid authentication attempts from being recorded in
        Prometheus metrics. A 401 status code is the definitive indicator of authentication
        failure. Additionally, we check exception messages for authentication error patterns
        to catch cases where the exception hasn't been converted to a ProxyException yet.

        Args:
            status_code: HTTP status code (401 indicates authentication error)
            exception: Exception object to check for auth-related error messages

        Returns:
            True if the request has an invalid API key and metrics should be skipped,
            False otherwise
        """
        if status_code == 401:
            return True

        # Handle cases where AssertionError is raised before conversion to ProxyException
        if exception is not None:
            exception_str: Final = str(exception).lower()
            auth_error_patterns: Final = [
                "virtual key expected",
                "expected to start with 'sk-'",
                "authentication error",
                "invalid api key",
                "api key not valid",
            ]
            if any(pattern in exception_str for pattern in auth_error_patterns):
                return True

        return False

    def _should_skip_metrics_for_invalid_key(
        self,
        kwargs: dict | None = None,
        user_api_key_dict: UserAPIKeyAuth | None = None,
        enum_values: UserAPIKeyLabelValues | None = None,
        standard_logging_payload: dict | StandardLoggingPayload | None = None,
        exception: Exception | None = None,
    ) -> bool:
        """
        Determine if Prometheus metrics should be skipped for invalid API key requests.

        This is a centralized validation method that extracts status code and exception
        information from various callback function signatures and determines if the request
        represents an invalid API key attempt that should be filtered from metrics.

        Args:
            kwargs: Dictionary potentially containing exception and other data
            user_api_key_dict: User API key authentication object (currently unused)
            enum_values: Object with status_code attribute
            standard_logging_payload: Standard logging payload dictionary
            exception: Exception object to check directly

        Returns:
            True if metrics should be skipped (invalid key detected), False otherwise
        """
        status_code: Final = self._extract_status_code(
            kwargs=kwargs,
            enum_values=enum_values,
            exception=exception,
        )

        if exception is None and kwargs:
            exception = kwargs.get("exception")

        if self._is_invalid_api_key_request(status_code, exception=exception):
            verbose_logger.debug(
                "Skipping Prometheus metrics for invalid API key request: status_code=%s, exception=%s",
                status_code,
                type(exception).__name__ if exception else None,
            )
            return True

        return False

    @staticmethod
    def _extract_api_provider_from_request_data(request_data: dict) -> str | None:
        """
        Best-effort provider for the client-side failure path.

        A request can fail before a deployment is resolved, so the provider is
        not always known. Prefer the resolved ``custom_llm_provider`` on
        ``litellm_params``, then any provider recovered onto a partial
        ``standard_logging_object`` (e.g. a stream that broke mid-flight), and
        finally infer it from the requested model name (e.g. ``gpt-4o-mini`` ->
        ``openai``) since the proxy's failure ``request_data`` usually carries
        only the client-supplied model. Return ``None`` when it cannot be
        determined so the label emits empty rather than a guess.
        """
        litellm_params: Final = request_data.get("litellm_params") or {}
        provider = litellm_params.get("custom_llm_provider")
        if provider:
            return provider
        standard_logging_object: Final = request_data.get("standard_logging_object") or {}
        provider = standard_logging_object.get("custom_llm_provider")
        if provider:
            return provider
        model: Final = litellm_params.get("model") or request_data.get("model")
        if not model:
            return None
        try:
            return litellm.get_llm_provider(model=model)[1] or None
        except litellm.exceptions.BadRequestError:
            return None
        except Exception as e:  # noqa: BLE001 - metrics labeling must never break request/failure handling
            verbose_logger.debug(
                "prometheus: unexpected error inferring api_provider from model=%s: %s",
                model,
                e,
            )
            return None

    @staticmethod
    def _extract_api_provider_from_exception(exception: Exception) -> str | None:
        if not isinstance(exception, litellm.exceptions.RateLimitError):
            return None
        llm_provider: Final = exception.llm_provider
        if not llm_provider or llm_provider == PROXY_LLM_PROVIDER_FALLBACK:
            return None
        return llm_provider

    async def async_post_call_failure_hook(
        self,
        request_data: dict,
        original_exception: Exception,
        user_api_key_dict: UserAPIKeyAuth,
        traceback_str: str | None = None,
    ):
        """
        Track client side failures

        Proxy level tracking - failed client side requests

        See :attr:`PrometheusMetricLabels.litellm_proxy_failed_requests_metric`
        for the authoritative list of labels emitted on this metric.
        """
        from litellm.litellm_core_utils.litellm_logging import (
            StandardLoggingPayloadSetup,
        )

        status_code: Final = self._extract_status_code(exception=original_exception)

        try:
            _tags: Final = StandardLoggingPayloadSetup._get_request_tags(
                litellm_params=request_data,
                proxy_server_request=request_data.get("proxy_server_request", {}),
            )
            _metadata: Final = request_data.get("metadata", {}) or {}
            model_id: Final = _metadata.get("model_info", {}).get("id") or request_data.get("model_info", {}).get("id")
            rate_limit_category, rate_limit_type = self._extract_rate_limit_labels(original_exception)
            api_provider: Final = self._extract_api_provider_from_request_data(
                request_data
            ) or self._extract_api_provider_from_exception(original_exception)
            enum_values: Final = UserAPIKeyLabelValues(
                end_user=user_api_key_dict.end_user_id,
                user=user_api_key_dict.user_id,
                user_email=user_api_key_dict.user_email,
                hashed_api_key=None if status_code == 401 else user_api_key_dict.api_key,
                api_key_alias=user_api_key_dict.key_alias,
                team=user_api_key_dict.team_id,
                team_alias=user_api_key_dict.team_alias,
                org_id=user_api_key_dict.org_id,
                org_alias=user_api_key_dict.organization_alias,
                requested_model=_bounded_requested_model_label(request_data.get("model", "")),
                status_code=str(status_code),
                exception_status=str(status_code),
                exception_class=self._get_exception_class_name(original_exception),
                rate_limit_category=rate_limit_category,
                rate_limit_type=rate_limit_type,
                tags=_tags,
                route=user_api_key_dict.request_route,
                client_ip=_metadata.get("requester_ip_address"),
                user_agent=_metadata.get("user_agent"),
                model_id=model_id,
                api_provider=api_provider,
                stream=(str(request_data.get("stream")) if litellm.prometheus_emit_stream_label else None),
            )
            _label_ctx: Final = PrometheusLabelFactoryContext(enum_values)
            PrometheusLogger._inc_labeled_counter(
                self,
                self.litellm_proxy_failed_requests_metric,
                "litellm_proxy_failed_requests_metric",
                enum_values,
                label_context=_label_ctx,
            )
            PrometheusLogger._inc_labeled_counter(
                self,
                self.litellm_proxy_total_requests_metric,
                "litellm_proxy_total_requests_metric",
                enum_values,
                label_context=_label_ctx,
            )

        except Exception as e:
            verbose_logger.exception("prometheus Layer Error(): Exception occured - %s", e)

    async def async_post_call_success_hook(self, data: dict, user_api_key_dict: UserAPIKeyAuth, response):
        """
        Proxy level tracking - triggered when the proxy responds with a success response to the client

        Note: litellm_proxy_total_requests_metric is NOT incremented here to avoid
        double-counting. It is incremented in async_log_success_event which fires
        for all successful requests (both streaming and non-streaming).
        """

    def _safe_get(self, obj: object, key: str, default: object = None) -> Any:
        """Get value from dict or Pydantic model."""
        if obj is None:
            return default
        if isinstance(obj, dict):
            return obj.get(key, default)
        return getattr(obj, key, default)

    def _extract_deployment_failure_label_values(self, request_kwargs: dict) -> dict[str, str | None]:
        """
        Extract label values for deployment failure metrics from all available
        sources in request_kwargs. Falls back to litellm_params metadata and
        user_api_key_auth when standard_logging_payload has None values.
        """
        standard_logging_payload: Final = request_kwargs.get("standard_logging_object", {}) or {}
        _litellm_params: Final = request_kwargs.get("litellm_params", {}) or {}
        _metadata_raw: Final[object] = self._safe_get(standard_logging_payload, "metadata") or {}
        if isinstance(_metadata_raw, dict):
            _metadata = _metadata_raw
        else:
            _metadata = {
                "user_api_key_alias": getattr(_metadata_raw, "user_api_key_alias", None),
                "user_api_key_user_email": getattr(_metadata_raw, "user_api_key_user_email", None),
                "user_api_key_team_id": getattr(_metadata_raw, "user_api_key_team_id", None),
                "user_api_key_team_alias": getattr(_metadata_raw, "user_api_key_team_alias", None),
                "user_api_key_hash": getattr(_metadata_raw, "user_api_key_hash", None),
                "requester_ip_address": getattr(_metadata_raw, "requester_ip_address", None),
                "user_agent": getattr(_metadata_raw, "user_agent", None),
            }
        _litellm_params_metadata: Final = _litellm_params.get("metadata", {}) or {}

        # Extract user_api_key_auth if present (proxy injects this, skipped in merge)
        user_api_key_auth: Final = _litellm_params_metadata.get("user_api_key_auth")

        def _get_api_key_alias() -> str | None:
            val = _metadata.get("user_api_key_alias")
            if val is not None:
                return val
            val = _litellm_params_metadata.get("user_api_key_alias")
            if val is not None:
                return val
            if user_api_key_auth is not None:
                return getattr(user_api_key_auth, "key_alias", None)
            return None

        def _get_user_email() -> str | None:
            from_metadata: Final = _metadata.get("user_api_key_user_email")
            if from_metadata is not None:
                return from_metadata
            from_params: Final = _litellm_params_metadata.get("user_api_key_user_email")
            if from_params is not None:
                return from_params
            if user_api_key_auth is not None:
                return self._safe_get(user_api_key_auth, "user_email")
            return None

        def _get_team_id() -> str | None:
            val = _metadata.get("user_api_key_team_id")
            if val is not None:
                return val
            val = _litellm_params_metadata.get("user_api_key_team_id")
            if val is not None:
                return val
            if user_api_key_auth is not None:
                return getattr(user_api_key_auth, "team_id", None)
            return None

        def _get_team_alias() -> str | None:
            val = _metadata.get("user_api_key_team_alias")
            if val is not None:
                return val
            val = _litellm_params_metadata.get("user_api_key_team_alias")
            if val is not None:
                return val
            if user_api_key_auth is not None:
                return getattr(user_api_key_auth, "team_alias", None)
            return None

        def _get_hashed_api_key() -> str | None:
            val = _metadata.get("user_api_key_hash")
            if val is not None:
                return val
            val = _litellm_params_metadata.get("user_api_key_hash")
            if val is not None:
                return val
            if user_api_key_auth is not None:
                return getattr(user_api_key_auth, "api_key", None) or getattr(user_api_key_auth, "api_key_hash", None)
            return None

        return {
            "api_key_alias": _get_api_key_alias(),
            "user_email": _get_user_email(),
            "team": _get_team_id(),
            "team_alias": _get_team_alias(),
            "hashed_api_key": _get_hashed_api_key(),
            "client_ip": _metadata.get("requester_ip_address") or _litellm_params_metadata.get("requester_ip_address"),
            "user_agent": _metadata.get("user_agent") or _litellm_params_metadata.get("user_agent"),
        }

    def set_llm_deployment_failure_metrics(self, request_kwargs: dict):
        """
        Sets Failure metrics when an LLM API call fails

        - mark the deployment as partial outage
        - increment deployment failure responses metric
        - increment deployment total requests metric

        Args:
            request_kwargs: dict

        """
        try:
            verbose_logger.debug("setting remaining tokens requests metric")
            standard_logging_payload: Final[StandardLoggingPayload] = request_kwargs.get("standard_logging_object", {})
            _litellm_params: Final = request_kwargs.get("litellm_params", {}) or {}
            litellm_model_name: Final = request_kwargs.get("model", None)
            model_group = standard_logging_payload.get("model_group", None)
            api_base: Final = standard_logging_payload.get("api_base", None)
            model_id = standard_logging_payload.get("model_id", None)
            exception: Final = request_kwargs.get("exception", None)

            # Fallback: model_id from litellm_metadata.model_info
            if model_id is None:
                _model_info: Final = (
                    (_litellm_params.get("litellm_metadata") or {}).get("model_info")
                    or (_litellm_params.get("metadata") or {}).get("model_info")
                    or {}
                )
                model_id = _model_info.get("id")

            # Fallback: model_group from litellm_metadata
            if model_group is None:
                model_group = (_litellm_params.get("litellm_metadata") or {}).get("model_group") or (
                    _litellm_params.get("metadata") or {}
                ).get("model_group")

            llm_provider: Final = _litellm_params.get("custom_llm_provider", None)

            if self._should_skip_metrics_for_invalid_key(
                kwargs=request_kwargs,
                standard_logging_payload=standard_logging_payload,
            ):
                return

            # Extract context labels from all available sources (fix for None labels)
            fallback_values: Final = self._extract_deployment_failure_label_values(request_kwargs)
            _metadata: Final = standard_logging_payload.get("metadata", {}) or {}
            hashed_api_key: Final = fallback_values.get("hashed_api_key") or _metadata.get("user_api_key_hash")
            api_key_alias: Final = fallback_values.get("api_key_alias") or _metadata.get("user_api_key_alias")
            user_email: Final = fallback_values.get("user_email")
            team: Final = fallback_values.get("team") or _metadata.get("user_api_key_team_id")
            team_alias: Final = fallback_values.get("team_alias") or _metadata.get("user_api_key_team_alias")
            client_ip: Final = fallback_values.get("client_ip") or _metadata.get("requester_ip_address")
            user_agent: Final = fallback_values.get("user_agent") or _metadata.get("user_agent")

            # exception_status: prefer status_code, fallback to exception class for known types
            exception_status = None
            if exception is not None:
                exception_status = str(getattr(exception, "status_code", None))
                if exception_status == "None" or not exception_status:
                    code: Final = getattr(exception, "code", None)
                    if code is not None:
                        exception_status = str(code)

            # On LiteLLM-side rejects (no deployment picked), route request_kwargs["model"]
            # into requested_model and leave deployment-scoped labels empty.
            deployment_selected: Final = bool(model_id)
            if deployment_selected:
                label_litellm_model_name = litellm_model_name
                label_model_id = model_id
                label_api_base = api_base
                label_api_provider = llm_provider
                label_requested_model = model_group or litellm_model_name
            else:
                label_litellm_model_name = ""
                label_model_id = ""
                label_api_base = ""
                label_api_provider = ""
                label_requested_model = (
                    _bounded_requested_model_label(litellm_model_name or model_group, router_originated=True) or ""
                )

            enum_values: Final = UserAPIKeyLabelValues(
                litellm_model_name=label_litellm_model_name,
                model_id=label_model_id,
                api_base=label_api_base,
                api_provider=label_api_provider,
                exception_status=exception_status,
                exception_class=(self._get_exception_class_name(exception) if exception else None),
                requested_model=label_requested_model,
                hashed_api_key=hashed_api_key,
                api_key_alias=api_key_alias,
                user_email=user_email,
                team=team,
                team_alias=team_alias,
                tags=standard_logging_payload.get("request_tags", []),
                client_ip=client_ip,
                user_agent=user_agent,
            )

            """
            log these labels
            ["litellm_model_name", "model_id", "api_base", "api_provider"]
            """
            # Only mark a deployment outage when one was actually picked.
            if deployment_selected:
                self.set_deployment_partial_outage(
                    litellm_model_name=litellm_model_name or "",
                    model_id=model_id,
                    api_base=api_base,
                    api_provider=llm_provider or "",
                )
            _deployment_label_ctx: Final = PrometheusLabelFactoryContext(enum_values)
            if exception is not None:
                PrometheusLogger._inc_labeled_counter(
                    self,
                    self.litellm_deployment_failure_responses,
                    "litellm_deployment_failure_responses",
                    enum_values,
                    label_context=_deployment_label_ctx,
                )

            PrometheusLogger._inc_labeled_counter(
                self,
                self.litellm_deployment_total_requests,
                "litellm_deployment_total_requests",
                enum_values,
                label_context=_deployment_label_ctx,
            )

        except Exception as e:
            verbose_logger.debug("Prometheus Error: set_llm_deployment_failure_metrics. Exception occured - %s", e)

    def _set_deployment_tpm_rpm_limit_metrics(
        self,
        model_info: dict,
        litellm_params: dict,
        litellm_model_name: str | None,
        model_id: str | None,
        api_base: str | None,
        llm_provider: str | None,
    ):
        """
        Set the deployment TPM and RPM limits metrics
        """
        tpm: Final = model_info.get("tpm") or litellm_params.get("tpm")
        rpm: Final = model_info.get("rpm") or litellm_params.get("rpm")

        if tpm is not None:
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_deployment_tpm_limit"),
                enum_values=UserAPIKeyLabelValues(
                    litellm_model_name=litellm_model_name,
                    model_id=model_id,
                    api_base=api_base,
                    api_provider=llm_provider,
                ),
            )
            self.litellm_deployment_tpm_limit.labels(**_labels).set(tpm)

        if rpm is not None:
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_deployment_rpm_limit"),
                enum_values=UserAPIKeyLabelValues(
                    litellm_model_name=litellm_model_name,
                    model_id=model_id,
                    api_base=api_base,
                    api_provider=llm_provider,
                ),
            )
            self.litellm_deployment_rpm_limit.labels(**_labels).set(rpm)

    async def _async_set_router_remaining_metrics(
        self,
        standard_logging_payload: StandardLoggingPayload,
        enum_values: UserAPIKeyLabelValues,
        label_context: PrometheusLabelFactoryContext | None = None,
    ) -> None:
        """
        Populate ``litellm_remaining_tokens_metric`` /
        ``litellm_remaining_requests_metric`` from the router's internal usage
        counters when the upstream provider did not return
        ``x-ratelimit-remaining-*`` response headers.

        OpenAI / Anthropic / Azure return remaining tokens/requests in response
        headers, but Bedrock and Vertex AI do not. This fallback computes
        ``configured_limit - current_usage`` via
        ``Router.get_remaining_model_group_usage`` so the same gauges are
        emitted for every provider when tpm/rpm is configured on the
        deployment.
        """
        try:
            additional_headers: Final = (standard_logging_payload.get("hidden_params", {}) or {}).get(
                "additional_headers"
            ) or {}

            already_have_tokens: Final = additional_headers.get("x_ratelimit_remaining_tokens") is not None
            already_have_requests: Final = additional_headers.get("x_ratelimit_remaining_requests") is not None
            if already_have_tokens and already_have_requests:
                return

            model_group: Final = standard_logging_payload.get("model_group")
            if not model_group:
                return

            try:
                from litellm.proxy.proxy_server import llm_router
            except ImportError:
                llm_router = None

            if llm_router is None:
                return

            try:
                remaining_usage: Final = await llm_router.get_remaining_model_group_usage(model_group)
            except Exception as e:
                verbose_logger.exception(
                    "Prometheus: get_remaining_model_group_usage failed for model_group=%s: %s",
                    model_group,
                    e,
                )
                return

            if not remaining_usage:
                return

            remaining_tokens: Final = remaining_usage.get("x-ratelimit-remaining-tokens")
            remaining_requests: Final = remaining_usage.get("x-ratelimit-remaining-requests")

            if not already_have_tokens and remaining_tokens is not None:
                _labels = prometheus_label_factory(
                    supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_remaining_tokens_metric"),
                    enum_values=enum_values,
                    label_context=label_context,
                )
                self.litellm_remaining_tokens_metric.labels(**_labels).set(remaining_tokens)

            if not already_have_requests and remaining_requests is not None:
                _labels = prometheus_label_factory(
                    supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_remaining_requests_metric"),
                    enum_values=enum_values,
                    label_context=label_context,
                )
                self.litellm_remaining_requests_metric.labels(**_labels).set(remaining_requests)
        except Exception as e:
            verbose_logger.exception("Prometheus Error: _async_set_router_remaining_metrics. Exception occured - %s", e)

    def set_llm_deployment_success_metrics(
        self,
        request_kwargs: dict,
        start_time,
        end_time,
        enum_values: UserAPIKeyLabelValues,
        output_tokens: float = 1.0,
        label_context: PrometheusLabelFactoryContext | None = None,
    ):
        try:
            verbose_logger.debug("setting remaining tokens requests metric")
            standard_logging_payload: StandardLoggingPayload | None = request_kwargs.get("standard_logging_object")

            if standard_logging_payload is None:
                return

            # Skip recording metrics for invalid API key requests
            if self._should_skip_metrics_for_invalid_key(
                kwargs=request_kwargs,
                enum_values=enum_values,
                standard_logging_payload=standard_logging_payload,
            ):
                return

            api_base: Final = standard_logging_payload["api_base"]
            _litellm_params: Final = request_kwargs.get("litellm_params", {}) or {}
            _metadata: Final = get_litellm_metadata_from_kwargs(request_kwargs)
            litellm_model_name: Final = request_kwargs.get("model", None)
            llm_provider: Final = _litellm_params.get("custom_llm_provider", None)
            _model_info: Final = _metadata.get("model_info") or {}
            model_id: Final = _model_info.get("id", None)

            if _model_info or _litellm_params:
                self._set_deployment_tpm_rpm_limit_metrics(
                    model_info=_model_info,
                    litellm_params=_litellm_params,
                    litellm_model_name=litellm_model_name,
                    model_id=model_id,
                    api_base=api_base,
                    llm_provider=llm_provider,
                )

            remaining_requests: int | None = None
            remaining_tokens: int | None = None
            if additional_headers := standard_logging_payload["hidden_params"]["additional_headers"]:
                # OpenAI / OpenAI Compatible headers
                remaining_requests = additional_headers.get("x_ratelimit_remaining_requests", None)
                remaining_tokens = additional_headers.get("x_ratelimit_remaining_tokens", None)

            if litellm_overhead_time_ms := standard_logging_payload["hidden_params"].get("litellm_overhead_time_ms"):
                _labels = prometheus_label_factory(
                    supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_overhead_latency_metric"),
                    enum_values=enum_values,
                    label_context=label_context,
                )
                self.litellm_overhead_latency_metric.labels(**_labels).observe(
                    litellm_overhead_time_ms / 1000
                )  # set as seconds

            self._set_overhead_with_guardrails_metric(
                standard_logging_payload=standard_logging_payload,
                enum_values=enum_values,
                label_context=label_context,
            )

            if remaining_requests:
                """
                "model_group",
                "api_provider",
                "api_base",
                "litellm_model_name"
                """
                _labels = prometheus_label_factory(
                    supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_remaining_requests_metric"),
                    enum_values=enum_values,
                    label_context=label_context,
                )
                self.litellm_remaining_requests_metric.labels(**_labels).set(remaining_requests)

            if remaining_tokens:
                _labels = prometheus_label_factory(
                    supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_remaining_tokens_metric"),
                    enum_values=enum_values,
                    label_context=label_context,
                )
                self.litellm_remaining_tokens_metric.labels(**_labels).set(remaining_tokens)

            """
            log these labels
            ["litellm_model_name", "requested_model", model_id", "api_base", "api_provider"]
            """
            self.set_deployment_healthy(
                litellm_model_name=litellm_model_name or "",
                model_id=model_id or "",
                api_base=api_base or "",
                api_provider=llm_provider or "",
            )

            PrometheusLogger._inc_labeled_counter(
                self,
                self.litellm_deployment_success_responses,
                "litellm_deployment_success_responses",
                enum_values,
                label_context=label_context,
            )
            PrometheusLogger._inc_labeled_counter(
                self,
                self.litellm_deployment_total_requests,
                "litellm_deployment_total_requests",
                enum_values,
                label_context=label_context,
            )

            # Track deployment Latency
            response_ms: Final[timedelta] = end_time - start_time
            time_to_first_token_response_time: timedelta | None = None

            if request_kwargs.get("stream", None) is not None and request_kwargs["stream"] is True:
                # only log ttft for streaming request
                time_to_first_token_response_time = request_kwargs.get("completion_start_time", end_time) - start_time

            # use the metric that is not None
            # if streaming - use time_to_first_token_response
            # if not streaming - use response_ms
            _latency: Final[timedelta] = time_to_first_token_response_time or response_ms
            _latency_seconds: Final = _latency.total_seconds()

            # latency per output token
            latency_per_token = None
            if output_tokens is not None and output_tokens > 0:
                latency_per_token = _latency_seconds / output_tokens
                _labels = prometheus_label_factory(
                    supported_enum_labels=self.get_labels_for_metric(
                        metric_name="litellm_deployment_latency_per_output_token"
                    ),
                    enum_values=enum_values,
                    label_context=label_context,
                )
                self.litellm_deployment_latency_per_output_token.labels(**_labels).observe(latency_per_token)

        except Exception as e:
            verbose_logger.exception("Prometheus Error: set_llm_deployment_success_metrics. Exception occured - %s", e)
            return

    def _record_guardrail_metrics(
        self,
        guardrail_name: str,
        latency_seconds: float,
        status: str,
        error_type: str | None,
        hook_type: str,
    ):
        """
        Record guardrail metrics for prometheus.

        Args:
            guardrail_name: Name of the guardrail
            latency_seconds: Execution latency in seconds
            status: "success", "error", or "intervened"
            error_type: Type of error if any, None otherwise
            hook_type: "pre_call", "during_call", or "post_call"
        """
        try:
            # Record latency
            self.litellm_guardrail_latency_metric.labels(
                guardrail_name=guardrail_name,
                status=status,
                error_type=error_type or "none",
                hook_type=hook_type,
            ).observe(latency_seconds)

            # Record request count
            self.litellm_guardrail_requests_total.labels(
                guardrail_name=guardrail_name,
                status=status,
                hook_type=hook_type,
            ).inc()

            # Record error count if there was an error
            if status == "error" and error_type:
                self.litellm_guardrail_errors_total.labels(
                    guardrail_name=guardrail_name,
                    error_type=error_type,
                    hook_type=hook_type,
                ).inc()
        except Exception as e:
            verbose_logger.debug("Error recording guardrail metrics: %s", e)

    ########################################
    # Managed Batch Metric Recording Methods
    ########################################

    def record_managed_batch_created(
        self,
        model: str | None,
        api_provider: str | None,
        user: str | None,
        user_email: str | None,
        api_key_alias: str | None,
    ):
        try:
            self.litellm_managed_batch_created_total.labels(
                model=model,
                api_provider=api_provider,
                user=user,
                user_email=user_email,
                api_key_alias=api_key_alias,
            ).inc()
        except Exception as e:
            verbose_logger.warning("Error recording batch created metric: %s", e)

    def record_managed_file_size(
        self,
        size_bytes: int,
        purpose: str,
        file_type: str,
        model: str | None = None,
        api_provider: str | None = None,
        user: str | None = None,
    ):
        """Record the size of a managed file. Uses a gauge (last-seen value per label combination)."""
        try:
            self.litellm_managed_file_size_bytes.labels(
                purpose=purpose,
                file_type=file_type,
                model=model or "",
                api_provider=api_provider or "",
                user=user or "",
            ).set(size_bytes)
        except Exception as e:
            verbose_logger.warning("Error recording file size metric: %s", e)

    def record_managed_batch_duration(
        self,
        duration_seconds: float,
        model: str | None = None,
        api_provider: str | None = None,
    ):
        try:
            self.litellm_managed_batch_duration_seconds.labels(
                model=model or "",
                api_provider=api_provider or "",
            ).observe(duration_seconds)
        except Exception as e:
            verbose_logger.warning("Error recording batch duration metric: %s", e)

    def record_managed_file_created(
        self,
        model: str | None,
        api_provider: str | None,
        user: str | None,
        user_email: str | None,
        api_key_alias: str | None,
    ):
        try:
            self.litellm_managed_file_created_total.labels(
                model=model,
                api_provider=api_provider,
                user=user,
                user_email=user_email,
                api_key_alias=api_key_alias,
            ).inc()
        except Exception as e:
            verbose_logger.warning("Error recording file created metric: %s", e)

    def record_managed_file_deleted(self, result: str):
        """Record a managed file deletion attempt. result is 'success' or 'blocked'."""
        try:
            self.litellm_managed_file_deleted_total.labels(result=result).inc()
        except Exception as e:
            verbose_logger.warning("Error recording file deleted metric: %s", e)

    def record_check_batch_cost_run(
        self,
        jobs_polled: int,
        processed_models: list[tuple[str | None, str | None]] | None = None,
    ):
        """
        Record CheckBatchCost polling metrics.

        Args:
            jobs_polled: Number of unprocessed batches found
            processed_models: List of (model, api_provider) tuples for processed jobs
        """
        import time

        try:
            self.litellm_check_batch_cost_last_run_timestamp.set(time.time())
            self.litellm_check_batch_cost_jobs_polled.set(jobs_polled)

            if processed_models:
                for model, api_provider in processed_models:
                    self.litellm_check_batch_cost_jobs_processed_total.labels(
                        model=model or "",
                        api_provider=api_provider or "",
                    ).inc()
        except Exception as e:
            verbose_logger.warning("Error recording check batch cost metrics: %s", e)

    def record_check_batch_cost_error(self, error_type: str):
        try:
            self.litellm_check_batch_cost_errors_total.labels(
                error_type=error_type,
            ).inc()
        except Exception as e:
            verbose_logger.warning("Error recording check batch cost error metric: %s", e)

    @staticmethod
    def _get_exception_class_name(exception: Exception) -> str:
        # Some exception types pin the ``exception_class`` label to a legacy
        # value for back-compat with existing dashboards (e.g. proxy-side 429s
        # keep reporting as "HTTPException"). Honor that opt-in marker before
        # deriving the label from the runtime class name. Reading it via
        # ``getattr`` keeps this core integrations module free of a transitive
        # ``fastapi`` dependency.
        legacy_class_name: Final = getattr(exception, "prometheus_exception_class_name", None)
        if isinstance(legacy_class_name, str) and legacy_class_name:
            return legacy_class_name

        # Same back-compat reasoning for ``BudgetExceededError``: the unified
        # rate-limit error work attached ``.llm_provider`` to budget errors
        # too (so callbacks reading ``StandardLoggingPayload`` get provider
        # attribution). Without this short-circuit, the provider prefix below
        # would silently flip the label from "BudgetExceededError" to e.g.
        # "Openai.BudgetExceededError" and break dashboards keyed on the
        # original value.
        try:
            from litellm.exceptions import BudgetExceededError
        except ImportError:
            BudgetExceededError = None

        if BudgetExceededError is not None and isinstance(exception, BudgetExceededError):
            return "BudgetExceededError"

        exception_class_name = ""
        if hasattr(exception, "llm_provider"):
            exception_class_name = getattr(exception, "llm_provider") or ""

        # pretty print the provider name on prometheus
        # eg. `openai` -> `Openai.`
        if len(exception_class_name) >= 1:
            exception_class_name = exception_class_name[0].upper() + exception_class_name[1:] + "."

        exception_class_name += exception.__class__.__name__
        return exception_class_name

    @staticmethod
    def _extract_rate_limit_labels(
        exception: Exception | None,
    ) -> tuple[str | None, str | None]:
        """
        Pull the unified ``category`` / ``rate_limit_type`` fields off any
        exception that declares them (``litellm.RateLimitError`` and bare-
        Exception subclasses like ``BudgetExceededError``).

        Values are validated against the :class:`RateLimitErrorCategory` /
        :class:`RateLimitType` enums so unrelated third-party exceptions that
        happen to declare ``.category`` / ``.rate_limit_type`` string attributes
        can't leak garbage into Prometheus label cardinality.
        """
        if exception is None:
            return None, None
        return (
            validate_rate_limit_category(getattr(exception, "category", None)),
            validate_rate_limit_type(getattr(exception, "rate_limit_type", None)),
        )

    async def log_success_fallback_event(self, original_model_group: str, kwargs: dict, original_exception: Exception):
        """

        Logs a successful LLM fallback event on prometheus

        """
        from litellm.litellm_core_utils.litellm_logging import (
            StandardLoggingMetadata,
            StandardLoggingPayloadSetup,
        )

        verbose_logger.debug(
            "Prometheus: log_success_fallback_event, original_model_group: %s, kwargs: %s",
            original_model_group,
            kwargs,
        )
        _metadata_key: Final = get_metadata_variable_name_from_kwargs(kwargs)
        _metadata: Final = kwargs.get(_metadata_key) or {}
        standard_metadata: Final[StandardLoggingMetadata] = StandardLoggingPayloadSetup.get_standard_logging_metadata(
            metadata=_metadata
        )
        _new_model: Final = kwargs.get("model")
        _tags: Final = cast(list[str], kwargs.get("tags") or [])

        enum_values: Final = UserAPIKeyLabelValues(
            requested_model=_bounded_requested_model_label(original_model_group, router_originated=True),
            fallback_model=_new_model,
            hashed_api_key=standard_metadata["user_api_key_hash"],
            api_key_alias=standard_metadata["user_api_key_alias"],
            team=standard_metadata["user_api_key_team_id"],
            team_alias=standard_metadata["user_api_key_team_alias"],
            exception_status=str(getattr(original_exception, "status_code", None)),
            exception_class=self._get_exception_class_name(original_exception),
            tags=_tags,
        )
        PrometheusLogger._inc_labeled_counter(
            self,
            self.litellm_deployment_successful_fallbacks,
            "litellm_deployment_successful_fallbacks",
            enum_values,
            label_context=PrometheusLabelFactoryContext(enum_values),
        )

    async def log_failure_fallback_event(self, original_model_group: str, kwargs: dict, original_exception: Exception):
        """
        Logs a failed LLM fallback event on prometheus
        """
        from litellm.litellm_core_utils.litellm_logging import (
            StandardLoggingMetadata,
            StandardLoggingPayloadSetup,
        )

        verbose_logger.debug(
            "Prometheus: log_failure_fallback_event, original_model_group: %s, kwargs: %s",
            original_model_group,
            kwargs,
        )
        _new_model: Final = kwargs.get("model")
        _metadata_key: Final = get_metadata_variable_name_from_kwargs(kwargs)
        _metadata: Final = kwargs.get(_metadata_key) or {}
        _tags: Final = cast(list[str], kwargs.get("tags") or [])
        standard_metadata: Final[StandardLoggingMetadata] = StandardLoggingPayloadSetup.get_standard_logging_metadata(
            metadata=_metadata
        )

        enum_values: Final = UserAPIKeyLabelValues(
            requested_model=_bounded_requested_model_label(original_model_group, router_originated=True),
            fallback_model=_new_model,
            hashed_api_key=standard_metadata["user_api_key_hash"],
            api_key_alias=standard_metadata["user_api_key_alias"],
            team=standard_metadata["user_api_key_team_id"],
            team_alias=standard_metadata["user_api_key_team_alias"],
            exception_status=str(getattr(original_exception, "status_code", None)),
            exception_class=self._get_exception_class_name(original_exception),
            tags=_tags,
        )

        PrometheusLogger._inc_labeled_counter(
            self,
            self.litellm_deployment_failed_fallbacks,
            "litellm_deployment_failed_fallbacks",
            enum_values,
            label_context=PrometheusLabelFactoryContext(enum_values),
        )

    def set_litellm_deployment_state(
        self,
        state: int,
        litellm_model_name: str,
        model_id: str | None,
        api_base: str | None,
        api_provider: str,
    ):
        """
        Set the deployment state.
        """
        ### get labels
        _labels: Final = prometheus_label_factory(
            supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_deployment_state"),
            enum_values=UserAPIKeyLabelValues(
                litellm_model_name=litellm_model_name,
                model_id=model_id,
                api_base=api_base,
                api_provider=api_provider,
            ),
        )
        self.litellm_deployment_state.labels(**_labels).set(state)

    def set_deployment_healthy(
        self,
        litellm_model_name: str,
        model_id: str,
        api_base: str,
        api_provider: str,
    ):
        self.set_litellm_deployment_state(0, litellm_model_name, model_id, api_base, api_provider)

    def set_deployment_partial_outage(
        self,
        litellm_model_name: str,
        model_id: str | None,
        api_base: str | None,
        api_provider: str,
    ):
        self.set_litellm_deployment_state(1, litellm_model_name, model_id, api_base, api_provider)

    def set_deployment_complete_outage(
        self,
        litellm_model_name: str,
        model_id: str | None,
        api_base: str | None,
        api_provider: str,
    ):
        self.set_litellm_deployment_state(2, litellm_model_name, model_id, api_base, api_provider)

    def increment_deployment_cooled_down(
        self,
        litellm_model_name: str,
        model_id: str,
        api_base: str,
        api_provider: str,
        exception_status: str,
    ):
        """
        increment metric when litellm.Router / load balancing logic places a deployment in cool down
        """
        self.litellm_deployment_cooled_down.labels(
            _sanitize_prometheus_label_value(litellm_model_name),
            _sanitize_prometheus_label_value(model_id),
            _sanitize_prometheus_label_value(api_base),
            _sanitize_prometheus_label_value(api_provider),
            _sanitize_prometheus_label_value(exception_status),
        ).inc()

    def increment_callback_logging_failure(
        self,
        callback_name: str,
    ):
        """
        Increment metric when logging to a callback fails (e.g., s3_v2, langfuse, etc.)
        """
        self.litellm_callback_logging_failures_metric.labels(callback_name=callback_name).inc()

    def track_provider_remaining_budget(self, provider: str, spend: float, budget_limit: float):
        """
        Track provider remaining budget in Prometheus
        """
        self.litellm_provider_remaining_budget_metric.labels(provider).set(
            self._safe_get_remaining_budget(
                max_budget=budget_limit,
                spend=spend,
            )
        )

    def _safe_get_remaining_budget(self, max_budget: float | None, spend: float | None) -> float:
        if max_budget is None:
            return float("inf")

        if spend is None:
            return max_budget

        return max_budget - spend

    async def _initialize_budget_metrics(
        self,
        data_fetch_function: Callable[..., Awaitable[tuple[Sequence[_BudgetRowT], int | None]]],
        set_metrics_function: Callable[[Sequence[_BudgetRowT]], Awaitable[None]],
        data_type: Literal["teams", "keys", "users", "orgs", "customers"],
    ):
        """
        Generic method to initialize budget metrics for teams or API keys.

        Args:
            data_fetch_function: Function to fetch data with pagination.
            set_metrics_function: Function to set metrics for the fetched data.
            data_type: String representing the type of data ("teams" or "keys") for logging purposes.
        """
        from litellm.proxy.proxy_server import prisma_client

        if prisma_client is None:
            return

        try:
            page = 1
            page_size: Final = 50
            data, total_count = await data_fetch_function(page_size=page_size, page=page)

            if total_count is None:
                total_count = len(data)

            # Calculate total pages needed
            total_pages: Final = (total_count + page_size - 1) // page_size

            # Set metrics for first page of data
            await set_metrics_function(data)

            # Get and set metrics for remaining pages
            for page in range(2, total_pages + 1):
                data, _ = await data_fetch_function(page_size=page_size, page=page)
                await set_metrics_function(data)

        except Exception as e:
            verbose_logger.exception("Error initializing %s budget metrics: %s", data_type, e)

    async def _initialize_team_budget_metrics(self):
        """
        Initialize team budget metrics by reusing the generic pagination logic.
        """
        from litellm.proxy.management_endpoints.team_endpoints import (
            get_paginated_teams,
        )
        from litellm.proxy.proxy_server import prisma_client

        if prisma_client is None:
            verbose_logger.debug("Prometheus: skipping team metrics initialization, DB not initialized")
            return

        async def fetch_teams(page_size: int, page: int) -> tuple[list[LiteLLM_TeamTable], int | None]:
            teams, total_count = await get_paginated_teams(prisma_client=prisma_client, page_size=page_size, page=page)
            if total_count is None:
                total_count = len(teams)
            return teams, total_count

        await self._initialize_budget_metrics(
            data_fetch_function=fetch_teams,
            set_metrics_function=self._set_team_list_budget_metrics,
            data_type="teams",
        )

    async def _initialize_api_key_budget_metrics(self):
        """
        Initialize API key budget metrics by reusing the generic pagination logic.
        """
        from litellm.constants import UI_SESSION_TOKEN_TEAM_ID
        from litellm.proxy.management_endpoints.key_management_endpoints import (
            _list_key_helper,
        )
        from litellm.proxy.proxy_server import prisma_client

        if prisma_client is None:
            verbose_logger.debug("Prometheus: skipping key metrics initialization, DB not initialized")
            return

        async def fetch_keys(
            page_size: int, page: int
        ) -> tuple[
            list[str | UserAPIKeyAuth | LiteLLM_DeletedVerificationToken],
            int | None,
        ]:
            key_list_response: Final = await _list_key_helper(
                prisma_client=prisma_client,
                page=page,
                size=page_size,
                user_id=None,
                team_id=None,
                key_alias=None,
                key_hash=None,
                exclude_team_id=UI_SESSION_TOKEN_TEAM_ID,
                return_full_object=True,
                organization_id=None,
            )
            keys: Final = key_list_response.get("keys", [])
            total_count = key_list_response.get("total_count")
            if total_count is None:
                total_count = len(keys)
            return keys, total_count

        await self._initialize_budget_metrics(
            data_fetch_function=fetch_keys,
            set_metrics_function=self._set_key_list_budget_metrics,
            data_type="keys",
        )

    async def _initialize_user_budget_metrics(self):
        """
        Initialize user budget metrics by reusing the generic pagination logic.
        """
        from litellm.proxy.proxy_server import prisma_client

        if prisma_client is None:
            verbose_logger.debug("Prometheus: skipping user metrics initialization, DB not initialized")
            return

        async def fetch_users(page_size: int, page: int) -> tuple[list[LiteLLM_UserTable], int | None]:
            skip: Final = (page - 1) * page_size
            users: Final = await _paginated_table(UserRepository(prisma_client)).find_many(
                skip=skip,
                take=page_size,
                order={"created_at": "desc"},
            )
            total_count: Final = await _paginated_table(UserRepository(prisma_client)).count()
            return users, total_count

        await self._initialize_budget_metrics(
            data_fetch_function=fetch_users,
            set_metrics_function=self._set_user_list_budget_metrics,
            data_type="users",
        )

    async def _initialize_org_budget_metrics(self):
        """
        Initialize org budget metrics by reusing the generic pagination logic.
        """
        from litellm.proxy.proxy_server import prisma_client

        if prisma_client is None:
            verbose_logger.debug("Prometheus: skipping org metrics initialization, DB not initialized")
            return

        async def fetch_orgs(page_size: int, page: int) -> tuple[list, int | None]:
            skip: Final = (page - 1) * page_size
            orgs: Final = await _paginated_table(OrganizationRepository(prisma_client)).find_many(
                skip=skip,
                take=page_size,
                order={"created_at": "desc"},
                include={"litellm_budget_table": True},
            )
            total_count: Final = await _paginated_table(OrganizationRepository(prisma_client)).count()
            return orgs, total_count

        await self._initialize_budget_metrics(
            data_fetch_function=fetch_orgs,
            set_metrics_function=self._set_org_list_budget_metrics,
            data_type="orgs",
        )

    async def _initialize_customer_budget_metrics(self):
        from litellm.proxy.proxy_server import prisma_client

        if prisma_client is None:
            verbose_logger.debug("Prometheus: skipping customer metrics initialization, DB not initialized")
            return

        if self._customer_budget_gauges_are_noop():
            return

        if not _customer_budget_metrics_enabled():
            verbose_logger.debug("Prometheus: skipping customer metrics initialization, end_user tracking disabled")
            return

        default_budget: Final = await self._get_default_customer_budget(prisma_client)
        customers_table: Final = EndUserRepository(prisma_client).table
        with_persisted_budget: Final[_BudgetedCustomerFilter] = {"budget_id": {"not": None}}
        budgeted_customers: Final = None if default_budget is not None else with_persisted_budget
        by_user_id: Final[LiteLLM_EndUserTableOrderByInput] = {"user_id": "asc"}
        with_budget: Final[LiteLLM_EndUserTableInclude] = {"litellm_budget_table": True}

        async def fetch_customers(page_size: int, page: int) -> tuple[Sequence[_CustomerBudgetRow], int | None]:
            skip: Final = (page - 1) * page_size
            customers: Final = await customers_table.find_many(
                skip=skip,
                take=page_size,
                where=budgeted_customers,
                order=by_user_id,
                include=with_budget,
            )
            total_count: Final = await customers_table.count(where=budgeted_customers) if page == 1 else None
            return customers, total_count

        async def set_customer_metrics(customers: Sequence[_CustomerBudgetRow]) -> None:
            for customer in customers:
                self._set_customer_budget_metrics_from_row(customer, default_budget=default_budget)

        await self._initialize_budget_metrics(
            data_fetch_function=fetch_customers,
            set_metrics_function=set_customer_metrics,
            data_type="customers",
        )

    async def initialize_remaining_budget_metrics(self):
        """
        Handler for initializing remaining budget metrics for all teams to avoid metric discrepancies.

        Runs when prometheus logger starts up.

        - If redis cache is available, we use the pod lock manager to acquire a lock and initialize the metrics.
            - Ensures only one pod emits the metrics at a time.
        - If redis cache is not available, we initialize the metrics directly.
        """
        from litellm.constants import PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME
        from litellm.proxy.proxy_server import proxy_logging_obj

        pod_lock_manager: Final = proxy_logging_obj.db_spend_update_writer.pod_lock_manager

        # if using redis, ensure only one pod emits the metrics at a time
        if pod_lock_manager and pod_lock_manager.redis_cache:
            if await pod_lock_manager.acquire_lock(cronjob_id=PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME):
                try:
                    await self._initialize_remaining_budget_metrics()
                finally:
                    await pod_lock_manager.release_lock(cronjob_id=PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME)
        else:
            # if not using redis, initialize the metrics directly
            await self._initialize_remaining_budget_metrics()

    async def _initialize_remaining_budget_metrics(self):
        """
        Helper to initialize remaining budget metrics for all teams, API keys, and users.
        """
        verbose_logger.debug("Emitting key, team, user, org, customer budget metrics....")
        await self._initialize_team_budget_metrics()
        await self._initialize_api_key_budget_metrics()
        await self._initialize_user_budget_metrics()
        await self._initialize_org_budget_metrics()
        await self._initialize_customer_budget_metrics()
        await self._initialize_user_and_team_count_metrics()

    async def _initialize_user_and_team_count_metrics(self):
        """
        Initialize user and team count metrics by querying the database.

        Updates:
        - litellm_total_users: Total count of users in the database
        - litellm_active_users: Count of billable users (excludes SCIM-deactivated)
        - litellm_teams_count: Total count of teams in the database
        """
        from litellm.proxy.proxy_server import prisma_client

        if prisma_client is None:
            verbose_logger.debug("Prometheus: skipping user/team count metrics initialization, DB not initialized")
            return

        try:
            # Get total user count
            total_users: Final = await _paginated_table(UserRepository(prisma_client)).count()
            self.litellm_total_users_metric.set(total_users)
            verbose_logger.debug("Prometheus: set litellm_total_users to %s", total_users)

            billable_users: Final = await UserRepository(prisma_client).count_billable_users()
            self.litellm_active_users_metric.set(billable_users)
            verbose_logger.debug("Prometheus: set litellm_active_users to %s", billable_users)

            # Get total team count
            total_teams: Final = await _paginated_table(TeamRepository(prisma_client)).count()
            self.litellm_teams_count_metric.set(total_teams)
            verbose_logger.debug("Prometheus: set litellm_teams_count to %s", total_teams)
        except Exception as e:
            verbose_logger.exception("Error initializing user/team count metrics: %s", e)

    async def _set_key_list_budget_metrics(
        self, keys: Sequence[str | UserAPIKeyAuth | LiteLLM_DeletedVerificationToken]
    ) -> None:
        """Helper function to set budget metrics for a list of keys"""
        for key in keys:
            if isinstance(key, UserAPIKeyAuth):
                self._set_key_budget_metrics(key)

    async def _set_team_list_budget_metrics(self, teams: Sequence[LiteLLM_TeamTable]):
        """Helper function to set budget metrics for a list of teams"""
        for team in teams:
            self._set_team_budget_metrics(team)

    async def _set_user_list_budget_metrics(self, users: Sequence[LiteLLM_UserTable]):
        """Helper function to set budget metrics for a list of users"""
        for user in users:
            self._set_user_budget_metrics(user)

    async def _set_org_list_budget_metrics(self, orgs: Sequence):
        """Helper function to set budget metrics for a list of orgs"""
        for org in orgs:
            budget_table: _JoinedBudgetRow | None = getattr(org, "litellm_budget_table", None)
            self._set_org_budget_metrics(
                org_id=org.organization_id or "",
                org_alias=org.organization_alias or "",
                spend=org.spend or 0.0,
                max_budget=budget_table.max_budget if budget_table else None,
                budget_reset_at=(getattr(budget_table, "budget_reset_at", None) if budget_table else None),
            )

    def _set_customer_budget_metrics_from_row(
        self, customer: _CustomerBudgetRow, default_budget: _JoinedBudgetRow | None
    ):
        budget_table: Final = (
            customer.litellm_budget_table if customer.litellm_budget_table is not None else default_budget
        )
        self._set_customer_budget_metrics(
            end_user_id=customer.user_id,
            spend=customer.spend,
            max_budget=budget_table.max_budget if budget_table is not None else None,
            budget_reset_at=budget_table.budget_reset_at if budget_table is not None else None,
        )

    async def _set_team_budget_metrics_after_api_request(
        self,
        user_api_team: str | None,
        user_api_team_alias: str | None,
        team_spend: float | None,
        team_max_budget: float | None,
        response_cost: float,
        carried: TeamBudgetSnapshot | None = None,
    ):
        """
        Set team budget metrics after an LLM API request

        - Assemble a LiteLLM_TeamTable object
            - looks up team info from db if not available in metadata
        - Set team budget metrics
        """
        if isinstance(self.litellm_remaining_team_budget_metric, NoOpMetric):
            return

        if user_api_team:
            team_object: Final = await self._assemble_team_object(
                team_id=user_api_team,
                team_alias=user_api_team_alias or "",
                spend=team_spend,
                max_budget=team_max_budget,
                response_cost=response_cost,
                carried=carried,
            )

            self._set_team_budget_metrics(team_object)

    async def _assemble_team_object(
        self,
        team_id: str,
        team_alias: str,
        spend: float | None,
        max_budget: float | None,
        response_cost: float,
        carried: TeamBudgetSnapshot | None = None,
    ) -> LiteLLM_TeamTable:
        """
        Assemble a LiteLLM_TeamTable object

        ``budget_reset_at`` comes from the auth-carried snapshot when the request has one,
        otherwise from the team lookup
        """
        from litellm.proxy.auth.auth_checks import get_team_object
        from litellm.proxy.proxy_server import prisma_client, user_api_key_cache

        _total_team_spend: Final = (spend or 0) + response_cost
        if carried is not None:
            return LiteLLM_TeamTable(
                team_id=team_id,
                team_alias=team_alias,
                spend=_total_team_spend,
                max_budget=max_budget if max_budget is not None else carried.max_budget,
                budget_reset_at=carried.budget_reset_at,
            )
        team_object: Final = LiteLLM_TeamTable(
            team_id=team_id,
            team_alias=team_alias,
            spend=_total_team_spend,
            max_budget=max_budget,
        )
        try:
            team_info: Final = await get_team_object(
                team_id=team_id,
                prisma_client=prisma_client,
                user_api_key_cache=user_api_key_cache,
            )
        except Exception as e:
            verbose_logger.debug("[Non-Blocking] Prometheus: Error getting team info: %s", e)
            return team_object

        if team_info:
            team_object.budget_reset_at = team_info.budget_reset_at
            if team_object.max_budget is None and team_info.max_budget is not None:
                team_object.max_budget = team_info.max_budget

        return team_object

    def _set_team_budget_metrics(
        self,
        team: LiteLLM_TeamTable,
    ):
        """
        Set team budget metrics for a single team

        - Remaining Budget
        - Max Budget
        - Budget Reset At
        """
        enum_values: Final = UserAPIKeyLabelValues(
            team=team.team_id,
            team_alias=team.team_alias or "",
        )

        _labels = prometheus_label_factory(
            supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_remaining_team_budget_metric"),
            enum_values=enum_values,
        )
        self.litellm_remaining_team_budget_metric.labels(**_labels).set(
            self._safe_get_remaining_budget(
                max_budget=team.max_budget,
                spend=team.spend,
            )
        )

        if team.max_budget is not None:
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_team_max_budget_metric"),
                enum_values=enum_values,
            )
            self.litellm_team_max_budget_metric.labels(**_labels).set(team.max_budget)

        if team.budget_reset_at is not None:
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(
                    metric_name="litellm_team_budget_remaining_hours_metric"
                ),
                enum_values=enum_values,
            )
            self.litellm_team_budget_remaining_hours_metric.labels(**_labels).set(
                self._get_remaining_hours_for_budget_reset(budget_reset_at=team.budget_reset_at)
            )

    def set_team_members_metric(self, team: LiteLLM_TeamTable) -> None:
        """Set the team members gauge to the team's current member count."""
        enum_values: Final = UserAPIKeyLabelValues(
            team=team.team_id,
            team_alias=team.team_alias or "",
        )
        _labels: Final = prometheus_label_factory(
            supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_team_members_metric"),
            enum_values=enum_values,
        )
        self.litellm_team_members_metric.labels(**_labels).set(len(team.members_with_roles))

    async def _set_org_budget_metrics_after_api_request(
        self,
        org_id: str | None,
        response_cost: float,
        carried: OrgBudgetSnapshot | None = None,
        org_alias: str | None = None,
    ):
        """
        Set org budget metrics after an LLM API request

        - Uses the auth-carried org budget when the request has one, else fetches via get_org_object
        - Sets org budget metrics
        """
        if isinstance(self.litellm_remaining_org_budget_metric, NoOpMetric):
            return

        if not org_id:
            return

        if carried is not None:
            self._set_org_budget_metrics(
                org_id=org_id,
                org_alias=org_alias or "",
                spend=carried.spend + response_cost,
                max_budget=carried.max_budget,
                budget_reset_at=None,
            )
            return

        from litellm.proxy.auth.auth_checks import get_org_object
        from litellm.proxy.proxy_server import prisma_client, user_api_key_cache

        if prisma_client is None:
            return

        try:
            org_info: Final = await get_org_object(
                org_id=org_id,
                prisma_client=prisma_client,
                user_api_key_cache=user_api_key_cache,
                include_budget_table=True,
            )
        except Exception as e:
            verbose_logger.debug("[Non-Blocking] Prometheus: Error getting org info: %s", e)
            return

        if org_info is None:
            return

        _total_org_spend: Final = (org_info.spend or 0.0) + response_cost
        budget_table: Final = org_info.litellm_budget_table
        max_budget: Final = budget_table.max_budget if budget_table else None
        budget_reset_at: Final = getattr(budget_table, "budget_reset_at", None) if budget_table else None

        self._set_org_budget_metrics(
            org_id=org_id,
            org_alias=org_info.organization_alias or "",
            spend=_total_org_spend,
            max_budget=max_budget,
            budget_reset_at=budget_reset_at,
        )

    def _set_org_budget_metrics(
        self,
        org_id: str,
        org_alias: str,
        spend: float,
        max_budget: float | None,
        budget_reset_at: datetime | None,
    ):
        """
        Set org budget metrics for a single org

        - Remaining Budget
        - Max Budget
        - Budget Reset At
        """
        enum_values: Final = UserAPIKeyLabelValues(
            org_id=org_id,
            org_alias=org_alias,
        )

        _labels = prometheus_label_factory(
            supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_remaining_org_budget_metric"),
            enum_values=enum_values,
        )
        self.litellm_remaining_org_budget_metric.labels(**_labels).set(
            self._safe_get_remaining_budget(
                max_budget=max_budget,
                spend=spend,
            )
        )

        if max_budget is not None:
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_org_max_budget_metric"),
                enum_values=enum_values,
            )
            self.litellm_org_max_budget_metric.labels(**_labels).set(max_budget)

        if budget_reset_at is not None:
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(
                    metric_name="litellm_org_budget_remaining_hours_metric"
                ),
                enum_values=enum_values,
            )
            self.litellm_org_budget_remaining_hours_metric.labels(**_labels).set(
                self._get_remaining_hours_for_budget_reset(budget_reset_at=budget_reset_at)
            )

    async def _set_customer_budget_metrics_after_api_request(
        self,
        end_user_id: str | None,
        response_cost: float,
    ):
        if self._customer_budget_gauges_are_noop() or not _customer_budget_metrics_enabled():
            return

        if not end_user_id:
            return

        from litellm.proxy.common_utils.user_api_key_cache import end_user_cache_key
        from litellm.proxy.proxy_server import user_api_key_cache

        try:
            cached_customer: Final = await user_api_key_cache.async_get_cache(
                key=end_user_cache_key(end_user_id),
                model_type=LiteLLM_EndUserTable,
            )
        except Exception as e:
            verbose_logger.debug("[Non-Blocking] Prometheus: Error getting customer info: %s", e)
            return

        if cached_customer is None:
            return

        budget_table: Final = cached_customer.litellm_budget_table
        self._set_customer_budget_metrics(
            end_user_id=end_user_id,
            spend=cached_customer.spend + response_cost,
            max_budget=budget_table.max_budget if budget_table is not None else None,
            budget_reset_at=None,
        )

    async def _get_default_customer_budget(self, prisma_client: PrismaClient) -> _JoinedBudgetRow | None:
        default_budget_id: Final = litellm.max_end_user_budget_id
        if default_budget_id is None:
            return None
        default_budget_key: Final[LiteLLM_BudgetTableWhereUniqueInput] = {"budget_id": default_budget_id}
        try:
            return await BudgetRepository(prisma_client).table.find_unique(where=default_budget_key)
        except Exception as e:
            verbose_logger.debug("[Non-Blocking] Prometheus: Error getting default customer budget: %s", e)
            return None

    def _customer_budget_gauges_are_noop(self) -> bool:
        return (
            isinstance(self.litellm_remaining_customer_budget_metric, NoOpMetric)
            and isinstance(self.litellm_customer_max_budget_metric, NoOpMetric)
            and isinstance(self.litellm_customer_budget_remaining_hours_metric, NoOpMetric)
        )

    def _set_customer_budget_metrics(
        self,
        end_user_id: str,
        spend: float,
        max_budget: float | None,
        budget_reset_at: datetime | None,
    ):
        _labels: Final[dict[str, str | None]] = prometheus_label_factory(
            supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_remaining_customer_budget_metric"),
            enum_values=UserAPIKeyLabelValues(end_user=end_user_id),
        )
        if _labels.get(UserAPIKeyLabelNames.END_USER.value) is None:
            return

        self.litellm_remaining_customer_budget_metric.labels(**_labels).set(
            self._safe_get_remaining_budget(
                max_budget=max_budget,
                spend=spend,
            )
        )
        self._track_end_user_metric_series(
            self.litellm_remaining_customer_budget_metric, "litellm_remaining_customer_budget_metric", _labels
        )

        if max_budget is not None:
            self.litellm_customer_max_budget_metric.labels(**_labels).set(max_budget)
            self._track_end_user_metric_series(
                self.litellm_customer_max_budget_metric, "litellm_customer_max_budget_metric", _labels
            )

        if budget_reset_at is not None:
            self.litellm_customer_budget_remaining_hours_metric.labels(**_labels).set(
                self._get_remaining_hours_for_budget_reset(budget_reset_at=budget_reset_at)
            )
            self._track_end_user_metric_series(
                self.litellm_customer_budget_remaining_hours_metric,
                "litellm_customer_budget_remaining_hours_metric",
                _labels,
            )

    def _set_key_budget_metrics(self, user_api_key_dict: UserAPIKeyAuth):
        """
        Set virtual key budget metrics

        - Remaining Budget
        - Max Budget
        - Budget Reset At
        """
        enum_values: Final = UserAPIKeyLabelValues(
            hashed_api_key=user_api_key_dict.token,
            api_key_alias=user_api_key_dict.key_alias or "",
        )
        _labels = prometheus_label_factory(
            supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_remaining_api_key_budget_metric"),
            enum_values=enum_values,
        )
        self.litellm_remaining_api_key_budget_metric.labels(**_labels).set(
            self._safe_get_remaining_budget(
                max_budget=user_api_key_dict.max_budget,
                spend=user_api_key_dict.spend,
            )
        )

        if user_api_key_dict.max_budget is not None:
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_api_key_max_budget_metric"),
                enum_values=enum_values,
            )
            self.litellm_api_key_max_budget_metric.labels(**_labels).set(user_api_key_dict.max_budget)

        if user_api_key_dict.budget_reset_at is not None:
            self.litellm_api_key_budget_remaining_hours_metric.labels(**_labels).set(
                self._get_remaining_hours_for_budget_reset(budget_reset_at=user_api_key_dict.budget_reset_at)
            )

    async def _set_api_key_budget_metrics_after_api_request(
        self,
        user_api_key: str | None,
        user_api_key_alias: str | None,
        response_cost: float,
        key_max_budget: float | None,
        key_spend: float | None,
        carried: KeyBudgetSnapshot | None = None,
    ):
        if isinstance(self.litellm_remaining_api_key_budget_metric, NoOpMetric):
            return

        if user_api_key:
            user_api_key_dict: Final = await self._assemble_key_object(
                user_api_key=user_api_key,
                user_api_key_alias=user_api_key_alias or "",
                key_max_budget=key_max_budget,
                key_spend=key_spend,
                response_cost=response_cost,
                carried=carried,
            )
            self._set_key_budget_metrics(user_api_key_dict)

    async def _assemble_key_object(
        self,
        user_api_key: str,
        user_api_key_alias: str,
        key_max_budget: float | None,
        key_spend: float | None,
        response_cost: float,
        carried: KeyBudgetSnapshot | None = None,
    ) -> UserAPIKeyAuth:
        """
        Assemble a UserAPIKeyAuth object
        """
        from litellm.proxy.auth.auth_checks import get_key_object
        from litellm.proxy.proxy_server import prisma_client, user_api_key_cache

        _total_key_spend: Final = (key_spend or 0) + response_cost
        if carried is not None:
            return UserAPIKeyAuth(
                token=user_api_key,
                key_alias=user_api_key_alias,
                max_budget=key_max_budget,
                spend=_total_key_spend,
                budget_reset_at=carried.budget_reset_at,
            )
        user_api_key_dict: Final = UserAPIKeyAuth(
            token=user_api_key,
            key_alias=user_api_key_alias,
            max_budget=key_max_budget,
            spend=_total_key_spend,
        )
        try:
            if user_api_key_dict.token:
                key_object: Final = await get_key_object(
                    hashed_token=user_api_key_dict.token,
                    prisma_client=prisma_client,
                    user_api_key_cache=user_api_key_cache,
                    check_cache_only=True,
                )
                if key_object:
                    user_api_key_dict.budget_reset_at = key_object.budget_reset_at
        except Exception as e:
            verbose_logger.debug("[Non-Blocking] Prometheus: Error getting key info: %s", e)

        return user_api_key_dict

    async def _set_user_budget_metrics_after_api_request(
        self,
        user_id: str | None,
        user_spend: float | None,
        user_max_budget: float | None,
        response_cost: float,
        carried: UserBudgetSnapshot | None = None,
        user_email: str | None = None,
    ):
        """
        Set user budget metrics after an LLM API request

        - Assemble a LiteLLM_UserTable object
            - looks up user info from db if not available in metadata
        - Set user budget metrics
        """
        if isinstance(self.litellm_remaining_user_budget_metric, NoOpMetric):
            return

        if user_id:
            user_object: Final = await self._assemble_user_object(
                user_id=user_id,
                spend=user_spend,
                max_budget=user_max_budget,
                response_cost=response_cost,
                carried=carried,
                user_email=user_email,
            )

            self._set_user_budget_metrics(user_object)

    async def _assemble_user_object(
        self,
        user_id: str,
        spend: float | None,
        max_budget: float | None,
        response_cost: float,
        carried: UserBudgetSnapshot | None = None,
        user_email: str | None = None,
    ) -> LiteLLM_UserTable:
        """
        Assemble a LiteLLM_UserTable object

        ``budget_reset_at`` and ``user_alias`` come from the auth-carried snapshot when the
        request has one, otherwise from the user lookup
        """
        from litellm.proxy.auth.auth_checks import get_user_object
        from litellm.proxy.proxy_server import prisma_client, user_api_key_cache

        _total_user_spend: Final = (spend or 0) + response_cost
        if carried is not None:
            return LiteLLM_UserTable(
                user_id=user_id,
                spend=_total_user_spend,
                max_budget=max_budget if max_budget is not None else carried.max_budget,
                budget_reset_at=carried.budget_reset_at,
                user_alias=carried.user_alias,
                user_email=user_email,
            )
        user_object: Final = LiteLLM_UserTable(
            user_id=user_id,
            spend=_total_user_spend,
            max_budget=max_budget,
        )
        try:
            # Note: Setting check_db_only=True bypasses cache and hits DB on every request,
            # causing huge latency increase and CPU spikes. Keep check_db_only=False.
            user_info: Final = await get_user_object(
                user_id=user_id,
                prisma_client=prisma_client,
                user_api_key_cache=user_api_key_cache,
                user_id_upsert=False,
                check_db_only=False,
            )
        except Exception as e:
            verbose_logger.debug("[Non-Blocking] Prometheus: Error getting user info: %s", e)
            return user_object

        if user_info:
            user_object.budget_reset_at = user_info.budget_reset_at
            if user_object.max_budget is None and user_info.max_budget is not None:
                user_object.max_budget = user_info.max_budget
            if user_info.user_email is not None:
                user_object.user_email = user_info.user_email
            if user_info.user_alias is not None:
                user_object.user_alias = user_info.user_alias

        return user_object

    def _set_user_budget_metrics(
        self,
        user: LiteLLM_UserTable,
    ):
        """
        Set user budget metrics for a single user

        - Remaining Budget
        - Max Budget
        - Budget Reset At
        """
        enum_values: Final = UserAPIKeyLabelValues(
            user=user.user_id,
            user_email=user.user_email or "",
            user_alias=user.user_alias or "",
        )

        _labels = prometheus_label_factory(
            supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_remaining_user_budget_metric"),
            enum_values=enum_values,
        )
        self.litellm_remaining_user_budget_metric.labels(**_labels).set(
            self._safe_get_remaining_budget(
                max_budget=user.max_budget,
                spend=user.spend,
            )
        )

        if user.max_budget is not None:
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_user_max_budget_metric"),
                enum_values=enum_values,
            )
            self.litellm_user_max_budget_metric.labels(**_labels).set(user.max_budget)

        if user.budget_reset_at is not None:
            _labels = prometheus_label_factory(
                supported_enum_labels=self.get_labels_for_metric(
                    metric_name="litellm_user_budget_remaining_hours_metric"
                ),
                enum_values=enum_values,
            )
            self.litellm_user_budget_remaining_hours_metric.labels(**_labels).set(
                self._get_remaining_hours_for_budget_reset(budget_reset_at=user.budget_reset_at)
            )

    def _get_remaining_hours_for_budget_reset(self, budget_reset_at: datetime) -> float:
        """
        Get remaining hours for budget reset
        """
        return (budget_reset_at - datetime.now(budget_reset_at.tzinfo)).total_seconds() / 3600

    def _safe_duration_seconds(
        self,
        start_time: object,
        end_time: object,
    ) -> float | None:
        """
        Compute the duration in seconds between two objects.

        Returns the duration as a float if both start and end are instances of datetime,
        otherwise returns None.
        """
        if isinstance(start_time, datetime) and isinstance(end_time, datetime):
            return (end_time - start_time).total_seconds()
        return None

    @staticmethod
    def initialize_budget_metrics_cron_job(scheduler: AsyncIOScheduler):
        """
        Initialize budget metrics as a cron job. This job runs every `PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES` minutes.

        It emits the current remaining budget metrics for all Keys and Teams.
        """
        from litellm.constants import PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES

        prometheus_loggers: Final[list[CustomLogger]] = litellm.logging_callback_manager.get_custom_loggers_for_type(
            callback_type=PrometheusLogger
        )
        # we need to get the initialized prometheus logger instance(s) and call logger.initialize_remaining_budget_metrics() on them
        verbose_logger.debug("found %s prometheus loggers", len(prometheus_loggers))
        if len(prometheus_loggers) > 0:
            prometheus_logger: Final = cast(PrometheusLogger, prometheus_loggers[0])
            verbose_logger.debug(
                "Initializing remaining budget metrics as a cron job executing every %s minutes"
                % PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES
            )
            scheduler.add_job(
                prometheus_logger.initialize_remaining_budget_metrics,
                "interval",
                minutes=PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES,
                # REMOVED jitter parameter - major cause of memory leak
                id="prometheus_budget_metrics_job",
                replace_existing=True,
            )

    @staticmethod
    def _mount_metrics_endpoint():
        """
        Mount the Prometheus metrics endpoint with optional authentication.

        Args:
            require_auth (bool, optional): Whether to require authentication for the metrics endpoint.
                                        Defaults to False.
        """
        from prometheus_client import REGISTRY

        from litellm._logging import verbose_proxy_logger
        from litellm.integrations.prometheus_metrics_endpoint import make_metrics_asgi_app
        from litellm.proxy.proxy_server import app

        # Create metrics ASGI app
        if "PROMETHEUS_MULTIPROC_DIR" in os.environ:
            from prometheus_client import CollectorRegistry, multiprocess

            registry: Final = CollectorRegistry()
            multiprocess.MultiProcessCollector(registry)
            metrics_app = make_metrics_asgi_app(registry)
        else:
            metrics_app = make_metrics_asgi_app(REGISTRY)

        # Mount the metrics app to the app
        app.mount("/metrics", metrics_app)
        verbose_proxy_logger.debug("Starting Prometheus Metrics on /metrics (no authentication)")


def _label_source(enum_values: UserAPIKeyLabelValues) -> Mapping[str, object]:
    """Flatten the label values into the opaque name/value mapping the label filters read."""
    return enum_values.model_dump()


def _prometheus_labels_from_context(
    supported_enum_labels: list[str],
    ctx: PrometheusLabelFactoryContext,
) -> dict[str, str | None]:
    filtered_labels: Final[dict[str, str | None]] = {
        label: ctx._sanitized_enum[label] for label in supported_enum_labels if label in ctx._sanitized_enum
    }

    if UserAPIKeyLabelNames.END_USER.value in filtered_labels:
        filtered_labels[UserAPIKeyLabelNames.END_USER.value] = ctx.get_resolved_end_user()

    for sk, val in ctx._custom_by_sanitized_key.items():
        if sk in supported_enum_labels:
            filtered_labels[sk] = val

    for k, v in ctx._tag_labels.items():
        if k in supported_enum_labels:
            filtered_labels[k] = v

    for label in supported_enum_labels:
        if label not in filtered_labels:
            filtered_labels[label] = None

    return filtered_labels


def prometheus_label_factory(
    supported_enum_labels: list[str],
    enum_values: UserAPIKeyLabelValues,
    tag: str | None = None,
    *,
    label_context: PrometheusLabelFactoryContext | None = None,
) -> dict:
    """
    Returns a dictionary of label + values for prometheus.

    Ensures end_user param is not sent to prometheus if it is not supported.

    When ``label_context`` is provided, it must have been built from the same
    ``enum_values`` object; work is amortized (single model_dump, tag map, etc.).
    """
    if label_context is not None:
        if label_context.enum_values is not enum_values:
            raise ValueError("label_context.enum_values must be the same object as enum_values")
        return _prometheus_labels_from_context(supported_enum_labels, label_context)

    # Extract dictionary from Pydantic object
    enum_dict: Final = _label_source(enum_values)

    # Filter supported labels and sanitize values to prevent breaking
    # the Prometheus text format (e.g. U+2028 Line Separator in label values)
    filtered_labels: Final = {
        label: _sanitize_prometheus_label_value(value)
        for label, value in enum_dict.items()
        if label in supported_enum_labels
    }

    if UserAPIKeyLabelNames.END_USER.value in filtered_labels:
        get_end_user_id_for_cost_tracking: Final = _get_cached_end_user_id_for_cost_tracking()

        filtered_labels["end_user"] = get_end_user_id_for_cost_tracking(
            litellm_params={"user_api_key_end_user_id": enum_values.end_user},
            service_type="prometheus",
        )

    if enum_values.custom_metadata_labels is not None:
        for key, value in enum_values.custom_metadata_labels.items():
            # check sanitized key
            sanitized_key = _sanitize_prometheus_label_name(key)
            if sanitized_key in supported_enum_labels:
                filtered_labels[sanitized_key] = _sanitize_prometheus_label_value(value)

    # Add custom tags if configured
    if enum_values.tags is not None:
        custom_tag_labels: Final = get_custom_labels_from_tags(enum_values.tags)
        for key, value in custom_tag_labels.items():
            if key in supported_enum_labels:
                filtered_labels[key] = _sanitize_prometheus_label_value(value)

    for label in supported_enum_labels:
        if label not in filtered_labels:
            filtered_labels[label] = None

    return filtered_labels


def get_custom_labels_from_metadata(metadata: dict) -> dict[str, str]:
    """
    Get custom labels from metadata
    """
    keys: Final = litellm.custom_prometheus_metadata_labels
    if keys is None or len(keys) == 0:
        return {}

    result: Final[dict[str, str]] = {}

    for key in keys:
        # Split the dot notation key into parts
        original_key = key
        key = key.replace("metadata.", "", 1) if key.startswith("metadata.") else key

        keys_parts = key.split(".")
        # Traverse through the dictionary using the parts
        value: object = metadata
        for part in keys_parts:
            if isinstance(value, dict):
                value = value.get(part, None)  # Get the value, return None if not found
            else:
                value = None
            if value is None:
                break

        if value is not None and isinstance(value, str):
            result[original_key.replace(".", "_")] = value

    return result


def _get_combined_custom_metadata_from_standard_logging_payload(
    standard_logging_payload: dict | None,
) -> dict[str, object]:
    """
    Combine the metadata sources that can supply custom Prometheus labels.

    Includes top-level scalar fields from the standard logging metadata (e.g.
    user_api_key_project_alias, user_api_key_team_alias) so they are accessible
    via custom_prometheus_metadata_labels configuration.
    """
    if not isinstance(standard_logging_payload, dict):
        return {}

    standard_logging_metadata: Final = standard_logging_payload.get("metadata") or {}
    if not isinstance(standard_logging_metadata, dict):
        return {}

    requester_metadata: Final = standard_logging_metadata.get("requester_metadata")
    user_api_key_auth_metadata: Final = standard_logging_metadata.get("user_api_key_auth_metadata")
    spend_logs_metadata: Final = standard_logging_metadata.get("spend_logs_metadata")

    return {
        **{k: v for k, v in standard_logging_metadata.items() if not isinstance(v, dict)},
        **(requester_metadata if isinstance(requester_metadata, dict) else {}),
        **(user_api_key_auth_metadata if isinstance(user_api_key_auth_metadata, dict) else {}),
        **(spend_logs_metadata if isinstance(spend_logs_metadata, dict) else {}),
    }


def _tag_matches_wildcard_configured_pattern(tags: Sequence[str], configured_tag: str) -> bool:
    """
    Check if any of the request tags matches a wildcard configured pattern

    Args:
        tags: List[str] - The request tags
        configured_tag: str - The configured tag

    Returns:
        bool - True if any of the request tags matches the configured tag, False otherwise

    e.g.
    tags = ["User-Agent: curl/7.68.0", "User-Agent: python-requests/2.28.1", "prod"]
    configured_tag = "User-Agent: curl/*"
    _tag_matches_wildcard_configured_pattern(tags=tags, configured_tag=configured_tag) # True

    configured_tag = "User-Agent: python-requests/*"
    _tag_matches_wildcard_configured_pattern(tags=tags, configured_tag=configured_tag) # True

    configured_tag = "gm"
    _tag_matches_wildcard_configured_pattern(tags=tags, configured_tag=configured_tag) # False
    """
    import re

    from litellm.router_utils.pattern_match_deployments import PatternMatchRouter

    pattern_router: Final = PatternMatchRouter()
    regex_pattern: Final = pattern_router._pattern_to_regex(configured_tag)
    return any(re.match(pattern=regex_pattern, string=tag) for tag in tags)


def get_custom_labels_from_tags(tags: Sequence[str]) -> dict[str, str]:
    """
    Get custom labels from tags based on admin configuration.

    Supports both exact matches and wildcard patterns:
    - Exact match: "prod" matches "prod" exactly
    - Wildcard pattern: "User-Agent: curl/*" matches "User-Agent: curl/7.68.0"

    Reuses PatternMatchRouter for wildcard pattern matching.

    Returns dict of label_name: "true" if the tag matches the configured tag, "false" otherwise

    {
        "tag_User-Agent_curl": "true",
        "tag_User-Agent_python_requests": "false",
        "tag_Environment_prod": "true",
        "tag_Environment_dev": "false",
        "tag_Service_api_gateway_v2": "true",
        "tag_Service_web_app_v1": "false",
    }
    """

    from litellm.types.integrations.prometheus import _sanitize_prometheus_label_name

    configured_tags: Final = litellm.custom_prometheus_tags
    if configured_tags is None or len(configured_tags) == 0:
        return {}

    result: Final[dict[str, str]] = {}

    for configured_tag in configured_tags:
        label_name = _sanitize_prometheus_label_name(f"tag_{configured_tag}")

        # Check for exact match first (backwards compatibility)
        if configured_tag in tags:
            result[label_name] = "true"
            continue

        # Use PatternMatchRouter for wildcard pattern matching
        if "*" in configured_tag and _tag_matches_wildcard_configured_pattern(tags=tags, configured_tag=configured_tag):
            result[label_name] = "true"
            continue

        # No match found
        result[label_name] = "false"

    return result