The backfill compares the naive start_time column against a timestamptz
cutover, and that coercion follows the session time zone, so a non-UTC
session shifts the cutover boundary by the offset. Pinning the session
makes the whole script timezone-independent. The date bucketing itself was
already safe: to_char on a timestamp without time zone ignores the session
time zone and the stored values are UTC
GET /v1/tool/spend served the Cost Optimization card with two raw queries
over LiteLLM_SpendLogToolIndex x LiteLLM_SpendLogs on every dashboard load;
the totals query's driving scan was all of SpendLogs in the window. Both
per-request tables reach 1M+ rows at customer scale, so the card cost
O(traffic) per view and had to be capped at 30 days.
The index writer also mined proxy_server_request.tools, i.e. tools DECLARED
in the request body, attributing each request's full spend to tools that
never ran; and all non-MCP mining ran against payload fields that are '{}'
unless store_prompts_in_spend_logs is enabled, so non-MCP coverage silently
depended on a privacy setting.
Now the spend writer builds a ToolUsageTransaction at request time from
invoked tools only, resolved by the shared get_tool_calls_from_response
normalizer so every response surface (chat completions, Responses API,
Anthropic Messages) is covered; the tool registry's response arm delegates
to the same owner. Transactions queue beside the spend-log queue and the
flush job writes index rows plus a new LiteLLM_DailyToolSpend rollup
(date, tool_name PK) in one transaction, retrying connection errors with
backoff (a failed batch commits nothing, so the retry cannot double-count)
and dropping the batch with an error log on anything else.
The endpoint aggregates in SQL: by_tool is the top TOOL_SPEND_TOP_TOOLS
tools by spend via group_by and daily covers only those tools, so the
response is bounded by days x TOOL_SPEND_TOP_TOOLS regardless of range or
tool-name cardinality; the 30-day clamp is gone. total_spend is dropped
from the response; it was never rendered and its deduplicated semantics
are not computable from a rollup. Spend-log retention deliberately does
not touch the rollup, so tool spend history outlives per-request rows.
Drops PLR0915 from ruff's extend-select along with its per-file-ignores,
and strips the now-unused `# noqa: PLR0915` directives across the codebase
(RUF100 would otherwise flag them as unused). The C901 suppression that
shared a directive with PLR0915 in streaming_handler.py is preserved.
High-volume deployments see LiteLLM_SpendLogs grow unbounded because
retention via DELETE leaves dead tuples that autovacuum cannot reclaim
fast enough. With a range-partitioned table, retention drops whole
partitions instead: an instant metadata operation that returns disk to
the OS immediately.
The feature is gated behind general_settings.use_spend_logs_partitioning
(default false). With the flag off, the cleanup job never queries the
catalog and behaves exactly as today. With it on, the job verifies the
table is partitioned, pre-creates upcoming partitions, and drops expired
ones; expired rows the drops cannot reach (DEFAULT partition, partitions
spanning the cutoff) are still deleted row-wise so retention is never
bypassed. If the table is not partitioned it falls back to batched
DELETE only.
Converting an existing table is a manual, documented operation in
db_scripts/partition_spend_logs.sql; db_scripts/unpartition_spend_logs.sql
rolls it back. Both scripts rename the old table's indexes aside before
recreating them, since a table rename keeps the schema-unique index names
and would otherwise silently skip the CREATE INDEX IF NOT EXISTS block.
Granularity and pre-create lookahead are tunable via
SPEND_LOG_PARTITION_INTERVAL (day/week/month, invalid values fall back to
day) and SPEND_LOG_PARTITION_PRECREATE_AHEAD.
* Add O3-Mini for Azure and Remove Vision Support (#8161)
* Azure Released O3-mini at the same time as OAI, so i've added support here. Confirmed to work with Sweden Central.
* [FIX] replace cgi for python 3.13 with email.Message as suggested in PEP 594 (#8160)
* Update model_prices_and_context_window.json (#8120)
codestral2501 pricing on vertex_ai
* Fix/db view names (#8119)
* Fix to case sensitive DB Views name
* Fix to case sensitive DB View names
* Added quotes to check query as well
* Added quotes to create view query
* test: handle server error for flaky test
vertex ai has unstable endpoints
---------
Co-authored-by: Wanis Elabbar <70503629+elabbarw@users.noreply.github.com>
Co-authored-by: Honghua Dong <dhh1995@163.com>
Co-authored-by: superpoussin22 <vincent.nadal@orange.fr>
Co-authored-by: Miguel Armenta <37154380+ma-armenta@users.noreply.github.com>
* fix(model_prices_and_context_window.json): add cost tracking for more vertex llama3.1 model
8b and 70b models
* fix(proxy/utils.py): handle data being none on pre-call hooks
* fix(proxy/): create views on initial proxy startup
fixes base case, where user starts proxy for first time
Fixes https://github.com/BerriAI/litellm/issues/5756
* build(config.yml): fix vertex version for test
* feat(ui/): support enabling/disabling slack alerting
Allows admin to turn on/off slack alerting through ui
* feat(rerank/main.py): support langfuse logging
* fix(proxy/utils.py): fix linting errors
* fix(langfuse.py): log clean metadata
* test(tests): replace deprecated openai model
* fix(proxy/utils.py): comment out auto-upsert logic in check_view_exists
Prevents proxy from failing on startup due to faulty logic
* fix(db/migration_scripts/create_views.py): fix 'DailyTagSpend' quotation on check
* fix(create_views.py): mongly global spend time period should be 30d not 20d
* fix(schema.prisma): index on startTime and endUser for efficient UI querying
