diff --git a/litellm/llms/base_llm/rerank/transformation.py b/litellm/llms/base_llm/rerank/transformation.py
index 7874201f7f0..166f876ba04 100644
--- a/litellm/llms/base_llm/rerank/transformation.py
+++ b/litellm/llms/base_llm/rerank/transformation.py
@@ -33,6 +33,7 @@ class BaseRerankConfig(ABC):
         model: str,
         optional_rerank_params: Dict,
         headers: dict,
+        litellm_params: Optional[dict] = None,
     ) -> dict:
         return {}
 
diff --git a/litellm/llms/cohere/rerank/transformation.py b/litellm/llms/cohere/rerank/transformation.py
index 531b94d1805..64ae8e8ffa7 100644
--- a/litellm/llms/cohere/rerank/transformation.py
+++ b/litellm/llms/cohere/rerank/transformation.py
@@ -111,6 +111,7 @@ class CohereRerankConfig(BaseRerankConfig):
         model: str,
         optional_rerank_params: Dict,
         headers: dict,
+        litellm_params: Optional[dict] = None,
     ) -> dict:
         if "query" not in optional_rerank_params:
             raise ValueError("query is required for Cohere rerank")
diff --git a/litellm/llms/cohere/rerank_v2/transformation.py b/litellm/llms/cohere/rerank_v2/transformation.py
index 60d22ff4be0..4c800d6455d 100644
--- a/litellm/llms/cohere/rerank_v2/transformation.py
+++ b/litellm/llms/cohere/rerank_v2/transformation.py
@@ -71,6 +71,7 @@ class CohereRerankV2Config(CohereRerankConfig):
         model: str,
         optional_rerank_params: Dict,
         headers: dict,
+        litellm_params: Optional[dict] = None,
     ) -> dict:
         if "query" not in optional_rerank_params:
             raise ValueError("query is required for Cohere rerank")
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index 4c9abaad908..b8635085bcc 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -978,6 +978,7 @@ class BaseLLMHTTPHandler:
         api_key: Optional[str] = None,
         api_base: Optional[str] = None,
         client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        litellm_params: Optional[Dict[str, Any]] = None,
     ) -> RerankResponse:
         # get config from model, custom llm provider
         headers = provider_config.validate_environment(
@@ -997,6 +998,7 @@ class BaseLLMHTTPHandler:
             model=model,
             optional_rerank_params=optional_rerank_params,
             headers=headers,
+            litellm_params=litellm_params,
         )
 
         ## LOGGING
diff --git a/litellm/llms/deepinfra/rerank/transformation.py b/litellm/llms/deepinfra/rerank/transformation.py
index 71e300d258c..276735f4758 100644
--- a/litellm/llms/deepinfra/rerank/transformation.py
+++ b/litellm/llms/deepinfra/rerank/transformation.py
@@ -132,6 +132,7 @@ class DeepinfraRerankConfig(BaseRerankConfig):
         model: str,
         optional_rerank_params: Dict,
         headers: dict,
+        litellm_params: Optional[dict] = None,
     ) -> dict:
         # Convert OptionalRerankParams to dict as expected by parent class
         if optional_rerank_params is None:
diff --git a/litellm/llms/fireworks_ai/rerank/transformation.py b/litellm/llms/fireworks_ai/rerank/transformation.py
index eb92399a058..4a7b64b9b77 100644
--- a/litellm/llms/fireworks_ai/rerank/transformation.py
+++ b/litellm/llms/fireworks_ai/rerank/transformation.py
@@ -127,6 +127,7 @@ class FireworksAIRerankConfig(FireworksAIMixin, BaseRerankConfig):
         model: str,
         optional_rerank_params: Dict,
         headers: dict,
+        litellm_params: Optional[dict] = None,
     ) -> dict:
         """
         Transform request to Fireworks AI rerank format
diff --git a/litellm/llms/hosted_vllm/rerank/transformation.py b/litellm/llms/hosted_vllm/rerank/transformation.py
index 8066e53afc7..60b6dc7d23d 100644
--- a/litellm/llms/hosted_vllm/rerank/transformation.py
+++ b/litellm/llms/hosted_vllm/rerank/transformation.py
@@ -121,6 +121,7 @@ class HostedVLLMRerankConfig(BaseRerankConfig):
         model: str,
         optional_rerank_params: Dict,
         headers: dict,
+        litellm_params: Optional[dict] = None,
     ) -> dict:
         if "query" not in optional_rerank_params:
             raise ValueError("query is required for Hosted VLLM rerank")
diff --git a/litellm/llms/huggingface/rerank/transformation.py b/litellm/llms/huggingface/rerank/transformation.py
index 3f83b8e422d..2c847b617ef 100644
--- a/litellm/llms/huggingface/rerank/transformation.py
+++ b/litellm/llms/huggingface/rerank/transformation.py
@@ -146,6 +146,7 @@ class HuggingFaceRerankConfig(BaseRerankConfig):
         model: str,
         optional_rerank_params: Union[OptionalRerankParams, dict],
         headers: dict,
+        litellm_params: Optional[dict] = None,
     ) -> dict:
         if "query" not in optional_rerank_params:
             raise ValueError("query is required for HuggingFace rerank")
diff --git a/litellm/llms/jina_ai/rerank/transformation.py b/litellm/llms/jina_ai/rerank/transformation.py
index 48d876f8ea2..ad4416925a6 100644
--- a/litellm/llms/jina_ai/rerank/transformation.py
+++ b/litellm/llms/jina_ai/rerank/transformation.py
@@ -74,7 +74,11 @@ class JinaAIRerankConfig(BaseRerankConfig):
         return cleaned_base
 
     def transform_rerank_request(
-        self, model: str, optional_rerank_params: Dict, headers: Dict
+        self,
+        model: str,
+        optional_rerank_params: Dict,
+        headers: Dict,
+        litellm_params: Optional[dict] = None,
     ) -> Dict:
         return {"model": model, **optional_rerank_params}
 
diff --git a/litellm/llms/nvidia_nim/rerank/ranking_transformation.py b/litellm/llms/nvidia_nim/rerank/ranking_transformation.py
index 757d874bf31..b9a46b8ac2b 100644
--- a/litellm/llms/nvidia_nim/rerank/ranking_transformation.py
+++ b/litellm/llms/nvidia_nim/rerank/ranking_transformation.py
@@ -66,6 +66,7 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig):
         model: str,
         optional_rerank_params: Dict,
         headers: dict,
+        litellm_params: Optional[dict] = None,
     ) -> dict:
         """
         Transform request, using clean model name without 'ranking/' prefix.
@@ -75,4 +76,5 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig):
             model=clean_model,
             optional_rerank_params=optional_rerank_params,
             headers=headers,
+            litellm_params=litellm_params,
         )
diff --git a/litellm/llms/nvidia_nim/rerank/transformation.py b/litellm/llms/nvidia_nim/rerank/transformation.py
index bd5abac60c8..fc317293acc 100644
--- a/litellm/llms/nvidia_nim/rerank/transformation.py
+++ b/litellm/llms/nvidia_nim/rerank/transformation.py
@@ -177,6 +177,7 @@ class NvidiaNimRerankConfig(BaseRerankConfig):
         model: str,
         optional_rerank_params: Dict,
         headers: dict,
+        litellm_params: Optional[dict] = None,
     ) -> dict:
         """
         Transform request to Nvidia NIM format.
diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py
index 5895a91f3aa..a8cc5e7bdec 100644
--- a/litellm/llms/vertex_ai/common_utils.py
+++ b/litellm/llms/vertex_ai/common_utils.py
@@ -27,6 +27,46 @@ class VertexAIError(BaseLLMException):
         super().__init__(message=message, status_code=status_code, headers=headers)
 
 
+def vertex_request_labels_from_litellm_params(
+    litellm_params: Optional[dict],
+) -> Optional[Dict[str, str]]:
+    """
+    Build Vertex/GCP billing labels from LiteLLM ``litellm_params["metadata"]``,
+    using ``requester_metadata`` string key-value pairs (same convention as Gemini).
+    """
+    if not litellm_params or "metadata" not in litellm_params:
+        return None
+    metadata = litellm_params["metadata"]
+    if metadata is None or not isinstance(metadata, dict):
+        return None
+    if "requester_metadata" not in metadata:
+        return None
+    rm = metadata["requester_metadata"]
+    if not isinstance(rm, dict):
+        return None
+    labels = {k: v for k, v in rm.items() if isinstance(v, str)}
+    return labels if labels else None
+
+
+def pop_vertex_request_labels(
+    optional_params: Optional[dict],
+    litellm_params: Optional[dict],
+) -> Optional[Dict[str, str]]:
+    """
+    Resolve labels from optional ``labels`` (Gemini-style) and/or
+    ``litellm_params["metadata"]["requester_metadata"]``. Pops ``labels`` from
+    optional_params when present.
+    """
+    labels: Optional[Dict[str, str]] = None
+    if optional_params is not None and "labels" in optional_params:
+        raw = optional_params.pop("labels")
+        if isinstance(raw, dict):
+            labels = {k: v for k, v in raw.items() if isinstance(v, str)}
+    if labels is None:
+        labels = vertex_request_labels_from_litellm_params(litellm_params)
+    return labels if labels else None
+
+
 class VertexAIModelRoute(str, Enum):
     """Enum for Vertex AI model routing"""
 
diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py
index 7945c44d44c..723fa2b1616 100644
--- a/litellm/llms/vertex_ai/gemini/transformation.py
+++ b/litellm/llms/vertex_ai/gemini/transformation.py
@@ -23,6 +23,7 @@ from litellm.litellm_core_utils.prompt_templates.factory import (
     response_schema_prompt,
 )
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.llms.vertex_ai.common_utils import pop_vertex_request_labels
 from litellm.types.files import (
     get_file_mime_type_for_file_type,
     get_file_type_from_extension,
@@ -711,16 +712,8 @@ def _transform_request_body(  # noqa: PLR0915
         optional_params.pop("output_config", None)
         config_fields = GenerationConfig.__annotations__.keys()
 
-        # If the LiteLLM client sends Gemini-supported parameter "labels", add it
-        # as "labels" field to the request sent to the Gemini backend.
-        labels: Optional[dict[str, str]] = optional_params.pop("labels", None)
-        # If the LiteLLM client sends OpenAI-supported parameter "metadata", add it
-        # as "labels" field to the request sent to the Gemini backend.
-        if labels is None and "metadata" in litellm_params:
-            metadata = litellm_params["metadata"]
-            if metadata is not None and "requester_metadata" in metadata:
-                rm = metadata["requester_metadata"]
-                labels = {k: v for k, v in rm.items() if isinstance(v, str)}
+        # labels: optional explicit param and/or metadata.requester_metadata (OpenAI metadata)
+        labels = pop_vertex_request_labels(optional_params, litellm_params)
 
         filtered_params = {
             k: v
diff --git a/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py b/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py
index 1c7696d55a2..05ebd685d91 100644
--- a/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py
+++ b/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py
@@ -7,7 +7,10 @@ import litellm
 from litellm.llms.base_llm.image_generation.transformation import (
     BaseImageGenerationConfig,
 )
-from litellm.llms.vertex_ai.common_utils import get_vertex_base_url
+from litellm.llms.vertex_ai.common_utils import (
+    get_vertex_base_url,
+    pop_vertex_request_labels,
+)
 from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import (
@@ -203,13 +206,16 @@ class VertexAIImagenImageGenerationConfig(BaseImageGenerationConfig, VertexLLM):
             "sampleCount": 1,
         }
 
-        # Merge with optional params
+        labels = pop_vertex_request_labels(optional_params, litellm_params)
+        # Merge with optional params (after popping labels so they are not sent as Imagen parameters)
         parameters = {**default_params, **optional_params}
 
-        request_body = {
+        request_body: dict = {
             "instances": [{"prompt": prompt}],
             "parameters": parameters,
         }
+        if labels:
+            request_body["labels"] = labels
 
         return request_body
 
diff --git a/litellm/llms/vertex_ai/rerank/transformation.py b/litellm/llms/vertex_ai/rerank/transformation.py
index 53651839671..3b84972e946 100644
--- a/litellm/llms/vertex_ai/rerank/transformation.py
+++ b/litellm/llms/vertex_ai/rerank/transformation.py
@@ -11,12 +11,15 @@ import httpx
 import litellm
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
 from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
+from litellm.llms.vertex_ai.common_utils import (
+    vertex_request_labels_from_litellm_params,
+)
 from litellm.llms.vertex_ai.vertex_llm_base import VertexBase
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.rerank import (
+    RerankBilledUnits,
     RerankResponse,
     RerankResponseMeta,
-    RerankBilledUnits,
     RerankResponseResult,
 )
 
@@ -109,6 +112,7 @@ class VertexAIRerankConfig(BaseRerankConfig, VertexBase):
         model: str,
         optional_rerank_params: Dict,
         headers: dict,
+        litellm_params: Optional[dict] = None,
     ) -> dict:
         """
         Transform the request from Cohere format to Vertex AI Discovery Engine format
@@ -145,6 +149,10 @@ class VertexAIRerankConfig(BaseRerankConfig, VertexBase):
         # When return_documents is False, we want to ignore record details (return only IDs)
         request_data["ignoreRecordDetailsInResponse"] = not return_documents
 
+        user_labels = vertex_request_labels_from_litellm_params(litellm_params)
+        if user_labels:
+            request_data["userLabels"] = user_labels
+
         return request_data
 
     def transform_rerank_response(
diff --git a/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py b/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py
index 5fffd983c24..729cc9c3ead 100644
--- a/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py
+++ b/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py
@@ -1,4 +1,4 @@
-from typing import Literal, Optional, Union
+from typing import Dict, Literal, Optional, Union
 
 import httpx
 
@@ -44,6 +44,7 @@ class VertexEmbedding(VertexBase):
         vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES] = None,
         gemini_api_key: Optional[str] = None,
         extra_headers: Optional[dict] = None,
+        litellm_params: Optional[Dict] = None,
     ) -> EmbeddingResponse:
         if aembedding is True:
             return self.async_embedding(  # type: ignore
@@ -61,6 +62,7 @@ class VertexEmbedding(VertexBase):
                 vertex_credentials=vertex_credentials,
                 gemini_api_key=gemini_api_key,
                 extra_headers=extra_headers,
+                litellm_params=litellm_params,
             )
 
         should_use_v1beta1_features = self.is_using_v1beta1_features(
@@ -91,7 +93,10 @@ class VertexEmbedding(VertexBase):
         )
         headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers)
         vertex_request: VertexEmbeddingRequest = litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
-            input=input, optional_params=optional_params, model=model
+            input=input,
+            optional_params=optional_params,
+            model=model,
+            litellm_params=litellm_params,
         )
 
         _client_params = {}
@@ -154,6 +159,7 @@ class VertexEmbedding(VertexBase):
         gemini_api_key: Optional[str] = None,
         extra_headers: Optional[dict] = None,
         encoding=None,
+        litellm_params: Optional[Dict] = None,
     ) -> EmbeddingResponse:
         """
         Async embedding implementation
@@ -185,7 +191,10 @@ class VertexEmbedding(VertexBase):
         )
         headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers)
         vertex_request: VertexEmbeddingRequest = litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
-            input=input, optional_params=optional_params, model=model
+            input=input,
+            optional_params=optional_params,
+            model=model,
+            litellm_params=litellm_params,
         )
 
         _async_client_params = {}
diff --git a/litellm/llms/vertex_ai/vertex_embeddings/transformation.py b/litellm/llms/vertex_ai/vertex_embeddings/transformation.py
index 132f29987af..a3018c5bd6b 100644
--- a/litellm/llms/vertex_ai/vertex_embeddings/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_embeddings/transformation.py
@@ -3,6 +3,7 @@ from typing import List, Literal, Optional, Union
 
 from pydantic import BaseModel
 
+from litellm.llms.vertex_ai.common_utils import pop_vertex_request_labels
 from litellm.types.utils import EmbeddingResponse, Usage
 
 from .types import *
@@ -100,7 +101,11 @@ class VertexAITextEmbeddingConfig(BaseModel):
         return optional_params
 
     def transform_openai_request_to_vertex_embedding_request(
-        self, input: Union[list, str], optional_params: dict, model: str
+        self,
+        input: Union[list, str],
+        optional_params: dict,
+        model: str,
+        litellm_params: Optional[dict] = None,
     ) -> VertexEmbeddingRequest:
         """
         Transforms an openai request to a vertex embedding request.
@@ -108,14 +113,24 @@ class VertexAITextEmbeddingConfig(BaseModel):
         # Import here to avoid circular import issues with litellm.__init__
         from litellm.llms.vertex_ai.vertex_embeddings.bge import VertexBGEConfig
 
+        labels = pop_vertex_request_labels(optional_params, litellm_params)
+
         if model.isdigit():
-            return self._transform_openai_request_to_fine_tuned_embedding_request(
-                input, optional_params, model
+            vertex_request = (
+                self._transform_openai_request_to_fine_tuned_embedding_request(
+                    input, optional_params, model
+                )
             )
+            if labels:
+                vertex_request["labels"] = labels
+            return vertex_request
         if VertexBGEConfig.is_bge_model(model):
-            return VertexBGEConfig.transform_request(
+            vertex_request = VertexBGEConfig.transform_request(
                 input=input, optional_params=optional_params, model=model
             )
+            if labels:
+                vertex_request["labels"] = labels
+            return vertex_request
 
         vertex_request: VertexEmbeddingRequest = VertexEmbeddingRequest()
         vertex_text_embedding_input_list: List[TextEmbeddingInput] = []
@@ -133,6 +148,8 @@ class VertexAITextEmbeddingConfig(BaseModel):
 
         vertex_request["instances"] = vertex_text_embedding_input_list
         vertex_request["parameters"] = EmbeddingParameters(**optional_params)
+        if labels:
+            vertex_request["labels"] = labels
 
         return vertex_request
 
diff --git a/litellm/llms/vertex_ai/vertex_embeddings/types.py b/litellm/llms/vertex_ai/vertex_embeddings/types.py
index 317b9c4fb81..bf73f4d193a 100644
--- a/litellm/llms/vertex_ai/vertex_embeddings/types.py
+++ b/litellm/llms/vertex_ai/vertex_embeddings/types.py
@@ -3,7 +3,7 @@ Types for Vertex Embeddings Requests
 """
 
 from enum import Enum
-from typing import List, Optional, Union
+from typing import Dict, List, Optional, Union
 
 from typing_extensions import TypedDict
 
@@ -56,6 +56,7 @@ class VertexEmbeddingRequest(TypedDict, total=False):
         List[TextEmbeddingFineTunedInput],
     ]
     parameters: Optional[Union[EmbeddingParameters, TextEmbeddingFineTunedParameters]]
+    labels: Optional[Dict[str, str]]
 
 
 # Example usage:
diff --git a/litellm/llms/voyage/rerank/transformation.py b/litellm/llms/voyage/rerank/transformation.py
index 521dae980d5..d64450a1211 100644
--- a/litellm/llms/voyage/rerank/transformation.py
+++ b/litellm/llms/voyage/rerank/transformation.py
@@ -67,7 +67,11 @@ class VoyageRerankConfig(BaseRerankConfig):
         return api_base
 
     def transform_rerank_request(
-        self, model: str, optional_rerank_params: Dict, headers: Dict
+        self,
+        model: str,
+        optional_rerank_params: Dict,
+        headers: Dict,
+        litellm_params: Optional[dict] = None,
     ) -> Dict:
         return {"model": model, **optional_rerank_params}
 
diff --git a/litellm/llms/watsonx/rerank/transformation.py b/litellm/llms/watsonx/rerank/transformation.py
index 4f8e196f254..202760f68a6 100644
--- a/litellm/llms/watsonx/rerank/transformation.py
+++ b/litellm/llms/watsonx/rerank/transformation.py
@@ -143,6 +143,7 @@ class IBMWatsonXRerankConfig(IBMWatsonXMixin, BaseRerankConfig):
         model: str,
         optional_rerank_params: Dict,
         headers: dict,
+        litellm_params: Optional[dict] = None,
     ) -> dict:
         """
         Transform request to IBM watsonx.ai rerank format
diff --git a/litellm/main.py b/litellm/main.py
index 14dac7a6727..47a13d1be1e 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -5310,6 +5310,7 @@ def embedding(  # noqa: PLR0915
                     api_key=api_key,
                     api_base=api_base,
                     client=client,
+                    litellm_params=litellm_params_dict,
                 )
         elif custom_llm_provider == "oobabooga":
             response = oobabooga.embedding(
diff --git a/litellm/rerank_api/main.py b/litellm/rerank_api/main.py
index 9868634362f..0dd18b6a5bf 100644
--- a/litellm/rerank_api/main.py
+++ b/litellm/rerank_api/main.py
@@ -163,19 +163,21 @@ def rerank(  # noqa: PLR0915
 
         model_response = RerankResponse()
 
+        rerank_litellm_params = {
+            "litellm_call_id": litellm_call_id,
+            "proxy_server_request": proxy_server_request,
+            "model_info": model_info,
+            "preset_cache_key": None,
+            "stream_response": {},
+            **optional_params.model_dump(exclude_unset=True),
+        }
+
         litellm_logging_obj.update_from_kwargs(
             kwargs=kwargs,
             model=model,
             user=user,
             optional_params=dict(optional_rerank_params),
-            litellm_params={
-                "litellm_call_id": litellm_call_id,
-                "proxy_server_request": proxy_server_request,
-                "model_info": model_info,
-                "preset_cache_key": None,
-                "stream_response": {},
-                **optional_params.model_dump(exclude_unset=True),
-            },
+            litellm_params=rerank_litellm_params,
             custom_llm_provider=_custom_llm_provider,
         )
 
@@ -214,6 +216,7 @@ def rerank(  # noqa: PLR0915
                 headers=headers or litellm.headers or {},
                 client=client,
                 model_response=model_response,
+                litellm_params=rerank_litellm_params,
             )
         elif _custom_llm_provider == litellm.LlmProviders.AZURE_AI:
             api_base = (
@@ -235,6 +238,7 @@ def rerank(  # noqa: PLR0915
                 headers=headers or litellm.headers or {},
                 client=client,
                 model_response=model_response,
+                litellm_params=rerank_litellm_params,
             )
         elif _custom_llm_provider == litellm.LlmProviders.INFINITY:
             # Implement Infinity rerank logic
@@ -265,6 +269,7 @@ def rerank(  # noqa: PLR0915
                 headers=headers or litellm.headers or {},
                 client=client,
                 model_response=model_response,
+                litellm_params=rerank_litellm_params,
             )
         elif _custom_llm_provider == litellm.LlmProviders.TOGETHER_AI:
             # Implement Together AI rerank logic
@@ -318,6 +323,7 @@ def rerank(  # noqa: PLR0915
                 headers=headers or litellm.headers or {},
                 client=client,
                 model_response=model_response,
+                litellm_params=rerank_litellm_params,
             )
         elif _custom_llm_provider == litellm.LlmProviders.NVIDIA_NIM:
             if dynamic_api_key is None:
@@ -346,6 +352,7 @@ def rerank(  # noqa: PLR0915
                 headers=headers or litellm.headers or {},
                 client=client,
                 model_response=model_response,
+                litellm_params=rerank_litellm_params,
             )
         elif _custom_llm_provider == litellm.LlmProviders.BEDROCK:
             api_base = (
@@ -409,6 +416,7 @@ def rerank(  # noqa: PLR0915
                 headers=headers or litellm.headers or {},
                 client=client,
                 model_response=model_response,
+                litellm_params=rerank_litellm_params,
             )
 
         elif _custom_llm_provider == litellm.LlmProviders.DEEPINFRA:
@@ -442,6 +450,7 @@ def rerank(  # noqa: PLR0915
                 headers=headers or litellm.headers or {},
                 client=client,
                 model_response=model_response,
+                litellm_params=rerank_litellm_params,
             )
         elif _custom_llm_provider == litellm.LlmProviders.FIREWORKS_AI:
             api_key = (
@@ -472,6 +481,7 @@ def rerank(  # noqa: PLR0915
                 headers=headers or litellm.headers or {},
                 client=client,
                 model_response=model_response,
+                litellm_params=rerank_litellm_params,
             )
         elif _custom_llm_provider == litellm.LlmProviders.VOYAGE:
             api_key = (
@@ -500,6 +510,7 @@ def rerank(  # noqa: PLR0915
                 headers=headers or litellm.headers or {},
                 client=client,
                 model_response=model_response,
+                litellm_params=rerank_litellm_params,
             )
         elif _custom_llm_provider == litellm.LlmProviders.WATSONX:
             credentials = IBMWatsonXMixin.get_watsonx_credentials(
@@ -527,6 +538,7 @@ def rerank(  # noqa: PLR0915
                 headers=headers or litellm.headers or {},
                 client=client,
                 model_response=model_response,
+                litellm_params=rerank_litellm_params,
             )
         else:
             # Generic handler for all providers that use base_llm_http_handler
@@ -559,6 +571,7 @@ def rerank(  # noqa: PLR0915
                 headers=headers or litellm.headers or {},
                 client=client,
                 model_response=model_response,
+                litellm_params=rerank_litellm_params,
             )
 
         # Placeholder return
diff --git a/tests/test_litellm/llms/vertex_ai/image_generation/test_vertex_ai_image_generation_transformation.py b/tests/test_litellm/llms/vertex_ai/image_generation/test_vertex_ai_image_generation_transformation.py
index 350fd75d3d8..61b5e3c9249 100644
--- a/tests/test_litellm/llms/vertex_ai/image_generation/test_vertex_ai_image_generation_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/image_generation/test_vertex_ai_image_generation_transformation.py
@@ -67,7 +67,9 @@ class TestVertexAIGeminiImageGenerationConfig:
 
     def test_get_supported_openai_params_includes_native_gemini_params(self):
         """Test that native Gemini imageConfig params are supported"""
-        supported = self.config.get_supported_openai_params("gemini-3-pro-image-preview")
+        supported = self.config.get_supported_openai_params(
+            "gemini-3-pro-image-preview"
+        )
         assert "aspectRatio" in supported
         assert "aspect_ratio" in supported
         assert "imageSize" in supported
@@ -188,11 +190,11 @@ class TestVertexAIGeminiImageGenerationConfig:
                     {
                         "modality": "IMAGE",
                         "tokenCount": 39,
-                    }
+                    },
                 ],
                 "candidatesTokenCount": 17,
                 "totalTokenCount": 110,
-            }
+            },
         }
         mock_response.headers = {}
 
@@ -219,7 +221,6 @@ class TestVertexAIGeminiImageGenerationConfig:
         assert result.usage.output_tokens == 17
         assert result.usage.total_tokens == 110
 
-
     def test_transform_image_generation_response_multiple_images(self):
         """Test response transformation with multiple images"""
         mock_response = MagicMock(spec=httpx.Response)
@@ -305,7 +306,10 @@ class TestVertexAIGeminiImageGenerationConfig:
 
         assert len(result.data) == 1
         assert result.data[0].b64_json == "base64_encoded_image_data"
-        assert result.data[0].provider_specific_fields["thought_signature"] == "test_signature_abc123"
+        assert (
+            result.data[0].provider_specific_fields["thought_signature"]
+            == "test_signature_abc123"
+        )
 
 
 class TestVertexAIImagenImageGenerationConfig:
@@ -369,14 +373,26 @@ class TestVertexAIImagenImageGenerationConfig:
         assert request["parameters"]["sampleCount"] == 2
         assert request["parameters"]["aspectRatio"] == "16:9"
 
+    def test_transform_image_generation_request_labels_from_metadata(self):
+        """Billing labels from litellm_params.metadata.requester_metadata on predict body."""
+        request = self.config.transform_image_generation_request(
+            model="imagegeneration@006",
+            prompt="A cat",
+            optional_params={},
+            litellm_params={
+                "metadata": {"requester_metadata": {"team": "platform", "env": "prod"}}
+            },
+            headers={},
+        )
+        assert request["labels"] == {"team": "platform", "env": "prod"}
+        assert "labels" not in request["parameters"]
+
     def test_transform_image_generation_response(self):
         """Test response transformation"""
         mock_response = MagicMock(spec=httpx.Response)
         mock_response.status_code = 200
         mock_response.json.return_value = {
-            "predictions": [
-                {"bytesBase64Encoded": "base64_encoded_image_data"}
-            ]
+            "predictions": [{"bytesBase64Encoded": "base64_encoded_image_data"}]
         }
         mock_response.headers = {}
 
@@ -453,9 +469,7 @@ class TestGetVertexAIImageGenerationConfig:
         config = get_vertex_ai_image_generation_config("imagen-4.0-generate-001")
         assert isinstance(config, VertexAIImagenImageGenerationConfig)
 
-        config = get_vertex_ai_image_generation_config(
-            "vertex_ai/imagegeneration@006"
-        )
+        config = get_vertex_ai_image_generation_config("vertex_ai/imagegeneration@006")
         assert isinstance(config, VertexAIImagenImageGenerationConfig)
 
     def test_get_non_gemini_model_config(self):
@@ -548,4 +562,3 @@ class TestVertexAIImageGenerationIntegration:
         assert "us-central1" in url
         assert "imagegeneration@006" in url
         assert "predict" in url
-
diff --git a/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py
index 5bf2cb97fa9..fb5b0c76eaa 100644
--- a/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py
@@ -63,13 +63,16 @@ class TestVertexAIRerankTransform:
             import litellm
 
             # Set vertex_project attribute if it doesn't exist
-            if not hasattr(litellm, 'vertex_project'):
+            if not hasattr(litellm, "vertex_project"):
                 litellm.vertex_project = None
             original_project = litellm.vertex_project
             litellm.vertex_project = "litellm-project-456"
             # Reset mock call count
             mock_ensure_access_token.reset_mock()
-            mock_ensure_access_token.return_value = ("mock-token", "litellm-project-456")
+            mock_ensure_access_token.return_value = (
+                "mock-token",
+                "litellm-project-456",
+            )
             try:
                 url = self.config.get_complete_url(api_base=None, model=self.model)
                 expected_url = "https://discoveryengine.googleapis.com/v1/projects/litellm-project-456/locations/global/rankingConfigs/default_ranking_config:rank"
@@ -82,15 +85,19 @@ class TestVertexAIRerankTransform:
             import litellm
 
             # Set vertex_project to None to ensure no project ID is available
-            if not hasattr(litellm, 'vertex_project'):
+            if not hasattr(litellm, "vertex_project"):
                 litellm.vertex_project = None
             original_project = litellm.vertex_project
             litellm.vertex_project = None
             # Reset mock and set it to raise an error
             mock_ensure_access_token.reset_mock()
-            mock_ensure_access_token.side_effect = ValueError("Vertex AI project ID is required")
+            mock_ensure_access_token.side_effect = ValueError(
+                "Vertex AI project ID is required"
+            )
             try:
-                with pytest.raises(ValueError, match="Vertex AI project ID is required"):
+                with pytest.raises(
+                    ValueError, match="Vertex AI project ID is required"
+                ):
                     self.config.get_complete_url(api_base=None, model=self.model)
             finally:
                 litellm.vertex_project = original_project
@@ -109,15 +116,13 @@ class TestVertexAIRerankTransform:
         self.config._ensure_access_token = mock_ensure_access_token
 
         headers = self.config.validate_environment(
-            headers={},
-            model=self.model,
-            api_key=None
+            headers={}, model=self.model, api_key=None
         )
 
         expected_headers = {
             "Authorization": "Bearer test-access-token",
             "Content-Type": "application/json",
-            "X-Goog-User-Project": "test-project-123"
+            "X-Goog-User-Project": "test-project-123",
         }
         assert headers == expected_headers
 
@@ -127,24 +132,22 @@ class TestVertexAIRerankTransform:
             "query": "What is Google Gemini?",
             "documents": [
                 "Gemini is a cutting edge large language model created by Google.",
-                "The Gemini zodiac symbol often depicts two figures standing side-by-side."
+                "The Gemini zodiac symbol often depicts two figures standing side-by-side.",
             ],
-            "top_n": 2
+            "top_n": 2,
         }
-        
+
         request_data = self.config.transform_rerank_request(
-            model=self.model,
-            optional_rerank_params=optional_params,
-            headers={}
+            model=self.model, optional_rerank_params=optional_params, headers={}
         )
-        
+
         # Verify basic structure
         assert request_data["model"] == self.model
         assert request_data["query"] == "What is Google Gemini?"
         assert request_data["topN"] == 2
         assert "records" in request_data
         assert len(request_data["records"]) == 2
-        
+
         # Verify record structure
         for i, record in enumerate(request_data["records"]):
             assert "id" in record
@@ -158,20 +161,25 @@ class TestVertexAIRerankTransform:
         optional_params = {
             "query": "What is Google Gemini?",
             "documents": [
-                {"text": "Gemini is a cutting edge large language model created by Google.", "title": "Custom Title 1"},
-                {"text": "The Gemini zodiac symbol often depicts two figures standing side-by-side."}
-            ]
+                {
+                    "text": "Gemini is a cutting edge large language model created by Google.",
+                    "title": "Custom Title 1",
+                },
+                {
+                    "text": "The Gemini zodiac symbol often depicts two figures standing side-by-side."
+                },
+            ],
         }
-        
+
         request_data = self.config.transform_rerank_request(
-            model=self.model,
-            optional_rerank_params=optional_params,
-            headers={}
+            model=self.model, optional_rerank_params=optional_params, headers={}
         )
-        
+
         # Verify record structure with custom titles
         assert request_data["records"][0]["title"] == "Custom Title 1"
-        assert request_data["records"][1]["title"] == "The Gemini zodiac"  # First 3 words
+        assert (
+            request_data["records"][1]["title"] == "The Gemini zodiac"
+        )  # First 3 words
 
     def test_transform_rerank_request_return_documents_mapping(self):
         """Test return_documents to ignoreRecordDetailsInResponse mapping."""
@@ -179,43 +187,50 @@ class TestVertexAIRerankTransform:
         optional_params_true = {
             "query": "test query",
             "documents": ["doc1", "doc2"],
-            "return_documents": True
+            "return_documents": True,
         }
-        
+
         request_data_true = self.config.transform_rerank_request(
-            model=self.model,
-            optional_rerank_params=optional_params_true,
-            headers={}
+            model=self.model, optional_rerank_params=optional_params_true, headers={}
         )
         assert request_data_true["ignoreRecordDetailsInResponse"] == False
-        
+
         # Test return_documents=False
         optional_params_false = {
             "query": "test query",
             "documents": ["doc1", "doc2"],
-            "return_documents": False
+            "return_documents": False,
         }
-        
+
         request_data_false = self.config.transform_rerank_request(
-            model=self.model,
-            optional_rerank_params=optional_params_false,
-            headers={}
+            model=self.model, optional_rerank_params=optional_params_false, headers={}
         )
         assert request_data_false["ignoreRecordDetailsInResponse"] == True
-        
+
         # Test return_documents not specified (should default to True)
-        optional_params_default = {
-            "query": "test query",
-            "documents": ["doc1", "doc2"]
-        }
-        
+        optional_params_default = {"query": "test query", "documents": ["doc1", "doc2"]}
+
         request_data_default = self.config.transform_rerank_request(
-            model=self.model,
-            optional_rerank_params=optional_params_default,
-            headers={}
+            model=self.model, optional_rerank_params=optional_params_default, headers={}
         )
         assert request_data_default["ignoreRecordDetailsInResponse"] == False
 
+    def test_transform_rerank_request_user_labels_from_metadata(self):
+        """Discovery Engine Rank API uses userLabels (string map) for billing."""
+        optional_params = {
+            "query": "q",
+            "documents": ["a", "b"],
+        }
+        request_data = self.config.transform_rerank_request(
+            model=self.model,
+            optional_rerank_params=optional_params,
+            headers={},
+            litellm_params={
+                "metadata": {"requester_metadata": {"app": "litellm", "tier": "1"}}
+            },
+        )
+        assert request_data["userLabels"] == {"app": "litellm", "tier": "1"}
+
     def test_transform_rerank_request_missing_required_params(self):
         """Test that transform_rerank_request handles missing required parameters."""
         # Test missing query
@@ -223,15 +238,17 @@ class TestVertexAIRerankTransform:
             self.config.transform_rerank_request(
                 model=self.model,
                 optional_rerank_params={"documents": ["doc1"]},
-                headers={}
+                headers={},
             )
-        
+
         # Test missing documents
-        with pytest.raises(ValueError, match="documents is required for Vertex AI rerank"):
+        with pytest.raises(
+            ValueError, match="documents is required for Vertex AI rerank"
+        ):
             self.config.transform_rerank_request(
                 model=self.model,
                 optional_rerank_params={"query": "test query"},
-                headers={}
+                headers={},
             )
 
     def test_transform_rerank_response_success(self):
@@ -243,34 +260,34 @@ class TestVertexAIRerankTransform:
                     "id": "1",
                     "score": 0.98,
                     "title": "The Science of a Blue Sky",
-                    "content": "The sky appears blue due to a phenomenon called Rayleigh scattering."
+                    "content": "The sky appears blue due to a phenomenon called Rayleigh scattering.",
                 },
                 {
                     "id": "0",
                     "score": 0.64,
                     "title": "The Color of the Sky: A Poem",
-                    "content": "A canvas stretched across the day, Where sunlight learns to dance and play."
-                }
+                    "content": "A canvas stretched across the day, Where sunlight learns to dance and play.",
+                },
             ]
         }
-        
+
         # Create mock httpx response
         mock_response = MagicMock(spec=httpx.Response)
         mock_response.json.return_value = response_data
         mock_response.text = json.dumps(response_data)
-        
+
         # Create mock logging object
         mock_logging = MagicMock()
-        
+
         model_response = RerankResponse()
-        
+
         result = self.config.transform_rerank_response(
             model=self.model,
             raw_response=mock_response,
             model_response=model_response,
             logging_obj=mock_logging,
         )
-        
+
         # Verify response structure
         assert result.id == f"vertex_ai_rerank_{self.model}"
         assert len(result.results) == 2
@@ -278,34 +295,29 @@ class TestVertexAIRerankTransform:
         assert result.results[0]["relevance_score"] == 0.98
         assert result.results[1]["index"] == 0
         assert result.results[1]["relevance_score"] == 0.64
-        
+
         # Verify metadata
         assert result.meta["billed_units"]["search_units"] == 2
 
     def test_transform_rerank_response_with_ignore_record_details(self):
         """Test response transformation when ignoreRecordDetailsInResponse=true."""
         # Mock response with only IDs (when ignoreRecordDetailsInResponse=true)
-        response_data = {
-            "records": [
-                {"id": "1"},
-                {"id": "0"}
-            ]
-        }
-        
+        response_data = {"records": [{"id": "1"}, {"id": "0"}]}
+
         mock_response = MagicMock(spec=httpx.Response)
         mock_response.json.return_value = response_data
         mock_response.text = json.dumps(response_data)
-        
+
         mock_logging = MagicMock()
         model_response = RerankResponse()
-        
+
         result = self.config.transform_rerank_response(
             model=self.model,
             raw_response=mock_response,
             model_response=model_response,
             logging_obj=mock_logging,
         )
-        
+
         # Verify response structure with default scores
         assert len(result.results) == 2
         assert result.results[0]["index"] == 1  # 0-based index
@@ -318,10 +330,10 @@ class TestVertexAIRerankTransform:
         mock_response = MagicMock(spec=httpx.Response)
         mock_response.json.side_effect = json.JSONDecodeError("Invalid JSON", "doc", 0)
         mock_response.text = "Invalid JSON response"
-        
+
         mock_logging = MagicMock()
         model_response = RerankResponse()
-        
+
         with pytest.raises(ValueError, match="Failed to parse response"):
             self.config.transform_rerank_response(
                 model=self.model,
@@ -345,14 +357,14 @@ class TestVertexAIRerankTransform:
             query="test query",
             documents=["doc1", "doc2"],
             top_n=2,
-            return_documents=True
+            return_documents=True,
         )
-        
+
         expected_params = {
             "query": "test query",
             "documents": ["doc1", "doc2"],
             "top_n": 2,
-            "return_documents": True
+            "return_documents": True,
         }
         assert params == expected_params
 
@@ -363,34 +375,32 @@ class TestVertexAIRerankTransform:
             "documents": [
                 "This is a very long document with many words that should be truncated to only the first three words for the title",
                 "Short doc",
-                "Another document with multiple words here"
-            ]
+                "Another document with multiple words here",
+            ],
         }
-        
+
         request_data = self.config.transform_rerank_request(
-            model=self.model,
-            optional_rerank_params=optional_params,
-            headers={}
+            model=self.model, optional_rerank_params=optional_params, headers={}
         )
-        
+
         # Verify title generation
         assert request_data["records"][0]["title"] == "This is a"  # First 3 words
         assert request_data["records"][1]["title"] == "Short doc"  # Less than 3 words
-        assert request_data["records"][2]["title"] == "Another document with"  # First 3 words
+        assert (
+            request_data["records"][2]["title"] == "Another document with"
+        )  # First 3 words
 
     def test_record_id_generation(self):
         """Test that record IDs are generated correctly with 0-based indexing."""
         optional_params = {
             "query": "test query",
-            "documents": ["doc1", "doc2", "doc3", "doc4"]
+            "documents": ["doc1", "doc2", "doc3", "doc4"],
         }
-        
+
         request_data = self.config.transform_rerank_request(
-            model=self.model,
-            optional_rerank_params=optional_params,
-            headers={}
+            model=self.model, optional_rerank_params=optional_params, headers={}
         )
-        
+
         # Verify 0-based indexing
         for i, record in enumerate(request_data["records"]):
             assert record["id"] == str(i)
@@ -402,9 +412,9 @@ class TestVertexAIRerankTransform:
             "documents": ["doc1", "doc2"],
             "vertex_credentials": "path/to/credentials.json",
             "vertex_project": "my-project-id",
-            "vertex_location": "us-central1"
+            "vertex_location": "us-central1",
         }
-        
+
         params = self.config.map_cohere_rerank_params(
             non_default_params=non_default_params,
             model=self.model,
@@ -412,14 +422,14 @@ class TestVertexAIRerankTransform:
             query="test query",
             documents=["doc1", "doc2"],
             top_n=2,
-            return_documents=True
+            return_documents=True,
         )
-        
+
         # Verify vertex-specific parameters are preserved
         assert params["vertex_credentials"] == "path/to/credentials.json"
         assert params["vertex_project"] == "my-project-id"
         assert params["vertex_location"] == "us-central1"
-        
+
         # Verify standard params are still present
         assert params["query"] == "test query"
         assert params["documents"] == ["doc1", "doc2"]
@@ -428,10 +438,8 @@ class TestVertexAIRerankTransform:
 
     def test_map_cohere_rerank_params_without_vertex_credentials(self):
         """Test that map_cohere_rerank_params works when vertex credentials are not provided."""
-        non_default_params = {
-            "documents": ["doc1", "doc2"]
-        }
-        
+        non_default_params = {"documents": ["doc1", "doc2"]}
+
         params = self.config.map_cohere_rerank_params(
             non_default_params=non_default_params,
             model=self.model,
@@ -439,14 +447,14 @@ class TestVertexAIRerankTransform:
             query="test query",
             documents=["doc1", "doc2"],
             top_n=2,
-            return_documents=True
+            return_documents=True,
         )
-        
+
         # Verify no vertex-specific parameters are added when not provided
         assert "vertex_credentials" not in params
         assert "vertex_project" not in params
         assert "vertex_location" not in params
-        
+
         # Verify standard params are still present
         assert params["query"] == "test query"
         assert params["documents"] == ["doc1", "doc2"]
@@ -470,14 +478,11 @@ class TestVertexAIRerankTransform:
             "vertex_credentials": "path/to/credentials.json",
             "vertex_project": "custom-project-id",
             "query": "test query",
-            "documents": ["doc1"]
+            "documents": ["doc1"],
         }
 
         headers = self.config.validate_environment(
-            headers={},
-            model=self.model,
-            api_key=None,
-            optional_params=optional_params
+            headers={}, model=self.model, api_key=None, optional_params=optional_params
         )
 
         # Verify that _ensure_access_token was called with the credentials from optional_params
@@ -490,7 +495,7 @@ class TestVertexAIRerankTransform:
         expected_headers = {
             "Authorization": "Bearer test-access-token",
             "Content-Type": "application/json",
-            "X-Goog-User-Project": "test-project-123"
+            "X-Goog-User-Project": "test-project-123",
         }
         assert headers == expected_headers
 
@@ -527,7 +532,10 @@ class TestVertexAIRerankTransform:
         assert optional_params["vertex_project"] == "custom-project-id"
 
         # get_complete_url should still be able to access the vertex params
-        with patch('litellm.llms.vertex_ai.rerank.transformation.get_secret_str', return_value=None):
+        with patch(
+            "litellm.llms.vertex_ai.rerank.transformation.get_secret_str",
+            return_value=None,
+        ):
             url = self.config.get_complete_url(
                 api_base=None,
                 model=self.model,
diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
index d483a81a349..8a5c397d6f4 100644
--- a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
+++ b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
@@ -15,7 +15,9 @@ from litellm.llms.vertex_ai.common_utils import (
     convert_anyof_null_to_nullable,
     get_vertex_location_from_url,
     get_vertex_project_id_from_url,
+    pop_vertex_request_labels,
     set_schema_property_ordering,
+    vertex_request_labels_from_litellm_params,
 )
 
 
@@ -440,7 +442,9 @@ def test_vertex_ai_complex_response_schema():
     optional_params = {}
 
     v.apply_response_schema_transformation(
-        value=non_default_params["response_format"], optional_params=optional_params, model="gemini-1.5-pro-preview-0409"
+        value=non_default_params["response_format"],
+        optional_params=optional_params,
+        model="gemini-1.5-pro-preview-0409",
     )
 
     # Assertions for the transformed schema
@@ -558,7 +562,6 @@ def test_get_vertex_url_global_region(stream, expected_endpoint_suffix):
     assert url == expected_url
 
 
-
 @pytest.mark.parametrize(
     "model_cost_entry, vertex_region, expected_region",
     [
@@ -571,9 +574,17 @@ def test_get_vertex_url_global_region(stream, expected_endpoint_suffix):
         # Model with supported_regions=["us-west2"], no user region -> use "us-west2"
         ({"supported_regions": ["us-west2"]}, None, "us-west2"),
         # Model with supported_regions=["us-west2", "us-central1"], user passes supported region -> respect it
-        ({"supported_regions": ["us-west2", "us-central1"]}, "us-central1", "us-central1"),
+        (
+            {"supported_regions": ["us-west2", "us-central1"]},
+            "us-central1",
+            "us-central1",
+        ),
         # Model with supported_regions=["us-west2", "us-central1"], user passes unsupported region -> override
-        ({"supported_regions": ["us-west2", "us-central1"]}, "europe-west1", "us-west2"),
+        (
+            {"supported_regions": ["us-west2", "us-central1"]},
+            "europe-west1",
+            "us-west2",
+        ),
         # No model_cost entry, no user region -> default us-central1
         ({}, None, "us-central1"),
         # No model_cost entry, user specifies region -> use specified region
@@ -656,11 +667,12 @@ def test_vertex_filter_format_uri():
 
     assert "uri" not in json.dumps(new_parameters)
 
+
 def test_convert_schema_types_type_array_conversion():
     """
     Test _convert_schema_types function handles type arrays and case conversion.
-    
-    This test verifies the fix for the issue where type arrays like ["string", "number"] 
+
+    This test verifies the fix for the issue where type arrays like ["string", "number"]
     would raise an exception in Vertex AI schema validation.
 
     Relevant issue: https://github.com/BerriAI/litellm/issues/14091
@@ -673,12 +685,12 @@ def test_convert_schema_types_type_array_conversion():
         "properties": {
             "studio": {
                 "type": ["string", "number"],
-                "description": "The studio ID or name"
+                "description": "The studio ID or name",
             }
         },
         "required": ["studio"],
         "additionalProperties": False,
-        "$schema": "http://json-schema.org/draft-07/schema#"
+        "$schema": "http://json-schema.org/draft-07/schema#",
     }
 
     # Expected output: Vertex AI compatible schema with anyOf and uppercase types
@@ -686,16 +698,13 @@ def test_convert_schema_types_type_array_conversion():
         "type": "object",
         "properties": {
             "studio": {
-                "anyOf": [
-                    {"type": "string"}, 
-                    {"type": "number"}
-                ],
-                "description": "The studio ID or name"
+                "anyOf": [{"type": "string"}, {"type": "number"}],
+                "description": "The studio ID or name",
             }
         },
         "required": ["studio"],
         "additionalProperties": False,
-        "$schema": "http://json-schema.org/draft-07/schema#"
+        "$schema": "http://json-schema.org/draft-07/schema#",
     }
 
     # Apply the transformation
@@ -718,15 +727,17 @@ def test_convert_schema_types_type_array_conversion():
     assert anyof_types[1]["type"] == "number"
 
     # 4. Other properties preserved
-    assert input_schema["properties"]["studio"]["description"] == "The studio ID or name"
+    assert (
+        input_schema["properties"]["studio"]["description"] == "The studio ID or name"
+    )
     assert input_schema["required"] == ["studio"]
 
 
 def test_fix_enum_empty_strings():
     """
     Test _fix_enum_empty_strings function replaces empty strings with None in enum arrays.
-    
-    This test verifies the fix for the issue where Gemini rejects tool definitions 
+
+    This test verifies the fix for the issue where Gemini rejects tool definitions
     with empty strings in enum values, causing API failures.
 
     Relevant issue: Gemini does not accept empty strings in enum values
@@ -740,23 +751,23 @@ def test_fix_enum_empty_strings():
             "user_agent_type": {
                 "enum": ["", "desktop", "mobile", "tablet"],
                 "type": "string",
-                "description": "Device type for user agent"
+                "description": "Device type for user agent",
             }
         },
-        "required": ["user_agent_type"]
+        "required": ["user_agent_type"],
     }
 
     # Expected output: Empty strings replaced with None
     expected_output = {
-        "type": "object", 
+        "type": "object",
         "properties": {
             "user_agent_type": {
                 "enum": [None, "desktop", "mobile", "tablet"],
                 "type": "string",
-                "description": "Device type for user agent"
+                "description": "Device type for user agent",
             }
         },
-        "required": ["user_agent_type"]
+        "required": ["user_agent_type"],
     }
 
     # Apply the transformation
@@ -859,7 +870,7 @@ def test_construct_target_url_with_version_prefix():
 def test_fix_enum_types():
     """
     Test _fix_enum_types function removes enum fields when type is not string.
-    
+
     This test verifies the fix for the issue where Gemini rejects cached content
     with function parameter enums on non-string types, causing API failures.
 
@@ -874,38 +885,41 @@ def test_fix_enum_types():
             "truncateMode": {
                 "enum": ["auto", "none", "start", "end"],
                 "type": "string",  # This should keep the enum
-                "description": "How to truncate content"
+                "description": "How to truncate content",
             },
             "maxLength": {
                 "enum": [100, 200, 500],  # This should be removed
                 "type": "integer",
-                "description": "Maximum length"
+                "description": "Maximum length",
             },
             "enabled": {
                 "enum": [True, False],  # This should be removed
                 "type": "boolean",
-                "description": "Whether feature is enabled"
+                "description": "Whether feature is enabled",
             },
             "nested": {
                 "type": "object",
                 "properties": {
                     "innerEnum": {
                         "enum": ["a", "b", "c"],  # This should be kept
-                        "type": "string"
+                        "type": "string",
                     },
                     "innerNonStringEnum": {
                         "enum": [1, 2, 3],  # This should be removed
-                        "type": "integer"
-                    }
-                }
+                        "type": "integer",
+                    },
+                },
             },
             "anyOfField": {
                 "anyOf": [
-                    {"type": "string", "enum": ["option1", "option2"]},  # This should be kept
-                    {"type": "integer", "enum": [1, 2, 3]}  # This should be removed
+                    {
+                        "type": "string",
+                        "enum": ["option1", "option2"],
+                    },  # This should be kept
+                    {"type": "integer", "enum": [1, 2, 3]},  # This should be removed
                 ]
-            }
-        }
+            },
+        },
     }
 
     # Expected output: Non-string enums removed, string enums kept
@@ -919,31 +933,32 @@ def test_fix_enum_types():
             },
             "maxLength": {  # enum removed
                 "type": "integer",
-                "description": "Maximum length"
+                "description": "Maximum length",
             },
             "enabled": {  # enum removed
                 "type": "boolean",
-                "description": "Whether feature is enabled"
+                "description": "Whether feature is enabled",
             },
             "nested": {
                 "type": "object",
                 "properties": {
                     "innerEnum": {
                         "enum": ["a", "b", "c"],  # Kept - string type
-                        "type": "string"
+                        "type": "string",
                     },
-                    "innerNonStringEnum": {  # enum removed
-                        "type": "integer"
-                    }
-                }
+                    "innerNonStringEnum": {"type": "integer"},  # enum removed
+                },
             },
             "anyOfField": {
                 "anyOf": [
-                    {"type": "string", "enum": ["option1", "option2"]},  # Kept - has string type
-                    {"type": "integer"}  # enum removed
+                    {
+                        "type": "string",
+                        "enum": ["option1", "option2"],
+                    },  # Kept - has string type
+                    {"type": "integer"},  # enum removed
                 ]
-            }
-        }
+            },
+        },
     }
 
     # Apply the transformation
@@ -955,15 +970,27 @@ def test_fix_enum_types():
     # Verify specific transformations:
     # 1. String enums are preserved
     assert "enum" in input_schema["properties"]["truncateMode"]
-    assert input_schema["properties"]["truncateMode"]["enum"] == ["auto", "none", "start", "end"]
-    
+    assert input_schema["properties"]["truncateMode"]["enum"] == [
+        "auto",
+        "none",
+        "start",
+        "end",
+    ]
+
     assert "enum" in input_schema["properties"]["nested"]["properties"]["innerEnum"]
-    assert input_schema["properties"]["nested"]["properties"]["innerEnum"]["enum"] == ["a", "b", "c"]
+    assert input_schema["properties"]["nested"]["properties"]["innerEnum"]["enum"] == [
+        "a",
+        "b",
+        "c",
+    ]
 
     # 2. Non-string enums are removed
     assert "enum" not in input_schema["properties"]["maxLength"]
     assert "enum" not in input_schema["properties"]["enabled"]
-    assert "enum" not in input_schema["properties"]["nested"]["properties"]["innerNonStringEnum"]
+    assert (
+        "enum"
+        not in input_schema["properties"]["nested"]["properties"]["innerNonStringEnum"]
+    )
 
     # 3. anyOf with string type keeps enum, non-string removes it
     assert "enum" in input_schema["properties"]["anyOfField"]["anyOf"][0]
@@ -1003,8 +1030,6 @@ def test_get_token_url():
 
     print("url=", url)
 
-
-
     should_use_v1beta1_features = vertex_llm.is_using_v1beta1_features(
         optional_params={"temperature": 0.1}
     )
@@ -1210,9 +1235,7 @@ def test_vertex_ai_minimax_uses_openai_handler():
         VertexAIPartnerModels,
     )
 
-    assert VertexAIPartnerModels.should_use_openai_handler(
-        "minimaxai/minimax-m2-maas"
-    )
+    assert VertexAIPartnerModels.should_use_openai_handler("minimaxai/minimax-m2-maas")
 
 
 def test_vertex_ai_moonshot_uses_openai_handler():
@@ -1236,9 +1259,7 @@ def test_vertex_ai_zai_uses_openai_handler():
         VertexAIPartnerModels,
     )
 
-    assert VertexAIPartnerModels.should_use_openai_handler(
-        "zai-org/glm-4.7-maas"
-    )
+    assert VertexAIPartnerModels.should_use_openai_handler("zai-org/glm-4.7-maas")
 
 
 def test_vertex_ai_zai_is_partner_model():
@@ -1255,14 +1276,14 @@ def test_vertex_ai_zai_is_partner_model():
 def test_build_vertex_schema_empty_properties():
     """
     Test _build_vertex_schema handles empty properties objects correctly.
-    
-    This test verifies the fix for the issue where Gemini rejects schemas 
+
+    This test verifies the fix for the issue where Gemini rejects schemas
     with empty properties objects like {"properties": {}, "type": "object"}.
-    
+
     Error from Gemini: "GenerateContentRequest.generation_config.response_schema
-    .properties[\"action\"].items.any_of[0].properties[\"go_back\"].properties: 
+    .properties[\"action\"].items.any_of[0].properties[\"go_back\"].properties:
     should be non-empty for OBJECT type"
-    
+
     The fix removes empty properties objects and their associated type/required fields.
     """
     from litellm.llms.vertex_ai.common_utils import _build_vertex_schema
@@ -1281,20 +1302,20 @@ def test_build_vertex_schema_empty_properties():
                                     "type": "object",
                                     "additionalProperties": False,
                                     "description": "Go back",
-                                    "required": []
+                                    "required": [],
                                 }
                             },
                             "required": ["go_back"],
                             "type": "object",
-                            "additionalProperties": False
+                            "additionalProperties": False,
                         }
                     ]
                 },
-                "type": "array"
+                "type": "array",
             }
         },
         "type": "object",
-        "additionalProperties": False
+        "additionalProperties": False,
     }
 
     # Apply the transformation
@@ -1302,24 +1323,36 @@ def test_build_vertex_schema_empty_properties():
 
     # Verify the transformation removed empty properties
     # Navigate to the go_back schema
-    go_back_schema = result["properties"]["action"]["items"]["anyOf"][0]["properties"]["go_back"]
-    
+    go_back_schema = result["properties"]["action"]["items"]["anyOf"][0]["properties"][
+        "go_back"
+    ]
+
     # Verify empty properties was removed
     assert "properties" not in go_back_schema, "Empty properties should be removed"
-    
+
     # Verify type is kept as object (Gemini requires type: object even without properties)
-    assert go_back_schema.get("type") == "object", "Type should be kept as object when properties is empty"
-    
+    assert (
+        go_back_schema.get("type") == "object"
+    ), "Type should be kept as object when properties is empty"
+
     # Verify required was also removed
-    assert "required" not in go_back_schema, "Required should be removed when properties is empty"
-    
+    assert (
+        "required" not in go_back_schema
+    ), "Required should be removed when properties is empty"
+
     # Verify description is preserved
-    assert go_back_schema.get("description") == "Go back", "Description should be preserved"
-    
+    assert (
+        go_back_schema.get("description") == "Go back"
+    ), "Description should be preserved"
+
     # Verify parent schema still has proper structure
     parent_schema = result["properties"]["action"]["items"]["anyOf"][0]
-    assert parent_schema["type"] == "object", "Parent schema should still have object type"
-    assert "go_back" in parent_schema["properties"], "go_back should still be in parent properties"
+    assert (
+        parent_schema["type"] == "object"
+    ), "Parent schema should still have object type"
+    assert (
+        "go_back" in parent_schema["properties"]
+    ), "go_back should still be in parent properties"
 
 
 def test_add_object_type_schema_with_no_properties_and_no_type():
@@ -1330,9 +1363,7 @@ def test_add_object_type_schema_with_no_properties_and_no_type():
     from litellm.llms.vertex_ai.common_utils import add_object_type
 
     # Input: Schema with no properties and no type (the problematic case)
-    input_schema = {
-        "$schema": "https://json-schema.org/draft/2020-12/schema"
-    }
+    input_schema = {"$schema": "https://json-schema.org/draft/2020-12/schema"}
 
     # Apply the transformation
     add_object_type(input_schema)
@@ -1351,10 +1382,7 @@ def test_add_object_type_does_not_override_existing_type():
     from litellm.llms.vertex_ai.common_utils import add_object_type
 
     # Input: Schema with existing type
-    input_schema = {
-        "type": "string",
-        "description": "A string field"
-    }
+    input_schema = {"type": "string", "description": "A string field"}
 
     # Apply the transformation
     add_object_type(input_schema)
@@ -1370,15 +1398,42 @@ def test_add_object_type_does_not_add_type_when_anyof_present():
     from litellm.llms.vertex_ai.common_utils import add_object_type
 
     # Input: Schema with anyOf but no type
-    input_schema = {
-        "anyOf": [
-            {"type": "string"},
-            {"type": "null"}
-        ]
-    }
+    input_schema = {"anyOf": [{"type": "string"}, {"type": "null"}]}
 
     # Apply the transformation
     add_object_type(input_schema)
 
     # Verify type was not added (anyOf handles the type)
     assert "type" not in input_schema, "type should not be added when anyOf is present"
+
+
+def test_vertex_request_labels_from_litellm_params_extracts_requester_metadata():
+    assert vertex_request_labels_from_litellm_params(None) is None
+    assert vertex_request_labels_from_litellm_params({}) is None
+    assert vertex_request_labels_from_litellm_params({"metadata": None}) is None
+    lp = {"metadata": {"requester_metadata": {"team": "analytics", "count": 3}}}
+    assert vertex_request_labels_from_litellm_params(lp) == {"team": "analytics"}
+
+
+def test_pop_vertex_request_labels_prefers_explicit_labels_then_metadata():
+    optional = {"labels": {"env": "prod"}}
+    litellm_params = {"metadata": {"requester_metadata": {"team": "x"}}}
+    assert pop_vertex_request_labels(optional, litellm_params) == {"env": "prod"}
+    assert "labels" not in optional
+
+    optional2: dict = {}
+    assert pop_vertex_request_labels(optional2, litellm_params) == {"team": "x"}
+
+
+def test_vertex_text_embedding_request_includes_labels_from_metadata():
+    import litellm
+
+    req = litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
+        input="hi",
+        optional_params={},
+        model="text-embedding-004",
+        litellm_params={
+            "metadata": {"requester_metadata": {"project_id": "cost-center-1"}}
+        },
+    )
+    assert req.get("labels") == {"project_id": "cost-center-1"}