feat(prometheus): add service_tier label to latency and spend metrics

litellm/integrations/prometheus.py

@@ -69,6 +69,15 @@
 
 _DEFAULT_BUDGET_METRICS_PER_REQUEST_TIMEOUT = 5.0
 
+# Tiers a caller may name in a request, across the providers that accept the
+# parameter: OpenAI ("auto", "default", "flex", "priority", "scale"), Bedrock and
+# Groq (subsets of those), Anthropic ("auto", "standard_only") and Vertex, which
+# maps "default" to "standard". Used to bound the caller-controlled fallback in
+# ``get_service_tier_from_standard_logging_payload``.
+KNOWN_REQUEST_SERVICE_TIERS = frozenset(
+    {"auto", "batch", "default", "flex", "priority", "scale", "standard", "standard_only"}
+)
+
 
 def _get_budget_metrics_per_request_timeout() -> float:
     raw = os.getenv("PROMETHEUS_BUDGET_METRICS_PER_REQUEST_TIMEOUT")
@@ -1245,6 +1254,7 @@ async def async_log_success_event(self, kwargs, response_obj, start_time, end_ti
             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-"):
@@ -4098,6 +4108,44 @@ def get_custom_labels_from_metadata(metadata: dict) -> Dict[str, str]:
     return result
 
 
+def get_service_tier_from_standard_logging_payload(
+    standard_logging_payload: StandardLoggingPayload,
+) -> str | None:
+    """
+    Resolve the service tier a request ran on, for the ``service_tier`` label.
+
+    The tier the provider actually served wins over the tier the caller asked for,
+    so latency and spend stay segmentable when the request said ``auto`` and the
+    provider picked the concrete tier. Providers report the served tier either at
+    the top level of the response (OpenAI, Bedrock, Groq) or on the usage object
+    (Anthropic).
+
+    Streaming responses carry no served tier, so the requested tier is the
+    fallback. That value is caller-controlled and survives param mapping even
+    where the provider then ignores it (Bedrock and Groq accept the request and
+    drop an unrecognized tier), so it is only labelled when it names a known
+    tier; otherwise one caller could mint a Prometheus series per string. Values
+    the provider itself reports are not caller-controlled and stay unrestricted,
+    so a tier a provider adds later is still labelled correctly.
+    """
+    response = standard_logging_payload.get("response")
+    usage_object = standard_logging_payload.get("metadata", {}).get("usage_object")
+
+    served_candidates: tuple[object, ...] = (
+        response.get("service_tier") if isinstance(response, dict) else None,
+        usage_object.get("service_tier") if isinstance(usage_object, dict) else None,
+    )
+    served_tier = next((tier for tier in served_candidates if isinstance(tier, str) and tier), None)
+    if served_tier is not None:
+        return served_tier
+
+    model_parameters = standard_logging_payload.get("model_parameters")
+    requested_tier = model_parameters.get("service_tier") if isinstance(model_parameters, dict) else None
+    if isinstance(requested_tier, str) and requested_tier in KNOWN_REQUEST_SERVICE_TIERS:
+        return requested_tier
+    return None
+
+
 def _get_combined_custom_metadata_from_standard_logging_payload(
     standard_logging_payload: Optional[dict],
 ) -> Dict[str, Any]:

litellm/types/integrations/prometheus.py

@@ -190,6 +190,7 @@ class UserAPIKeyLabelNames(Enum):
     ORG_ALIAS = "org_alias"
     MCP_TOOL_NAME = "mcp_tool_name"
     MCP_SERVER_NAME = "mcp_server_name"
+    SERVICE_TIER = "service_tier"
 
 
 DEFINED_PROMETHEUS_METRICS = Literal[
@@ -286,6 +287,7 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.USER.value,
         UserAPIKeyLabelNames.MODEL_ID.value,
         UserAPIKeyLabelNames.API_PROVIDER.value,
+        UserAPIKeyLabelNames.SERVICE_TIER.value,
     ]
 
     litellm_llm_api_time_to_first_token_metric = [
@@ -299,6 +301,7 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.USER.value,
         UserAPIKeyLabelNames.MODEL_ID.value,
         UserAPIKeyLabelNames.API_PROVIDER.value,
+        UserAPIKeyLabelNames.SERVICE_TIER.value,
     ]
 
     litellm_request_total_latency_metric = [
@@ -312,6 +315,7 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
         UserAPIKeyLabelNames.MODEL_ID.value,
         UserAPIKeyLabelNames.API_PROVIDER.value,
+        UserAPIKeyLabelNames.SERVICE_TIER.value,
     ]
 
     litellm_request_queue_time_seconds = [
@@ -453,6 +457,7 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.REQUESTED_MODEL.value,
         UserAPIKeyLabelNames.MODEL_ID.value,
         UserAPIKeyLabelNames.API_PROVIDER.value,
+        UserAPIKeyLabelNames.SERVICE_TIER.value,
     ]
 
     litellm_input_tokens_metric = [
@@ -878,6 +883,7 @@ class UserAPIKeyLabelValues:
     org_alias: Optional[str] = None
     mcp_tool_name: Optional[str] = None
     mcp_server_name: Optional[str] = None
+    service_tier: Optional[str] = None
 
     # Added for test compatibility.
     def __init__(self, **kwargs: Any) -> None:

tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py

@@ -428,6 +428,7 @@ def test_set_latency_metrics(prometheus_logger):
         model="gpt-5-mini",
         model_id="model-123",
         api_provider="openai",
+        service_tier=None,
     )
     prometheus_logger.litellm_llm_api_time_to_first_token_metric.labels().observe.assert_called_once_with(
         0.5
@@ -447,6 +448,7 @@ def test_set_latency_metrics(prometheus_logger):
         model="gpt-5-mini",
         model_id="model-123",
         api_provider="openai",
+        service_tier=None,
     )
     prometheus_logger.litellm_llm_api_latency_metric.labels().observe.assert_called_once_with(
         1.5
@@ -466,6 +468,7 @@ def test_set_latency_metrics(prometheus_logger):
         model="gpt-5-mini",
         model_id="model-123",
         api_provider="openai",
+        service_tier=None,
     )
     prometheus_logger.litellm_request_total_latency_metric.labels().observe.assert_called_once_with(
         2.0
@@ -634,6 +637,7 @@ def test_increment_top_level_request_and_spend_metrics(prometheus_logger):
         client_ip=None,
         user_agent=None,
         requested_model=None,
+        service_tier=None,
     )
     prometheus_logger.litellm_spend_metric.labels().inc.assert_called_once_with(0.1)
 

tests/test_litellm/integrations/test_prometheus_service_tier_label.py

@@ -0,0 +1,232 @@
+"""
+Unit tests for the service_tier Prometheus label on latency and spend metrics.
+
+Covers the label being declared on the metrics that carry it, the precedence
+between the tier a provider served and the tier a caller requested, and the
+end-to-end emit wiring through async_log_success_event.
+
+Run with:
+    uv run pytest tests/test_litellm/integrations/test_prometheus_service_tier_label.py -v
+"""
+
+import datetime
+
+import pytest
+
+from litellm.integrations.prometheus import (
+    KNOWN_REQUEST_SERVICE_TIERS,
+    PrometheusLogger,
+    get_service_tier_from_standard_logging_payload,
+)
+from litellm.types.integrations.prometheus import (
+    PrometheusMetricLabels,
+    UserAPIKeyLabelNames,
+    UserAPIKeyLabelValues,
+)
+
+SERVICE_TIER_METRICS = [
+    "litellm_llm_api_latency_metric",
+    "litellm_llm_api_time_to_first_token_metric",
+    "litellm_request_total_latency_metric",
+    "litellm_spend_metric",
+]
+
+
+def _clear_prometheus_registry() -> None:
+    from prometheus_client import REGISTRY
+
+    for collector in list(REGISTRY._collector_to_names.keys()):
+        try:
+            REGISTRY.unregister(collector)
+        except Exception:
+            pass
+
+
+def _collected_samples(metric_name: str):
+    from prometheus_client import REGISTRY
+
+    return [sample for metric in REGISTRY.collect() for sample in metric.samples if sample.name == metric_name]
+
+
+def _standard_logging_payload(
+    response: object = None,
+    usage_object: object = None,
+    model_parameters: object = None,
+) -> dict:
+    return {
+        "id": "t",
+        "call_type": "completion",
+        "response_cost": 0.001,
+        "status": "success",
+        "total_tokens": 30,
+        "prompt_tokens": 20,
+        "completion_tokens": 10,
+        "startTime": 1.0,
+        "endTime": 2.0,
+        "completionStartTime": 1.5,
+        "model": "gpt-4o-mini",
+        "model_id": "model-123",
+        "model_group": "gpt-4o-mini",
+        "api_base": "https://api.openai.com",
+        "custom_llm_provider": "openai",
+        "request_tags": [],
+        "end_user": None,
+        "cache_hit": False,
+        "stream": True,
+        "response": response,
+        "model_parameters": model_parameters,
+        "metadata": {
+            "user_api_key_hash": "h",
+            "user_api_key_alias": "a",
+            "user_api_key_team_id": "t",
+            "user_api_key_team_alias": "ta",
+            "user_api_key_user_id": "u",
+            "user_api_key_user_email": "e@x.com",
+            "user_api_key_org_id": None,
+            "user_api_key_org_alias": None,
+            "requester_metadata": None,
+            "user_api_key_end_user_id": None,
+            "usage_object": usage_object,
+        },
+        "hidden_params": {"litellm_overhead_time_ms": None, "additional_headers": None},
+    }
+
+
+def test_service_tier_label_declared_on_latency_and_spend_metrics():
+    for metric_name in SERVICE_TIER_METRICS:
+        labels = PrometheusMetricLabels.get_labels(metric_name)
+        assert UserAPIKeyLabelNames.SERVICE_TIER.value in labels, f"{metric_name} should carry the service_tier label"
+
+
+def test_user_api_key_label_values_carries_service_tier():
+    values = UserAPIKeyLabelValues(service_tier="flex")
+
+    assert values.service_tier == "flex"
+    assert values.model_dump()["service_tier"] == "flex"
+    assert UserAPIKeyLabelValues().service_tier is None
+
+
+def test_served_tier_wins_over_requested_tier():
+    payload = _standard_logging_payload(
+        response={"service_tier": "default"},
+        model_parameters={"service_tier": "auto"},
+    )
+
+    assert get_service_tier_from_standard_logging_payload(payload) == "default"
+
+
+def test_usage_object_tier_used_when_response_has_none():
+    payload = _standard_logging_payload(
+        response={"id": "chatcmpl-1"},
+        usage_object={"prompt_tokens": 1, "service_tier": "standard"},
+        model_parameters={"service_tier": "auto"},
+    )
+
+    assert get_service_tier_from_standard_logging_payload(payload) == "standard"
+
+
+def test_requested_tier_used_when_no_served_tier():
+    payload = _standard_logging_payload(
+        response={"id": "chatcmpl-1"},
+        usage_object={"prompt_tokens": 1},
+        model_parameters={"service_tier": "flex"},
+    )
+
+    assert get_service_tier_from_standard_logging_payload(payload) == "flex"
+
+
+def test_unrecognized_requested_tier_is_not_labelled():
+    """
+    A caller-supplied tier survives param mapping even where the provider then
+    ignores it (Bedrock and Groq drop an unrecognized tier and still answer), so
+    labelling it verbatim would let one caller mint a series per string.
+    """
+    payload = _standard_logging_payload(model_parameters={"service_tier": "attacker-controlled-a1b2c3"})
+
+    assert get_service_tier_from_standard_logging_payload(payload) is None
+
+
+def test_unrecognized_served_tier_is_labelled():
+    """
+    The tier a provider reports is not caller-controlled, so a tier added by a
+    provider after this release still gets labelled instead of being dropped.
+    """
+    payload = _standard_logging_payload(
+        response={"service_tier": "tier-added-by-provider-later"},
+        model_parameters={"service_tier": "auto"},
+    )
+
+    assert get_service_tier_from_standard_logging_payload(payload) == "tier-added-by-provider-later"
+
+
+@pytest.mark.parametrize("tier", sorted(KNOWN_REQUEST_SERVICE_TIERS))
+def test_every_known_requested_tier_is_labelled(tier):
+    payload = _standard_logging_payload(model_parameters={"service_tier": tier})
+
+    assert get_service_tier_from_standard_logging_payload(payload) == tier
+
+
+@pytest.mark.parametrize(
+    "response, usage_object, model_parameters",
+    [
+        (None, None, None),
+        ({"service_tier": None}, {"service_tier": ""}, {"service_tier": None}),
+        ("redacted-by-litellm", None, {}),
+        ({"service_tier": 1}, None, None),
+    ],
+)
+def test_no_tier_resolves_to_none(response, usage_object, model_parameters):
+    payload = _standard_logging_payload(
+        response=response,
+        usage_object=usage_object,
+        model_parameters=model_parameters,
+    )
+
+    assert get_service_tier_from_standard_logging_payload(payload) is None
+
+
+@pytest.mark.asyncio
+async def test_success_event_emits_service_tier_on_latency_and_spend_metrics():
+    """
+    End-to-end emit wiring.
+
+    Drives the real logger with a payload whose response was served on the flex
+    tier and asserts every latency histogram and the spend counter carries
+    service_tier="flex". Fails if the label is dropped from a metric's label list
+    or if the value is not populated on the success path.
+    """
+    payload = _standard_logging_payload(
+        response={"service_tier": "flex"},
+        model_parameters={"service_tier": "auto"},
+    )
+    now = datetime.datetime.now()
+    kwargs = {
+        "model": "gpt-4o-mini",
+        "litellm_params": {"metadata": {}},
+        "standard_logging_object": payload,
+        "stream": True,
+        "start_time": now - datetime.timedelta(seconds=3),
+        "api_call_start_time": now - datetime.timedelta(seconds=2),
+        "completion_start_time": now - datetime.timedelta(seconds=1),
+        "end_time": now,
+    }
+
+    _clear_prometheus_registry()
+    try:
+        logger = PrometheusLogger()
+        await logger.async_log_success_event(kwargs, None, now, now)
+
+        for metric_name in (
+            "litellm_request_total_latency_metric_bucket",
+            "litellm_llm_api_latency_metric_bucket",
+            "litellm_llm_api_time_to_first_token_metric_bucket",
+            "litellm_spend_metric_total",
+        ):
+            samples = _collected_samples(metric_name)
+            assert samples, f"expected {metric_name} to be emitted"
+            assert all(sample.labels.get("service_tier") == "flex" for sample in samples), (
+                f"{metric_name} must carry service_tier=flex, got "
+                f"{sorted({sample.labels.get('service_tier') for sample in samples})}"
+            )
+    finally:
+        _clear_prometheus_registry()