# used for /metrics endpoint on LiteLLM Proxy #### What this does #### # On success, log events to Prometheus import sys from datetime import datetime, timedelta from typing import Optional from litellm._logging import print_verbose, verbose_logger from litellm.integrations.custom_logger import CustomLogger from litellm.proxy._types import UserAPIKeyAuth from litellm.types.integrations.prometheus import * from litellm.types.utils import StandardLoggingPayload from litellm.utils import get_end_user_id_for_cost_tracking class PrometheusLogger(CustomLogger): # Class variables or attributes def __init__( self, **kwargs, ): try: from prometheus_client import Counter, Gauge, Histogram from litellm.proxy.proxy_server import CommonProxyErrors, premium_user if premium_user is not True: verbose_logger.warning( f"🚨🚨🚨 Prometheus Metrics is on LiteLLM Enterprise\n🚨 {CommonProxyErrors.not_premium_user.value}" ) self.litellm_not_a_premium_user_metric = Counter( name="litellm_not_a_premium_user_metric", documentation=f"🚨🚨🚨 Prometheus Metrics is on LiteLLM Enterprise. 🚨 {CommonProxyErrors.not_premium_user.value}", ) return self.litellm_proxy_failed_requests_metric = Counter( 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=[ "end_user", "hashed_api_key", "api_key_alias", REQUESTED_MODEL, "team", "team_alias", "user", ] + EXCEPTION_LABELS, ) self.litellm_proxy_total_requests_metric = Counter( name="litellm_proxy_total_requests_metric", documentation="Total number of requests made to the proxy server - track number of client side requests", labelnames=[ "end_user", "hashed_api_key", "api_key_alias", REQUESTED_MODEL, "team", "team_alias", "user", STATUS_CODE, ], ) # request latency metrics self.litellm_request_total_latency_metric = Histogram( "litellm_request_total_latency_metric", "Total latency (seconds) for a request to LiteLLM", labelnames=[ "model", "hashed_api_key", "api_key_alias", "team", "team_alias", ], buckets=LATENCY_BUCKETS, ) self.litellm_llm_api_latency_metric = Histogram( "litellm_llm_api_latency_metric", "Total latency (seconds) for a models LLM API call", labelnames=[ "model", "hashed_api_key", "api_key_alias", "team", "team_alias", ], buckets=LATENCY_BUCKETS, ) self.litellm_llm_api_time_to_first_token_metric = Histogram( "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", ], buckets=LATENCY_BUCKETS, ) # Counter for spend self.litellm_spend_metric = Counter( "litellm_spend_metric", "Total spend on LLM requests", labelnames=[ "end_user", "hashed_api_key", "api_key_alias", "model", "team", "team_alias", "user", ], ) # Counter for total_output_tokens self.litellm_tokens_metric = Counter( "litellm_total_tokens", "Total number of input + output tokens from LLM requests", labelnames=[ "end_user", "hashed_api_key", "api_key_alias", "model", "team", "team_alias", "user", ], ) self.litellm_input_tokens_metric = Counter( "litellm_input_tokens", "Total number of input tokens from LLM requests", labelnames=[ "end_user", "hashed_api_key", "api_key_alias", "model", "team", "team_alias", "user", ], ) self.litellm_output_tokens_metric = Counter( "litellm_output_tokens", "Total number of output tokens from LLM requests", labelnames=[ "end_user", "hashed_api_key", "api_key_alias", "model", "team", "team_alias", "user", ], ) # Remaining Budget for Team self.litellm_remaining_team_budget_metric = Gauge( "litellm_remaining_team_budget_metric", "Remaining budget for team", labelnames=["team_id", "team_alias"], ) # Remaining Budget for API Key self.litellm_remaining_api_key_budget_metric = Gauge( "litellm_remaining_api_key_budget_metric", "Remaining budget for api key", labelnames=["hashed_api_key", "api_key_alias"], ) ######################################## # LiteLLM Virtual API KEY metrics ######################################## # Remaining MODEL RPM limit for API Key self.litellm_remaining_api_key_requests_for_model = Gauge( "litellm_remaining_api_key_requests_for_model", "Remaining Requests API Key can make for model (model based rpm limit on key)", labelnames=["hashed_api_key", "api_key_alias", "model"], ) # Remaining MODEL TPM limit for API Key self.litellm_remaining_api_key_tokens_for_model = Gauge( "litellm_remaining_api_key_tokens_for_model", "Remaining Tokens API Key can make for model (model based tpm limit on key)", labelnames=["hashed_api_key", "api_key_alias", "model"], ) ######################################## # LLM API Deployment Metrics / analytics ######################################## # Remaining Rate Limit for model self.litellm_remaining_requests_metric = Gauge( "litellm_remaining_requests", "LLM Deployment Analytics - remaining requests for model, returned from LLM API Provider", labelnames=[ "model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias", ], ) self.litellm_remaining_tokens_metric = Gauge( "litellm_remaining_tokens", "remaining tokens for model, returned from LLM API Provider", labelnames=[ "model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias", ], ) # llm api provider budget metrics self.litellm_provider_remaining_budget_metric = Gauge( "litellm_provider_remaining_budget_metric", "Remaining budget for provider - used when you set provider budget limits", labelnames=["api_provider"], ) # Get all keys _logged_llm_labels = [ "litellm_model_name", "model_id", "api_base", "api_provider", ] team_and_key_labels = [ "hashed_api_key", "api_key_alias", "team", "team_alias", ] # Metric for deployment state self.litellm_deployment_state = Gauge( "litellm_deployment_state", "LLM Deployment Analytics - The state of the deployment: 0 = healthy, 1 = partial outage, 2 = complete outage", labelnames=_logged_llm_labels, ) self.litellm_deployment_cooled_down = Counter( "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], ) self.litellm_deployment_success_responses = Counter( name="litellm_deployment_success_responses", documentation="LLM Deployment Analytics - Total number of successful LLM API calls via litellm", labelnames=[REQUESTED_MODEL] + _logged_llm_labels + team_and_key_labels, ) self.litellm_deployment_failure_responses = Counter( 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=[REQUESTED_MODEL] + _logged_llm_labels + EXCEPTION_LABELS + team_and_key_labels, ) self.litellm_deployment_total_requests = Counter( name="litellm_deployment_total_requests", documentation="LLM Deployment Analytics - Total number of LLM API calls via litellm - success + failure", labelnames=[REQUESTED_MODEL] + _logged_llm_labels + team_and_key_labels, ) # Deployment Latency tracking team_and_key_labels = [ "hashed_api_key", "api_key_alias", "team", "team_alias", ] self.litellm_deployment_latency_per_output_token = Histogram( name="litellm_deployment_latency_per_output_token", documentation="LLM Deployment Analytics - Latency per output token", labelnames=_logged_llm_labels + team_and_key_labels, ) self.litellm_deployment_successful_fallbacks = Counter( "litellm_deployment_successful_fallbacks", "LLM Deployment Analytics - Number of successful fallback requests from primary model -> fallback model", [REQUESTED_MODEL, "fallback_model"] + team_and_key_labels + EXCEPTION_LABELS, ) self.litellm_deployment_failed_fallbacks = Counter( "litellm_deployment_failed_fallbacks", "LLM Deployment Analytics - Number of failed fallback requests from primary model -> fallback model", [REQUESTED_MODEL, "fallback_model"] + team_and_key_labels + EXCEPTION_LABELS, ) self.litellm_llm_api_failed_requests_metric = Counter( name="litellm_llm_api_failed_requests_metric", documentation="deprecated - use litellm_proxy_failed_requests_metric", labelnames=[ "end_user", "hashed_api_key", "api_key_alias", "model", "team", "team_alias", "user", ], ) self.litellm_requests_metric = Counter( 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=[ "end_user", "hashed_api_key", "api_key_alias", "model", "team", "team_alias", "user", ], ) except Exception as e: print_verbose(f"Got exception on init prometheus client {str(e)}") raise e async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): # Define prometheus client from litellm.types.utils import StandardLoggingPayload verbose_logger.debug( f"prometheus Logging - Enters success logging function for kwargs {kwargs}" ) # unpack kwargs standard_logging_payload: Optional[StandardLoggingPayload] = 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}" ) model = kwargs.get("model", "") litellm_params = kwargs.get("litellm_params", {}) or {} _metadata = litellm_params.get("metadata", {}) end_user_id = get_end_user_id_for_cost_tracking( litellm_params, service_type="prometheus" ) user_id = standard_logging_payload["metadata"]["user_api_key_user_id"] user_api_key = standard_logging_payload["metadata"]["user_api_key_hash"] user_api_key_alias = standard_logging_payload["metadata"]["user_api_key_alias"] user_api_team = standard_logging_payload["metadata"]["user_api_key_team_id"] user_api_team_alias = standard_logging_payload["metadata"][ "user_api_key_team_alias" ] output_tokens = standard_logging_payload["completion_tokens"] tokens_used = standard_logging_payload["total_tokens"] response_cost = standard_logging_payload["response_cost"] 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}" ) 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) # 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, ) # 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, # type: ignore 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, ) # remaining budget metrics 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, ) # 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, ) # 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 standard_logging_payload=standard_logging_payload, # type: ignore ) # set x-ratelimit headers self.set_llm_deployment_success_metrics( kwargs, start_time, end_time, output_tokens ) pass def _increment_token_metrics( self, standard_logging_payload: StandardLoggingPayload, end_user_id: Optional[str], user_api_key: Optional[str], user_api_key_alias: Optional[str], model: Optional[str], user_api_team: Optional[str], user_api_team_alias: Optional[str], user_id: Optional[str], ): # token metrics self.litellm_tokens_metric.labels( end_user_id, user_api_key, user_api_key_alias, model, user_api_team, user_api_team_alias, user_id, ).inc(standard_logging_payload["total_tokens"]) self.litellm_input_tokens_metric.labels( end_user_id, user_api_key, user_api_key_alias, model, user_api_team, user_api_team_alias, user_id, ).inc(standard_logging_payload["prompt_tokens"]) self.litellm_output_tokens_metric.labels( end_user_id, user_api_key, user_api_key_alias, model, user_api_team, user_api_team_alias, user_id, ).inc(standard_logging_payload["completion_tokens"]) def _increment_remaining_budget_metrics( self, user_api_team: Optional[str], user_api_team_alias: Optional[str], user_api_key: Optional[str], user_api_key_alias: Optional[str], litellm_params: dict, ): _team_spend = litellm_params.get("metadata", {}).get( "user_api_key_team_spend", None ) _team_max_budget = litellm_params.get("metadata", {}).get( "user_api_key_team_max_budget", None ) _remaining_team_budget = self._safe_get_remaining_budget( max_budget=_team_max_budget, spend=_team_spend ) _api_key_spend = litellm_params.get("metadata", {}).get( "user_api_key_spend", None ) _api_key_max_budget = litellm_params.get("metadata", {}).get( "user_api_key_max_budget", None ) _remaining_api_key_budget = self._safe_get_remaining_budget( max_budget=_api_key_max_budget, spend=_api_key_spend ) # Remaining Budget Metrics self.litellm_remaining_team_budget_metric.labels( user_api_team, user_api_team_alias ).set(_remaining_team_budget) self.litellm_remaining_api_key_budget_metric.labels( user_api_key, user_api_key_alias ).set(_remaining_api_key_budget) def _increment_top_level_request_and_spend_metrics( self, end_user_id: Optional[str], user_api_key: Optional[str], user_api_key_alias: Optional[str], model: Optional[str], user_api_team: Optional[str], user_api_team_alias: Optional[str], user_id: Optional[str], response_cost: float, ): self.litellm_requests_metric.labels( end_user_id, user_api_key, user_api_key_alias, model, user_api_team, user_api_team_alias, user_id, ).inc() self.litellm_spend_metric.labels( end_user_id, user_api_key, user_api_key_alias, model, user_api_team, user_api_team_alias, user_id, ).inc(response_cost) def _set_virtual_key_rate_limit_metrics( self, user_api_key: Optional[str], user_api_key_alias: Optional[str], kwargs: dict, metadata: dict, ): 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 = get_model_group_from_litellm_kwargs(kwargs) remaining_requests_variable_name = ( f"litellm-key-remaining-requests-{model_group}" ) remaining_tokens_variable_name = f"litellm-key-remaining-tokens-{model_group}" remaining_requests = metadata.get(remaining_requests_variable_name, sys.maxsize) remaining_tokens = metadata.get(remaining_tokens_variable_name, sys.maxsize) self.litellm_remaining_api_key_requests_for_model.labels( user_api_key, user_api_key_alias, model_group ).set(remaining_requests) self.litellm_remaining_api_key_tokens_for_model.labels( user_api_key, user_api_key_alias, model_group ).set(remaining_tokens) def _set_latency_metrics( self, kwargs: dict, model: Optional[str], user_api_key: Optional[str], user_api_key_alias: Optional[str], user_api_team: Optional[str], user_api_team_alias: Optional[str], standard_logging_payload: StandardLoggingPayload, ): # latency metrics model_parameters: dict = standard_logging_payload["model_parameters"] end_time: datetime = kwargs.get("end_time") or datetime.now() start_time: Optional[datetime] = kwargs.get("start_time") api_call_start_time = kwargs.get("api_call_start_time", None) completion_start_time = kwargs.get("completion_start_time", None) if ( completion_start_time is not None and isinstance(completion_start_time, datetime) and model_parameters.get("stream") is True # only emit for streaming requests ): time_to_first_token_seconds = ( completion_start_time - api_call_start_time ).total_seconds() self.litellm_llm_api_time_to_first_token_metric.labels( model, user_api_key, user_api_key_alias, user_api_team, user_api_team_alias, ).observe(time_to_first_token_seconds) else: verbose_logger.debug( "Time to first token metric not emitted, stream option in model_parameters is not True" ) if api_call_start_time is not None and isinstance( api_call_start_time, datetime ): api_call_total_time: timedelta = end_time - api_call_start_time api_call_total_time_seconds = api_call_total_time.total_seconds() self.litellm_llm_api_latency_metric.labels( model, user_api_key, user_api_key_alias, user_api_team, user_api_team_alias, ).observe(api_call_total_time_seconds) # total request latency if start_time is not None and isinstance(start_time, datetime): total_time: timedelta = end_time - start_time total_time_seconds = total_time.total_seconds() self.litellm_request_total_latency_metric.labels( model, user_api_key, user_api_key_alias, user_api_team, user_api_team_alias, ).observe(total_time_seconds) async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): from litellm.types.utils import StandardLoggingPayload verbose_logger.debug( f"prometheus Logging - Enters failure logging function for kwargs {kwargs}" ) # unpack kwargs model = kwargs.get("model", "") standard_logging_payload: StandardLoggingPayload = kwargs.get( "standard_logging_object", {} ) litellm_params = kwargs.get("litellm_params", {}) or {} end_user_id = get_end_user_id_for_cost_tracking( litellm_params, service_type="prometheus" ) user_id = standard_logging_payload["metadata"]["user_api_key_user_id"] user_api_key = standard_logging_payload["metadata"]["user_api_key_hash"] user_api_key_alias = standard_logging_payload["metadata"]["user_api_key_alias"] user_api_team = standard_logging_payload["metadata"]["user_api_key_team_id"] user_api_team_alias = standard_logging_payload["metadata"][ "user_api_key_team_alias" ] kwargs.get("exception", None) try: self.litellm_llm_api_failed_requests_metric.labels( end_user_id, user_api_key, user_api_key_alias, model, user_api_team, user_api_team_alias, user_id, ).inc() self.set_llm_deployment_failure_metrics(kwargs) except Exception as e: verbose_logger.exception( "prometheus Layer Error(): Exception occured - {}".format(str(e)) ) pass pass async def async_post_call_failure_hook( self, request_data: dict, original_exception: Exception, user_api_key_dict: UserAPIKeyAuth, ): """ Track client side failures Proxy level tracking - failed client side requests labelnames=[ "end_user", "hashed_api_key", "api_key_alias", REQUESTED_MODEL, "team", "team_alias", ] + EXCEPTION_LABELS, """ try: self.litellm_proxy_failed_requests_metric.labels( end_user=user_api_key_dict.end_user_id, hashed_api_key=user_api_key_dict.api_key, api_key_alias=user_api_key_dict.key_alias, requested_model=request_data.get("model", ""), team=user_api_key_dict.team_id, team_alias=user_api_key_dict.team_alias, user=user_api_key_dict.user_id, exception_status=getattr(original_exception, "status_code", None), exception_class=str(original_exception.__class__.__name__), ).inc() self.litellm_proxy_total_requests_metric.labels( end_user=user_api_key_dict.end_user_id, hashed_api_key=user_api_key_dict.api_key, api_key_alias=user_api_key_dict.key_alias, requested_model=request_data.get("model", ""), team=user_api_key_dict.team_id, team_alias=user_api_key_dict.team_alias, user=user_api_key_dict.user_id, status_code=str(getattr(original_exception, "status_code", None)), ).inc() pass except Exception as e: verbose_logger.exception( "prometheus Layer Error(): Exception occured - {}".format(str(e)) ) pass 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 """ try: self.litellm_proxy_total_requests_metric.labels( end_user=user_api_key_dict.end_user_id, hashed_api_key=user_api_key_dict.api_key, api_key_alias=user_api_key_dict.key_alias, requested_model=data.get("model", ""), team=user_api_key_dict.team_id, team_alias=user_api_key_dict.team_alias, user=user_api_key_dict.user_id, status_code="200", ).inc() except Exception as e: verbose_logger.exception( "prometheus Layer Error(): Exception occured - {}".format(str(e)) ) pass 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: StandardLoggingPayload = request_kwargs.get( "standard_logging_object", {} ) _litellm_params = request_kwargs.get("litellm_params", {}) or {} litellm_model_name = request_kwargs.get("model", None) model_group = standard_logging_payload.get("model_group", None) api_base = standard_logging_payload.get("api_base", None) model_id = standard_logging_payload.get("model_id", None) exception: Exception = request_kwargs.get("exception", None) llm_provider = _litellm_params.get("custom_llm_provider", None) """ log these labels ["litellm_model_name", "model_id", "api_base", "api_provider"] """ self.set_deployment_partial_outage( litellm_model_name=litellm_model_name, model_id=model_id, api_base=api_base, api_provider=llm_provider, ) self.litellm_deployment_failure_responses.labels( litellm_model_name=litellm_model_name, model_id=model_id, api_base=api_base, api_provider=llm_provider, exception_status=str(getattr(exception, "status_code", None)), exception_class=exception.__class__.__name__, requested_model=model_group, hashed_api_key=standard_logging_payload["metadata"][ "user_api_key_hash" ], api_key_alias=standard_logging_payload["metadata"][ "user_api_key_alias" ], team=standard_logging_payload["metadata"]["user_api_key_team_id"], team_alias=standard_logging_payload["metadata"][ "user_api_key_team_alias" ], ).inc() self.litellm_deployment_total_requests.labels( litellm_model_name=litellm_model_name, model_id=model_id, api_base=api_base, api_provider=llm_provider, requested_model=model_group, hashed_api_key=standard_logging_payload["metadata"][ "user_api_key_hash" ], api_key_alias=standard_logging_payload["metadata"][ "user_api_key_alias" ], team=standard_logging_payload["metadata"]["user_api_key_team_id"], team_alias=standard_logging_payload["metadata"][ "user_api_key_team_alias" ], ).inc() pass except Exception: pass def set_llm_deployment_success_metrics( self, request_kwargs: dict, start_time, end_time, output_tokens: float = 1.0, ): try: verbose_logger.debug("setting remaining tokens requests metric") standard_logging_payload: Optional[StandardLoggingPayload] = ( request_kwargs.get("standard_logging_object") ) if standard_logging_payload is None: return model_group = standard_logging_payload["model_group"] api_base = standard_logging_payload["api_base"] _response_headers = request_kwargs.get("response_headers") _litellm_params = request_kwargs.get("litellm_params", {}) or {} _metadata = _litellm_params.get("metadata", {}) litellm_model_name = request_kwargs.get("model", None) llm_provider = _litellm_params.get("custom_llm_provider", None) _model_info = _metadata.get("model_info") or {} model_id = _model_info.get("id", None) remaining_requests: Optional[int] = None remaining_tokens: Optional[int] = 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 remaining_requests: """ "model_group", "api_provider", "api_base", "litellm_model_name" """ self.litellm_remaining_requests_metric.labels( model_group, llm_provider, api_base, litellm_model_name, standard_logging_payload["metadata"]["user_api_key_hash"], standard_logging_payload["metadata"]["user_api_key_alias"], ).set(remaining_requests) if remaining_tokens: self.litellm_remaining_tokens_metric.labels( model_group, llm_provider, api_base, litellm_model_name, standard_logging_payload["metadata"]["user_api_key_hash"], standard_logging_payload["metadata"]["user_api_key_alias"], ).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, model_id=model_id, api_base=api_base, api_provider=llm_provider, ) self.litellm_deployment_success_responses.labels( litellm_model_name=litellm_model_name, model_id=model_id, api_base=api_base, api_provider=llm_provider, requested_model=model_group, hashed_api_key=standard_logging_payload["metadata"][ "user_api_key_hash" ], api_key_alias=standard_logging_payload["metadata"][ "user_api_key_alias" ], team=standard_logging_payload["metadata"]["user_api_key_team_id"], team_alias=standard_logging_payload["metadata"][ "user_api_key_team_alias" ], ).inc() self.litellm_deployment_total_requests.labels( litellm_model_name=litellm_model_name, model_id=model_id, api_base=api_base, api_provider=llm_provider, requested_model=model_group, hashed_api_key=standard_logging_payload["metadata"][ "user_api_key_hash" ], api_key_alias=standard_logging_payload["metadata"][ "user_api_key_alias" ], team=standard_logging_payload["metadata"]["user_api_key_team_id"], team_alias=standard_logging_payload["metadata"][ "user_api_key_team_alias" ], ).inc() # Track deployment Latency response_ms: timedelta = end_time - start_time time_to_first_token_response_time: Optional[timedelta] = 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: timedelta = time_to_first_token_response_time or response_ms _latency_seconds = _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 self.litellm_deployment_latency_per_output_token.labels( litellm_model_name=litellm_model_name, model_id=model_id, api_base=api_base, api_provider=llm_provider, hashed_api_key=standard_logging_payload["metadata"][ "user_api_key_hash" ], api_key_alias=standard_logging_payload["metadata"][ "user_api_key_alias" ], team=standard_logging_payload["metadata"]["user_api_key_team_id"], team_alias=standard_logging_payload["metadata"][ "user_api_key_team_alias" ], ).observe(latency_per_token) except Exception as e: verbose_logger.error( "Prometheus Error: set_llm_deployment_success_metrics. Exception occured - {}".format( str(e) ) ) return 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 = kwargs.get("metadata", {}) standard_metadata: StandardLoggingMetadata = ( StandardLoggingPayloadSetup.get_standard_logging_metadata( metadata=_metadata ) ) _new_model = kwargs.get("model") self.litellm_deployment_successful_fallbacks.labels( requested_model=original_model_group, 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=str(original_exception.__class__.__name__), ).inc() 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 = kwargs.get("model") _metadata = kwargs.get("metadata", {}) standard_metadata: StandardLoggingMetadata = ( StandardLoggingPayloadSetup.get_standard_logging_metadata( metadata=_metadata ) ) self.litellm_deployment_failed_fallbacks.labels( requested_model=original_model_group, 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=str(original_exception.__class__.__name__), ).inc() def set_litellm_deployment_state( self, state: int, litellm_model_name: str, model_id: Optional[str], api_base: Optional[str], api_provider: str, ): self.litellm_deployment_state.labels( litellm_model_name, model_id, api_base, api_provider ).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: Optional[str], api_base: Optional[str], 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: Optional[str], api_base: Optional[str], 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( litellm_model_name, model_id, api_base, api_provider, exception_status ).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: Optional[float], spend: Optional[float] ) -> float: if max_budget is None: return float("inf") if spend is None: return max_budget return max_budget - spend