📈 [BETA] Prometheus metrics

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✨ Prometheus metrics is on LiteLLM Enterprise starting at $250/mo

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LiteLLM Exposes a /metrics endpoint for Prometheus to Poll

Quick Start

If you're using the LiteLLM CLI with litellm --config proxy_config.yaml then you need to pip install prometheus_client==0.20.0. This is already pre-installed on the litellm Docker image

Add this to your proxy config.yaml

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  success_callback: ["prometheus"]
  failure_callback: ["prometheus"]

Start the proxy

litellm --config config.yaml --debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
}'

View Metrics on /metrics, Visit http://localhost:4000/metrics

http://localhost:4000/metrics

# <proxy_base_url>/metrics

📈 Metrics Tracked

Virtual Keys, Teams, Internal Users Metrics

Use this for for tracking per user, key, team, etc.

Metric Name	Description
litellm_requests_metric	Number of requests made, per "user", "key", "model", "team", "end-user"
litellm_spend_metric	Total Spend, per "user", "key", "model", "team", "end-user"
litellm_total_tokens	input + output tokens per "user", "key", "model", "team", "end-user"

Proxy Level Tracking Metrics

Use this to track overall LiteLLM Proxy usage.

• Track Actual traffic rate to proxy
• Number of client side requests and failures for requests made to proxy

Metric Name	Description
litellm_proxy_failed_requests_metric	Total number of failed responses from proxy - the client did not get a success response from litellm proxy "user", "key", "model", "team", "end-user"
litellm_proxy_total_requests_metric	Total number of requests made to the proxy server - track number of client side requests "user", "key", "model", "team", "end-user"

LLM API / Provider Metrics

Use this for LLM API Error monitoring and tracking remaining rate limits and token limits

Labels Tracked for LLM API Metrics

litellm_model_name: The name of the LLM model used by LiteLLM
requested_model: The model sent in the request
model_id: The model_id of the deployment. Autogenerated by LiteLLM, each deployment has a unique model_id
api_base: The API Base of the deployment
api_provider: The LLM API provider, used for the provider. Example (azure, openai, vertex_ai)

Metric Name	Description
litellm_deployment_success_responses	Total number of successful LLM API calls for deployment
litellm_deployment_failure_responses	Total number of failed LLM API calls for a specific LLM deploymeny. exception_status is the status of the exception from the llm api
litellm_deployment_total_requests	Total number of LLM API calls for deployment - success + failure
litellm_remaining_requests_metric	Track x-ratelimit-remaining-requests returned from LLM API Deployment
litellm_remaining_tokens	Track x-ratelimit-remaining-tokens return from LLM API Deployment
litellm_deployment_state	The state of the deployment: 0 = healthy, 1 = partial outage, 2 = complete outage.
litellm_deployment_latency_per_output_token	Latency per output token for deployment

Load Balancing, Fallback, Cooldown Metrics

Use this for tracking litellm router load balancing metrics

Metric Name	Description
litellm_deployment_cooled_down	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
litellm_deployment_successful_fallbacks	Number of successful fallback requests from primary model -> fallback model
litellm_deployment_failed_fallbacks	Number of failed fallback requests from primary model -> fallback model

Request Latency Metrics

Metric Name	Description
litellm_request_total_latency_metric	Total latency (seconds) for a request to LiteLLM Proxy Server - tracked for labels litellm_call_id, model
litellm_llm_api_latency_metric	latency (seconds) for just the LLM API call - tracked for labels litellm_call_id, model

Budget Metrics

Metric Name	Description
litellm_remaining_team_budget_metric	Remaining Budget for Team (A team created on LiteLLM)
litellm_remaining_api_key_budget_metric	Remaining Budget for API Key (A key Created on LiteLLM)

Monitor System Health

To monitor the health of litellm adjacent services (redis / postgres), do:

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  service_callback: ["prometheus_system"]

Metric Name	Description
litellm_redis_latency	histogram latency for redis calls
litellm_redis_fails	Number of failed redis calls
litellm_self_latency	Histogram latency for successful litellm api call

🔥 Community Maintained Grafana Dashboards

Link to Grafana Dashboards made by LiteLLM community

https://github.com/BerriAI/litellm/tree/main/cookbook/litellm_proxy_server/grafana_dashboard