📈 Prometheus metrics [BETA]

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

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"
litellm_llm_api_failed_requests_metric	Number of failed LLM API requests per "user", "key", "model", "team", "end-user"

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)

✨ (Enterprise) LLM Remaining Requests and Remaining Tokens

Set this on your config.yaml to allow you to track how close you are to hitting your TPM / RPM limits on each model group

litellm_settings:
  success_callback: ["prometheus"]
  failure_callback: ["prometheus"]
  return_response_headers: true # ensures the LLM API calls track the response headers

Metric Name	Description
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

Example Metric

Remaining Requests

litellm_remaining_requests
{
  api_base="https://api.openai.com/v1",
  api_provider="openai",
  litellm_model_name="gpt-3.5-turbo",
  model_group="gpt-3.5-turbo"
} 
8998.0

Remaining Tokens

litellm_remaining_tokens
{
  api_base="https://api.openai.com/v1",
  api_provider="openai",
  litellm_model_name="gpt-3.5-turbo",
  model_group="gpt-3.5-turbo"
} 
999981.0

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