litellm/docs/my-website/docs/proxy/prometheus.md
2024-07-05 14:08:55 -07:00

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import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# 📈 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
```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
```shell
litellm --config config.yaml --debug
```
Test Request
```shell
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`
```shell
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
```yaml
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
<Tabs>
<TabItem value="Remaining Requests" label="Remaining Requests">
```shell
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
```
</TabItem>
<TabItem value="Requests" label="Remaining Tokens">
```shell
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
```
</TabItem>
</Tabs>
## Monitor System Health
To monitor the health of litellm adjacent services (redis / postgres), do:
```yaml
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