⚡ Best Practices for Production

1. Use this config.yaml

Use this config.yaml in production (with your own LLMs)

model_list:
  - model_name: fake-openai-endpoint
    litellm_params:
      model: openai/fake
      api_key: fake-key
      api_base: https://exampleopenaiendpoint-production.up.railway.app/

general_settings:
  master_key: sk-1234      # enter your own master key, ensure it starts with 'sk-'
  alerting: ["slack"]      # Setup slack alerting - get alerts on LLM exceptions, Budget Alerts, Slow LLM Responses
  proxy_batch_write_at: 60 # Batch write spend updates every 60s
  database_connection_pool_limit: 10 # limit the number of database connections to = MAX Number of DB Connections/Number of instances of litellm proxy (Around 10-20 is good number)

litellm_settings:
  request_timeout: 600    # raise Timeout error if call takes longer than 600 seconds. Default value is 6000seconds if not set
  set_verbose: False      # Switch off Debug Logging, ensure your logs do not have any debugging on
  json_logs: true         # Get debug logs in json format

Set slack webhook url in your env

export SLACK_WEBHOOK_URL="https://hooks.slack.com/services/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"

Turn off FASTAPI's default info logs

export LITELLM_LOG="ERROR"

info

Need Help or want dedicated support ? Talk to a founder [here]: (https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)

2. On Kubernetes - Use 1 Uvicorn worker [Suggested CMD]

Use this Docker CMD. This will start the proxy with 1 Uvicorn Async Worker

(Ensure that you're not setting run_gunicorn or num_workers in the CMD).

CMD ["--port", "4000", "--config", "./proxy_server_config.yaml"]

3. Use Redis 'port','host', 'password'. NOT 'redis_url'

If you decide to use Redis, DO NOT use 'redis_url'. We recommend usig redis port, host, and password params.

redis_urlis 80 RPS slower

This is still something we're investigating. Keep track of it here

Recommended to do this for prod:

router_settings:
  routing_strategy: usage-based-routing-v2 
  # redis_url: "os.environ/REDIS_URL"
  redis_host: os.environ/REDIS_HOST
  redis_port: os.environ/REDIS_PORT
  redis_password: os.environ/REDIS_PASSWORD

litellm_settings:
  cache: True
  cache_params:
    type: redis
    host: os.environ/REDIS_HOST
    port: os.environ/REDIS_PORT
    password: os.environ/REDIS_PASSWORD

4. Disable 'load_dotenv'

Set export LITELLM_MODE="PRODUCTION"

This disables the load_dotenv() functionality, which will automatically load your environment credentials from the local .env.

5. Set LiteLLM Salt Key

If you plan on using the DB, set a salt key for encrypting/decrypting variables in the DB.

Do not change this after adding a model. It is used to encrypt / decrypt your LLM API Key credentials

We recommned - https://1password.com/password-generator/ password generator to get a random hash for litellm salt key.

export LITELLM_SALT_KEY="sk-1234"

See Code

Extras

Expected Performance in Production

1 LiteLLM Uvicorn Worker on Kubernetes

Description	Value
Avg latency	50ms
Median latency	51ms
/chat/completions Requests/second	100
/chat/completions Requests/minute	6000
/chat/completions Requests/hour	360K

Verifying Debugging logs are off

You should only see the following level of details in logs on the proxy server

# INFO:     192.168.2.205:11774 - "POST /chat/completions HTTP/1.1" 200 OK
# INFO:     192.168.2.205:34717 - "POST /chat/completions HTTP/1.1" 200 OK
# INFO:     192.168.2.205:29734 - "POST /chat/completions HTTP/1.1" 200 OK

Machine Specifications to Deploy LiteLLM

Service	Spec	CPUs	Memory	Architecture	Version
Server	t2.small.	1vCPUs	8GB	x86
Redis Cache	-	-	-	-	7.0+ Redis Engine

Reference Kubernetes Deployment YAML

Reference Kubernetes deployment.yaml that was load tested by us

apiVersion: apps/v1
kind: Deployment
metadata:
  name: litellm-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: litellm
  template:
    metadata:
      labels:
        app: litellm
    spec:
      containers:
        - name: litellm-container
          image: ghcr.io/berriai/litellm:main-latest
          imagePullPolicy: Always
          env:
            - name: AZURE_API_KEY
              value: "d6******"
            - name: AZURE_API_BASE
              value: "https://ope******"
            - name: LITELLM_MASTER_KEY
              value: "sk-1234"
            - name: DATABASE_URL
              value: "po**********"
          args:
            - "--config"
            - "/app/proxy_config.yaml"  # Update the path to mount the config file
          volumeMounts:                 # Define volume mount for proxy_config.yaml
            - name: config-volume
              mountPath: /app
              readOnly: true
          livenessProbe:
            httpGet:
              path: /health/liveliness
              port: 4000
            initialDelaySeconds: 120
            periodSeconds: 15
            successThreshold: 1
            failureThreshold: 3
            timeoutSeconds: 10
          readinessProbe:
            httpGet:
              path: /health/readiness
              port: 4000
            initialDelaySeconds: 120
            periodSeconds: 15
            successThreshold: 1
            failureThreshold: 3
            timeoutSeconds: 10
      volumes:  # Define volume to mount proxy_config.yaml
        - name: config-volume
          configMap:
            name: litellm-config  

Reference Kubernetes service.yaml that was load tested by us

apiVersion: v1
kind: Service
metadata:
  name: litellm-service
spec:
  selector:
    app: litellm
  ports:
    - protocol: TCP
      port: 4000
      targetPort: 4000
  type: LoadBalancer