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⚡ 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

litellm_settings:
set_verbose: False # Switch off Debug Logging, ensure your logs do not have any debugging on

Set slack webhook url in your env

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

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.

Extras

Expected Performance in Production

1 LiteLLM Uvicorn Worker on Kubernetes

DescriptionValue
Avg latency50ms
Median latency51ms
/chat/completions Requests/second35
/chat/completions Requests/minute2100
/chat/completions Requests/hour126K

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

ServiceSpecCPUsMemoryArchitectureVersion
Servert2.small.1vCPUs8GBx86
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