⚡ 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
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"
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_url
is 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
Description | Value |
---|---|
Avg latency | 50ms |
Median latency | 51ms |
/chat/completions Requests/second | 35 |
/chat/completions Requests/minute | 2100 |
/chat/completions Requests/hour | 126K |
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