tiennm99/litellm

Fork 0

mirror of https://github.com/tiennm99/litellm.git synced 2026-07-22 12:20:14 +00:00

Files

T

Harshit Jain 56fab12fbe fix: prompt registry

2026-02-18 00:34:54 +05:30

33 KiB

Raw Blame History

import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import Image from '@theme/IdealImage';

Docker, Helm, Terraform

:::info No Limits on LiteLLM OSS There are no limits on the number of users, keys, or teams you can create on LiteLLM OSS. :::

You can find the Dockerfile to build litellm proxy here

Note: Production requires at least 4 CPU cores and 8 GB RAM.

Quick Start

:::info Facing issues with pulling the docker image? Email us at support@berri.ai. :::

To start using Litellm, run the following commands in a shell:

docker pull docker.litellm.ai/berriai/litellm:main-latest

See all docker images

$ pip install 'litellm[proxy]'

Use this docker compose to spin up the proxy with a postgres database running locally.

# Get the docker compose file
curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/docker-compose.yml
curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/prometheus.yml

# Add the master key - you can change this after setup
echo 'LITELLM_MASTER_KEY="sk-1234"' > .env

# Add the litellm salt key - you cannot change this after adding a model
# It is used to encrypt / decrypt your LLM API Key credentials
# We recommend - https://1password.com/password-generator/ 
# password generator to get a random hash for litellm salt key
echo 'LITELLM_SALT_KEY="sk-1234"' >> .env

# Start
docker compose up

Docker Run

Step 1. CREATE config.yaml

Example litellm_config.yaml

model_list:
  - model_name: azure-gpt-4o
    litellm_params:
      model: azure/<your-azure-model-deployment>
      api_base: os.environ/AZURE_API_BASE # runs os.getenv("AZURE_API_BASE")
      api_key: os.environ/AZURE_API_KEY # runs os.getenv("AZURE_API_KEY")
      api_version: "2025-01-01-preview"

Step 2. RUN Docker Image

docker run \
    -v $(pwd)/litellm_config.yaml:/app/config.yaml \
    -e AZURE_API_KEY=d6*********** \
    -e AZURE_API_BASE=https://openai-***********/ \
    -p 4000:4000 \
    docker.litellm.ai/berriai/litellm:main-stable \
    --config /app/config.yaml --detailed_debug

Get Latest Image 👉 here

Step 3. TEST Request

Pass model=azure-gpt-4o this was set on step 1

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "azure-gpt-4o",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
}'

Docker Run - CLI Args

See all supported CLI args here:

Here's how you can run the docker image and pass your config to litellm

docker run docker.litellm.ai/berriai/litellm:main-stable --config your_config.yaml

Here's how you can run the docker image and start litellm on port 8002 with num_workers=8

docker run docker.litellm.ai/berriai/litellm:main-stable --port 8002 --num_workers 8

Use litellm as a base image

# Use the provided base image
FROM docker.litellm.ai/berriai/litellm:main-stable

# Set the working directory to /app
WORKDIR /app

# Copy the configuration file into the container at /app
COPY config.yaml .

# Make sure your docker/entrypoint.sh is executable
RUN chmod +x ./docker/entrypoint.sh

# Expose the necessary port
EXPOSE 4000/tcp

# Override the CMD instruction with your desired command and arguments
# WARNING: FOR PROD DO NOT USE `--detailed_debug` it slows down response times, instead use the following CMD
# CMD ["--port", "4000", "--config", "config.yaml"]

CMD ["--port", "4000", "--config", "config.yaml", "--detailed_debug"]

Build from litellm `pip` package

Follow these instructions to build a docker container from the litellm pip package. If your company has a strict requirement around security / building images you can follow these steps.

Note: You'll need to copy the schema.prisma file from the litellm repository to your build directory alongside the Dockerfile and requirements.txt.

Dockerfile

FROM cgr.dev/chainguard/python:latest-dev

USER root
WORKDIR /app

ENV HOME=/home/litellm
ENV PATH="${HOME}/venv/bin:$PATH"

# Install runtime dependencies
RUN apk update && \
    apk add --no-cache gcc python3-dev openssl openssl-dev

RUN python -m venv ${HOME}/venv
RUN ${HOME}/venv/bin/pip install --no-cache-dir --upgrade pip

COPY requirements.txt .
RUN --mount=type=cache,target=${HOME}/.cache/pip \
    ${HOME}/venv/bin/pip install -r requirements.txt

# Copy Prisma schema file
COPY schema.prisma .

# Generate prisma client
RUN prisma generate

EXPOSE 4000/tcp

ENTRYPOINT ["litellm"]
CMD ["--port", "4000"]

Example requirements.txt

litellm[proxy]==1.57.3 # Specify the litellm version you want to use
litellm-enterprise
prometheus_client
langfuse
prisma

Build the docker image

docker build \
  -f Dockerfile.build_from_pip \
  -t litellm-proxy-with-pip-5 .

Run the docker image

docker run \
    -v $(pwd)/litellm_config.yaml:/app/config.yaml \
    -e OPENAI_API_KEY="sk-1222" \
    -e DATABASE_URL="postgresql://xxxxxxxxx \
    -p 4000:4000 \
    litellm-proxy-with-pip-5 \
    --config /app/config.yaml --detailed_debug

Terraform

s/o Nicholas Cecere for his LiteLLM User Management Terraform

👉 Go here for Terraform

Kubernetes

Deploying a config file based litellm instance just requires a simple deployment that loads the config.yaml file via a config map. Also it would be a good practice to use the env var declaration for api keys, and attach the env vars with the api key values as an opaque secret.

apiVersion: v1
kind: ConfigMap
metadata:
  name: litellm-config-file
data:
  config.yaml: |
      model_list: 
        - model_name: gpt-4o
          litellm_params:
            model: azure/gpt-4o-ca
            api_base: https://my-endpoint-canada-berri992.openai.azure.com/
            api_key: os.environ/CA_AZURE_OPENAI_API_KEY
---
apiVersion: v1
kind: Secret
type: Opaque
metadata:
  name: litellm-secrets
data:
  CA_AZURE_OPENAI_API_KEY: bWVvd19pbV9hX2NhdA== # your api key in base64
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: litellm-deployment
  labels:
    app: litellm
spec:
  selector:
    matchLabels:
      app: litellm
  template:
    metadata:
      labels:
        app: litellm
    spec:
      containers:
      - name: litellm
        image: docker.litellm.ai/berriai/litellm:main-stable # it is recommended to fix a version generally
        args:
          - "--config"
          - "/app/proxy_server_config.yaml"
        ports:
        - containerPort: 4000
        volumeMounts:
        - name: config-volume
          mountPath: /app/proxy_server_config.yaml
          subPath: config.yaml
        envFrom:
        - secretRef:
            name: litellm-secrets
      volumes:
        - name: config-volume
          configMap:
            name: litellm-config-file

:::info To avoid issues with predictability, difficulties in rollback, and inconsistent environments, use versioning or SHA digests (for example, litellm:main-v1.30.3 or litellm@sha256:12345abcdef...) instead of litellm:main-stable. :::

Helm Chart

:::info

[BETA] Helm Chart is BETA. If you run into an issues/have feedback please let us know https://github.com/BerriAI/litellm/issues

:::

Use this when you want to use litellm helm chart as a dependency for other charts. The litellm-helm OCI is hosted here https://github.com/BerriAI/litellm/pkgs/container/litellm-helm

Step 1. Pull the litellm helm chart

helm pull oci://docker.litellm.ai/berriai/litellm-helm

# Pulled: docker.litellm.ai/berriai/litellm-helm:0.1.2
# Digest: sha256:7d3ded1c99c1597f9ad4dc49d84327cf1db6e0faa0eeea0c614be5526ae94e2a

Step 2. Unzip litellm helm

Unzip the specific version that was pulled in Step 1

tar -zxvf litellm-helm-0.1.2.tgz

Step 3. Install litellm helm

helm install lite-helm ./litellm-helm

Step 4. Expose the service to localhost

kubectl --namespace default port-forward $POD_NAME 8080:$CONTAINER_PORT

Your LiteLLM Proxy Server is now running on http://127.0.0.1:4000.

That's it ! That's the quick start to deploy litellm

Make LLM API Requests

:::info 💡 Go here 👉 to make your first LLM API Request

LiteLLM is compatible with several SDKs - including OpenAI SDK, Anthropic SDK, Mistral SDK, LLamaIndex, Langchain (Js, Python)

:::

Deployment Options

Docs	When to Use
Quick Start	call 100+ LLMs + Load Balancing
Deploy with Database	+ use Virtual Keys + Track Spend (Note: When deploying with a database providing a `DATABASE_URL` and `LITELLM_MASTER_KEY` are required in your env )
LiteLLM container + Redis	+ load balance across multiple litellm containers
LiteLLM Database container + PostgresDB + Redis	+ use Virtual Keys + Track Spend + load balance across multiple litellm containers

Deploy with Database

Docker, Kubernetes, Helm Chart

:::warning High Traffic Deployments (1000+ RPS)

If you expect high traffic (1000+ requests per second), Redis is required to prevent database connection exhaustion and deadlocks.

Add this to your config:

general_settings:
  use_redis_transaction_buffer: true

litellm_settings:
  cache: true
  cache_params:
    type: redis
    host: your-redis-host

See Resolve DB Deadlocks for details.

:::

Requirements:

Need a postgres database (e.g. Supabase, Neon, etc) Set DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname> in your env
Set a LITELLM_MASTER_KEY, this is your Proxy Admin key - you can use this to create other keys (🚨 must start with sk-)

We maintain a separate Dockerfile for reducing build time when running LiteLLM proxy with a connected Postgres Database

docker pull docker.litellm.ai/berriai/litellm-database:main-stable

docker run \
    -v $(pwd)/litellm_config.yaml:/app/config.yaml \
    -e LITELLM_MASTER_KEY=sk-1234 \
    -e DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname> \
    -e AZURE_API_KEY=d6*********** \
    -e AZURE_API_BASE=https://openai-***********/ \
    -p 4000:4000 \
    docker.litellm.ai/berriai/litellm-database:main-stable \
    --config /app/config.yaml --detailed_debug

Your LiteLLM Proxy Server is now running on http://0.0.0.0:4000.

Step 1. Create deployment.yaml

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: docker.litellm.ai/berriai/litellm:main-stable
          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/proxy_config.yaml
              subPath: config.yaml      # Specify the field under data of the ConfigMap litellm-config
              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

kubectl apply -f /path/to/deployment.yaml

Step 2. Create service.yaml

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

kubectl apply -f /path/to/service.yaml

Step 3. Start server

kubectl port-forward service/litellm-service 4000:4000

Your LiteLLM Proxy Server is now running on http://0.0.0.0:4000.

:::info

[BETA] Helm Chart is BETA. If you run into an issues/have feedback please let us know https://github.com/BerriAI/litellm/issues

:::

Use this to deploy litellm using a helm chart. Link to the LiteLLM Helm Chart

Step 1. Clone the repository

git clone https://github.com/BerriAI/litellm.git

Step 2. Deploy with Helm

Run the following command in the root of your litellm repo. This will set the litellm proxy master key as sk-1234

helm install \
  --set masterkey=sk-1234 \
  mydeploy \
  deploy/charts/litellm-helm

Step 3. Expose the service to localhost

kubectl \
  port-forward \
  service/mydeploy-litellm-helm \
  4000:4000

Your LiteLLM Proxy Server is now running on http://127.0.0.1:4000.

If you need to set your litellm proxy config.yaml, you can find this in values.yaml

:::info

[BETA] Helm Chart is BETA. If you run into an issues/have feedback please let us know https://github.com/BerriAI/litellm/issues

:::

Use this when you want to use litellm helm chart as a dependency for other charts. The litellm-helm OCI is hosted here https://github.com/BerriAI/litellm/pkgs/container/litellm-helm

Step 1. Pull the litellm helm chart

helm pull oci://docker.litellm.ai/berriai/litellm-helm

# Pulled: docker.litellm.ai/berriai/litellm-helm:0.1.2
# Digest: sha256:7d3ded1c99c1597f9ad4dc49d84327cf1db6e0faa0eeea0c614be5526ae94e2a

Step 2. Unzip litellm helm

Unzip the specific version that was pulled in Step 1

tar -zxvf litellm-helm-0.1.2.tgz

Step 3. Install litellm helm

helm install lite-helm ./litellm-helm

Step 4. Expose the service to localhost

kubectl --namespace default port-forward $POD_NAME 8080:$CONTAINER_PORT

Your LiteLLM Proxy Server is now running on http://127.0.0.1:4000.

Deploy with Redis

Use Redis when you need litellm to load balance across multiple litellm containers

The only change required is setting Redis on your config.yaml LiteLLM Proxy supports sharing rpm/tpm shared across multiple litellm instances, pass redis_host, redis_password and redis_port to enable this. (LiteLLM will use Redis to track rpm/tpm usage )

model_list:
  - model_name: gpt-4o
    litellm_params:
      model: azure/<your-deployment-name>
      api_base: <your-azure-endpoint>
      api_key: <your-azure-api-key>
      rpm: 6      # Rate limit for this deployment: in requests per minute (rpm)
  - model_name: gpt-4o
    litellm_params:
      model: azure/gpt-4o-ca
      api_base: https://my-endpoint-canada-berri992.openai.azure.com/
      api_key: <your-azure-api-key>
      rpm: 6
router_settings:
  redis_host: <your redis host>
  redis_password: <your redis password>
  redis_port: 1992

Start docker container with config

docker run docker.litellm.ai/berriai/litellm:main-stable --config your_config.yaml

Deploy with Database + Redis

model_list:
  - model_name: gpt-4o
    litellm_params:
      model: azure/<your-deployment-name>
      api_base: <your-azure-endpoint>
      api_key: <your-azure-api-key>
      rpm: 6      # Rate limit for this deployment: in requests per minute (rpm)
  - model_name: gpt-4o
    litellm_params:
      model: azure/gpt-4o-ca
      api_base: https://my-endpoint-canada-berri992.openai.azure.com/
      api_key: <your-azure-api-key>
      rpm: 6
router_settings:
  redis_host: <your redis host>
  redis_password: <your redis password>
  redis_port: 1992

Start litellm-databasedocker container with config

docker run --name litellm-proxy \
-e DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname> \
-p 4000:4000 \
docker.litellm.ai/berriai/litellm-database:main-stable --config your_config.yaml

(Non Root) - without Internet Connection

By default prisma generate downloads prisma's engine binaries. This might cause errors when running without internet connection.

Use this docker image to deploy litellm with pre-generated prisma binaries.

docker pull docker.litellm.ai/berriai/litellm-non_root:main-stable

Published Docker Image link

Advanced Deployment Settings

1. Custom server root path (Proxy base url)

Refer to Custom Root Path for more details.

2. SSL Certification

Use this, If you need to set ssl certificates for your on prem litellm proxy

Pass ssl_keyfile_path (Path to the SSL keyfile) and ssl_certfile_path (Path to the SSL certfile) when starting litellm proxy

docker run docker.litellm.ai/berriai/litellm:main-stable \
    --ssl_keyfile_path ssl_test/keyfile.key \
    --ssl_certfile_path ssl_test/certfile.crt

Provide an ssl certificate when starting litellm proxy server

3. Http/2 with Hypercorn

Use this if you want to run the proxy with hypercorn to support http/2

Step 1. Build your custom docker image with hypercorn

# Use the provided base image
FROM docker.litellm.ai/berriai/litellm:main-stable

# Set the working directory to /app
WORKDIR /app

# Copy the configuration file into the container at /app
COPY config.yaml .

# Make sure your docker/entrypoint.sh is executable
RUN chmod +x ./docker/entrypoint.sh

# Expose the necessary port
EXPOSE 4000/tcp

# 👉 Key Change: Install hypercorn
RUN pip install hypercorn

# Override the CMD instruction with your desired command and arguments
# WARNING: FOR PROD DO NOT USE `--detailed_debug` it slows down response times, instead use the following CMD
# CMD ["--port", "4000", "--config", "config.yaml"]

CMD ["--port", "4000", "--config", "config.yaml", "--detailed_debug"]

Step 2. Pass the --run_hypercorn flag when starting the proxy

docker run \
    -v $(pwd)/proxy_config.yaml:/app/config.yaml \
    -p 4000:4000 \
    -e LITELLM_LOG="DEBUG"\
    -e SERVER_ROOT_PATH="/api/v1"\
    -e DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname> \
    -e LITELLM_MASTER_KEY="sk-1234"\
    your_custom_docker_image \
    --config /app/config.yaml
    --run_hypercorn

4. Keepalive Timeout

Defaults to 5 seconds. Between requests, connections must receive new data within this period or be disconnected.

Usage Example: In this example, we set the keepalive timeout to 75 seconds.

docker run docker.litellm.ai/berriai/litellm:main-stable \
    --keepalive_timeout 75

Or set via environment variable: In this example, we set the keepalive timeout to 75 seconds.

export KEEPALIVE_TIMEOUT=75
docker run docker.litellm.ai/berriai/litellm:main-stable

Restart Workers After N Requests

Use this to mitigate memory growth by recycling workers after a fixed number of requests. When set, each worker restarts after completing the specified number of requests. Defaults to disabled when unset.

Usage Examples:

docker run docker.litellm.ai/berriai/litellm:main-stable \
    --max_requests_before_restart 10000

Or set via environment variable:

export MAX_REQUESTS_BEFORE_RESTART=10000
docker run docker.litellm.ai/berriai/litellm:main-stable

5. config.yaml file on s3, GCS Bucket Object/url

Use this if you cannot mount a config file on your deployment service (example - AWS Fargate, Railway etc)

LiteLLM Proxy will read your config.yaml from an s3 Bucket or GCS Bucket

Set the following .env vars

LITELLM_CONFIG_BUCKET_TYPE = "gcs"                              # set this to "gcs"         
LITELLM_CONFIG_BUCKET_NAME = "litellm-proxy"                    # your bucket name on GCS
LITELLM_CONFIG_BUCKET_OBJECT_KEY = "proxy_config.yaml"         # object key on GCS

Start litellm proxy with these env vars - litellm will read your config from GCS

docker run --name litellm-proxy \
   -e DATABASE_URL=<database_url> \
   -e LITELLM_CONFIG_BUCKET_NAME=<bucket_name> \
   -e LITELLM_CONFIG_BUCKET_OBJECT_KEY="<object_key>> \
   -e LITELLM_CONFIG_BUCKET_TYPE="gcs" \
   -p 4000:4000 \
   docker.litellm.ai/berriai/litellm-database:main-stable --detailed_debug

Set the following .env vars

LITELLM_CONFIG_BUCKET_NAME = "litellm-proxy"                    # your bucket name on s3 
LITELLM_CONFIG_BUCKET_OBJECT_KEY = "litellm_proxy_config.yaml"  # object key on s3

Start litellm proxy with these env vars - litellm will read your config from s3

docker run --name litellm-proxy \
   -e DATABASE_URL=<database_url> \
   -e LITELLM_CONFIG_BUCKET_NAME=<bucket_name> \
   -e LITELLM_CONFIG_BUCKET_OBJECT_KEY="<object_key>> \
   -p 4000:4000 \
   docker.litellm.ai/berriai/litellm-database:main-stable

6. Disable pulling live model prices

Disable pulling the model prices from LiteLLM's hosted model prices file, if you're seeing long cold start times or network security issues.

export LITELLM_LOCAL_MODEL_COST_MAP="True"

This will use the local model prices file instead.

Platform-specific Guide

Terraform-based ECS Deployment

LiteLLM maintains a dedicated Terraform tutorial for deploying the proxy on ECS. Follow the step-by-step guide in the litellm-ecs-deployment repository to provision the required ECS services, task definitions, and supporting AWS resources.

Clone the tutorial repository to review the Terraform modules and variables.

git clone https://github.com/BerriAI/litellm-ecs-deployment.git
cd litellm-ecs-deployment

Initialize and validate the Terraform project before applying it to your chosen workspace/account.

terraform init
terraform plan
terraform apply

Once terraform apply completes, do ./build.sh to push the repository on ECR and update the ECS cluster. Use that endpoint (port 4000 by default) for API requests to your LiteLLM proxy.

Kubernetes (AWS EKS)

Step1. Create an EKS Cluster with the following spec

eksctl create cluster --name=litellm-cluster --region=us-west-2 --node-type=t2.small

Step 2. Mount litellm proxy config on kub cluster

This will mount your local file called proxy_config.yaml on kubernetes cluster

kubectl create configmap litellm-config --from-file=proxy_config.yaml

Step 3. Apply kub.yaml and service.yaml Clone the following kub.yaml and service.yaml files and apply locally

Use this kub.yaml file - litellm kub.yaml
Use this service.yaml file - litellm service.yaml

Apply kub.yaml

kubectl apply -f kub.yaml

Apply service.yaml - creates an AWS load balancer to expose the proxy

kubectl apply -f service.yaml

# service/litellm-service created

Step 4. Get Proxy Base URL

kubectl get services

# litellm-service   LoadBalancer   10.100.6.31   a472dc7c273fd47fd******.us-west-2.elb.amazonaws.com   4000:30374/TCP   63m

Proxy Base URL = a472dc7c273fd47fd******.us-west-2.elb.amazonaws.com:4000

That's it, now you can start using LiteLLM Proxy

AWS Cloud Formation Stack

LiteLLM AWS Cloudformation Stack - Get the best LiteLLM AutoScaling Policy and Provision the DB for LiteLLM Proxy

This will provision:

LiteLLMServer - EC2 Instance
LiteLLMServerAutoScalingGroup
LiteLLMServerScalingPolicy (autoscaling policy)
LiteLLMDB - RDS::DBInstance

Using AWS Cloud Formation Stack

LiteLLM Cloudformation stack is located here - litellm.yaml

1. Create the CloudFormation Stack:

In the AWS Management Console, navigate to the CloudFormation service, and click on "Create Stack."

On the "Create Stack" page, select "Upload a template file" and choose the litellm.yaml file

Now monitor the stack was created successfully.

2. Get the Database URL:

Once the stack is created, get the DatabaseURL of the Database resource, copy this value

3. Connect to the EC2 Instance and deploy litellm on the EC2 container

From the EC2 console, connect to the instance created by the stack (e.g., using SSH).

Run the following command, replacing <database_url> with the value you copied in step 2

docker run --name litellm-proxy \
   -e DATABASE_URL=<database_url> \
   -p 4000:4000 \
   docker.litellm.ai/berriai/litellm-database:main-stable

4. Access the Application:

Once the container is running, you can access the application by going to http://<ec2-public-ip>:4000 in your browser.

Google Cloud Run

Fork this repo - github.com/BerriAI/example_litellm_gcp_cloud_run
Edit the litellm_config.yaml file in the repo to include your model settings
Deploy your forked github repo on Google Cloud Run

Testing your deployed proxy

Assuming the required keys are set as Environment Variables

https://litellm-7yjrj3ha2q-uc.a.run.app is our example proxy, substitute it with your deployed cloud run app

curl https://litellm-7yjrj3ha2q-uc.a.run.app/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
     "model": "gpt-4o",
     "messages": [{"role": "user", "content": "Say this is a test!"}],
     "temperature": 0.7
   }'

Render

https://render.com/

Railway

https://railway.app

Step 1: Click the button to deploy to Railway

Step 2: Set PORT = 4000 on Railway Environment Variables

Extras

Docker compose

Step 1

(Recommended) Use the example file docker-compose.yml given in the project root. e.g. https://github.com/BerriAI/litellm/blob/main/docker-compose.yml

Here's an example docker-compose.yml file

version: "3.9"
services:
  litellm:
    build:
      context: .
      args:
        target: runtime
    image: docker.litellm.ai/berriai/litellm:main-stable
    ports:
      - "4000:4000" # Map the container port to the host, change the host port if necessary
    volumes:
      - ./litellm-config.yaml:/app/config.yaml # Mount the local configuration file
    # You can change the port or number of workers as per your requirements or pass any new supported CLI argument. Make sure the port passed here matches with the container port defined above in `ports` value
    command: [ "--config", "/app/config.yaml", "--port", "4000", "--num_workers", "8" ]

# ...rest of your docker-compose config if any

Step 2

Create a litellm-config.yaml file with your LiteLLM config relative to your docker-compose.yml file.

Check the config doc here

Step 3

Run the command docker-compose up or docker compose up as per your docker installation.

Use -d flag to run the container in detached mode (background) e.g. docker compose up -d

Your LiteLLM container should be running now on the defined port e.g. 4000.

IAM-based Auth for RDS DB

Set AWS env var

export AWS_WEB_IDENTITY_TOKEN='/path/to/token'
export AWS_ROLE_NAME='arn:aws:iam::123456789012:role/MyRole'
export AWS_SESSION_NAME='MySession'

See all Auth options

Add RDS credentials to env

export DATABASE_USER="db-user"
export DATABASE_PORT="5432"
export DATABASE_HOST="database-1-instance-1.cs1ksmwz2xt3.us-west-2.rds.amazonaws.com"
export DATABASE_NAME="database-1-instance-1"
export DATABASE_SCHEMA="schema-name" # skip to use the default "public" schema

Run proxy with iam+rds

litellm --config /path/to/config.yaml --iam_token_db_auth

✨ Blocking web crawlers

Note: This is an enterprise only feature.

To block web crawlers from indexing the proxy server endpoints, set the block_robots setting to true in your litellm_config.yaml file.

general_settings:
  block_robots: true

How it works

When this is enabled, the /robots.txt endpoint will return a 200 status code with the following content:

User-agent: *
Disallow: /

Deployment FAQ

Q: Is Postgres the only supported database, or do you support other ones (like Mongo)?

A: We explored MySQL but that was hard to maintain and led to bugs for customers. Currently, PostgreSQL is our primary supported database for production deployments.

Q: If there is Postgres downtime, how does LiteLLM react? Does it fail-open or is there API downtime?

A: You can gracefully handle DB unavailability if it's on your VPC. See our production guide for more details: Gracefully Handle DB Unavailability

33 KiB Raw Blame History Unescape Escape

Docker, Helm, Terraform

Quick Start

Docker Run

Step 1. CREATE config.yaml

Step 2. RUN Docker Image

Step 3. TEST Request

Docker Run - CLI Args

Use litellm as a base image

Build from litellm pip package

Terraform

Kubernetes

Helm Chart

Step 1. Pull the litellm helm chart

Step 2. Unzip litellm helm

Step 3. Install litellm helm

Step 4. Expose the service to localhost

Make LLM API Requests

Deployment Options

Deploy with Database

Docker, Kubernetes, Helm Chart

Step 1. Create deployment.yaml

Step 2. Create service.yaml

Step 3. Start server

Step 1. Clone the repository

Step 2. Deploy with Helm

Step 3. Expose the service to localhost

Step 1. Pull the litellm helm chart

Step 2. Unzip litellm helm

Step 3. Install litellm helm

Step 4. Expose the service to localhost

Deploy with Redis

Deploy with Database + Redis

(Non Root) - without Internet Connection

Advanced Deployment Settings

1. Custom server root path (Proxy base url)

2. SSL Certification

3. Http/2 with Hypercorn

4. Keepalive Timeout

Restart Workers After N Requests

5. config.yaml file on s3, GCS Bucket Object/url

6. Disable pulling live model prices

Platform-specific Guide

Terraform-based ECS Deployment

Kubernetes (AWS EKS)

AWS Cloud Formation Stack

Using AWS Cloud Formation Stack

1. Create the CloudFormation Stack:

2. Get the Database URL:

3. Connect to the EC2 Instance and deploy litellm on the EC2 container

4. Access the Application:

Google Cloud Run

Testing your deployed proxy

Render

Railway

Extras

Docker compose

IAM-based Auth for RDS DB

✨ Blocking web crawlers

How it works

Deployment FAQ

33 KiB

Raw Blame History

Build from litellm `pip` package