🚀 Cloud Native

LLM Proxy Serverless Deployment

Deploy your LLM proxy on serverless platforms for automatic scaling, pay-per-use pricing, and zero infrastructure management. Perfect for variable workloads and cost optimization.

🟠 AWS Lambda

🔵 Cloudflare Workers

▲ Vercel Functions

🔷 Google Cloud Functions

🔵 Azure Functions

Why Serverless?

Benefits of serverless deployment for LLM proxies

📈

Auto Scaling

Scales automatically with demand

💰

Pay Per Use

Only pay for actual compute time

🚀

Zero Ops

No server management required

🌍

Global Deploy

Deploy close to users worldwide

Platform Guides

Deploy on your preferred serverless platform

🟠

AWS Lambda

Deploy your LLM proxy as a Lambda function with API Gateway for HTTP access.

serverless.yml

service: llm-proxy

provider:
  name: aws
  runtime: python3.11
  timeout: 30

functions:
  proxy:
    handler: handler.main
    events:
      - httpApi:
          path: /{proxy+}
          method: ANY
                            

🔵

Cloudflare Workers

Edge deployment with global distribution for minimal latency.

wrangler.toml

name = "llm-proxy"
main = "src/index.js"
compatibility_date = "2024-01-01"

[vars]
OPENAI_API_KEY = ""

[ai]
binding = "AI"
                            

▲

Vercel Functions

Simple deployment with automatic CI/CD and preview deployments.

api/proxy.js

export default async function handler(req) {
  const response = await fetch(
    process.env.PROXY_URL,
    { method: req.method, body: req.body }
  )
  return new Response(response.body)
}
                            

🔷

Google Cloud Functions

Integrate with Google Cloud services and Vertex AI.

main.py

def llm_proxy(request):
    import requests
    
    response = requests.post(
        "https://api.openai.com/v1/chat/completions",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json=request.get_json()
    )
    return response.text
                            