Cloudflare Workers AI Gateway Proxy

Why Cloudflare Workers for AI Gateway?

Leverage the power of edge computing for your LLM proxy infrastructure

⚡

Ultra-Low Latency

Deploy your AI gateway at 275+ edge locations worldwide. Requests are processed at the nearest edge node, reducing latency to under 50ms for most users globally.

🔄

Automatic Scaling

Serverless architecture handles millions of requests without manual scaling. Cloudflare automatically distributes load across all edge locations based on traffic patterns.

💰

Cost-Effective

Pay only for actual compute time with generous free tier. No idle server costs, no infrastructure management overhead, and predictable pricing based on request volume.

🛡️

Built-in Security

DDoS protection, Web Application Firewall, and bot mitigation come standard. Secure your AI API endpoints with enterprise-grade security at no additional cost.

🌐

Global Distribution

Code is automatically deployed to all edge locations. Your AI gateway runs close to users everywhere, from San Francisco to Singapore, without any configuration.

🔧

Easy Deployment

Deploy with a single command using Wrangler CLI. Rollback instantly, run multiple environments, and manage your gateway with familiar developer tools and workflows.

Architecture Overview

How Cloudflare Workers AI Gateway processes requests

Request flow through the edge network

Client App

→

CF Edge

→

Worker Proxy

→

LLM Provider

Edge Processing

Cloudflare Workers execute your custom logic at the edge before forwarding requests to LLM providers. This enables authentication, rate limiting, caching, and request transformation without additional infrastructure.

The V8 JavaScript engine powers Workers, providing near-native performance with minimal cold start times. Your code runs in isolated sandboxes, ensuring security and reliability for multi-tenant deployments.

Smart Routing

Implement intelligent routing logic to distribute requests across multiple LLM providers based on cost, latency, availability, or custom rules. Workers provide the flexibility to route requests dynamically based on real-time conditions.

Cache responses at the edge for common queries, reducing API costs and improving response times. Implement fallback providers automatically when primary services experience outages.

Implementation Guide

Step-by-step setup for your Cloudflare Workers AI Gateway

worker.js JavaScript

// Cloudflare Workers AI Gateway Proxy
export default {
  async fetch(request, env, ctx) {
    const url = new URL(request.url);
    
    // Authentication check
    const apiKey = request.headers.get('X-API-Key');
    if (!apiKey || !await validateKey(apiKey, env)) {
      return new Response(
        JSON.stringify({ error: 'Unauthorized' }),
        { status: 401, headers: { 'Content-Type': 'application/json' } }
      );
    }
    
    // Rate limiting with KV
    const clientId = await getClientId(apiKey);
    const rateLimit = await checkRateLimit(clientId, env);
    if (!rateLimit.allowed) {
      return new Response(
        JSON.stringify({ error: 'Rate limit exceeded' }),
        { status: 429, headers: { 'Content-Type': 'application/json' } }
      );
    }
    
    // Forward to LLM provider
    const targetUrl = 'https://api.openai.com/v1/chat/completions';
    const response = await fetch(targetUrl, {
      method: request.method,
      headers: {
        'Authorization': `Bearer ${env.OPENAI_API_KEY}`,
        'Content-Type': 'application/json'
      },
      body: request.body
    });
    
    return response;
  }
};

async function validateKey(apiKey, env) {
  const validKey = await env.API_KEYS.get(apiKey);
  return validKey !== null;
}

async function checkRateLimit(clientId, env) {
  const key = `ratelimit:${clientId}`;
  const count = parseInt(await env.KV.get(key) || '0');
  
  if (count >= 100) {
    return { allowed: false };
  }
  
  await env.KV.put(key, (count + 1).toString(), {
    expirationTtl: 60
  });
  
  return { allowed: true, remaining: 100 - count - 1 };
}

💡 Pro Tip: Caching Strategies

Implement response caching using Cloudflare KV or Cache API for identical prompts. Cache embeddings and common completions to reduce API costs significantly. Use Cache-TTL headers to control cache duration based on your use case requirements.

Deployment Steps

1

Install Wrangler CLI

npm install -g wrangler
2

Authenticate with Cloudflare

wrangler login
3

Create Worker Project

wrangler init ai-gateway
4

Deploy to Edge

wrangler deploy

Key Benefits

✓ No server management required

✓ Instant global deployment

✓ Built-in DDoS protection

✓ Automatic HTTPS

✓ Real-time logs and analytics

✓ Zero cold start penalty

Advanced Configuration

Optimize your AI gateway for production workloads

wrangler.toml TOML

name = "ai-gateway-proxy"
main = "worker.js"
compatibility_date = "2024-01-01"

# KV namespace for caching and rate limiting
[[kv_namespaces]]
binding = "KV"
id = "your-kv-namespace-id"

# Environment variables (secrets)
[vars]
ENVIRONMENT = "production"

# Secrets (set via: wrangler secret put)
# OPENAI_API_KEY
# ANTHROPIC_API_KEY
# API_KEYS_SECRET

# Custom domain routing
[[routes]]
pattern = "api.yourdomain.com/*"
custom_domain = true

🔗 Partner Resources

Explore related guides: LiteLLM Getting Started | Authentication Setup | Ollama OpenAI API | LM Studio Proxy