Edge Deployments: Running Requesty Behind Cloudflare Workers

Edge computing has revolutionized how we think about API performance and global scalability. By bringing compute closer to users, we can dramatically reduce latency and improve the user experience. Today, we'll explore how to supercharge your AI applications by running Requesty's unified LLM gateway behind Cloudflare Workers, creating a globally distributed, lightning-fast AI infrastructure.

Why Edge Deployments Matter for AI Applications

Traditional centralized API deployments often struggle with latency, especially for globally distributed users. When you're building AI-powered applications, every millisecond counts. Users expect instant responses, whether they're in Tokyo, London, or São Paulo.

Edge deployments solve this by running your code at hundreds of locations worldwide, bringing compute closer to your users. For AI applications using Requesty's LLM routing, this means:

  • Sub-50ms response times for initial requests

  • Intelligent caching of AI responses at the edge

  • Automatic failover across 160+ models with minimal latency

  • Cost savings through edge caching and smart routing

Understanding Cloudflare Workers

Cloudflare Workers are serverless functions that run at Cloudflare's edge network, spanning hundreds of locations globally. With typical execution times under 1 millisecond and deployment in under 30 seconds, they're perfect for high-performance API gateways.

Key benefits include:

  • Global deployment: Your code runs in 300+ cities worldwide

  • Auto-scaling: Handle millions of requests without infrastructure management

  • Cost-effective: Starting at $0.50 per million requests

  • JavaScript/TypeScript support: Familiar development experience

Implementing Requesty at the Edge

Running Requesty behind Cloudflare Workers creates a powerful combination. Here's how the architecture works:

Architecture Overview

1. Client Request: User sends a request to your Cloudflare Worker endpoint 2. Edge Processing: Worker handles authentication, rate limiting, and request validation 3. Requesty Routing: Worker forwards the request to Requesty's API 4. Smart Model Selection: Requesty's smart routing automatically selects the best model 5. Response Caching: Worker caches successful responses at the edge 6. Client Response: User receives the AI response with minimal latency

Setting Up Your Edge Deployment

Here's a step-by-step approach to deploying Requesty behind Cloudflare Workers:

Step 1: Initialize Your Worker Project

Create a new Cloudflare Worker project using Wrangler CLI. This will be your edge gateway to Requesty's 160+ models.

Step 2: Configure Environment Variables

Store your Requesty API key securely in Worker environment variables. This keeps your credentials safe while enabling global access to models like Claude 4, DeepSeek R1, and GPT-4o.

Step 3: Implement Request Handling

Your Worker should:

  • Validate incoming requests

  • Add any necessary headers or authentication

  • Forward requests to Requesty's API endpoint

  • Handle responses and errors gracefully

Step 4: Add Edge Caching

Leverage Cloudflare's Cache API to store AI responses. This is particularly powerful when combined with Requesty's built-in caching, creating a two-tier cache system that can reduce costs by up to 80%.

Step 5: Deploy Globally

Use Wrangler to deploy your Worker to Cloudflare's global network. Your AI gateway is now available at the edge, ready to serve users worldwide.

Optimization Strategies

Intelligent Caching

Combine Cloudflare's edge caching with Requesty's response caching for maximum efficiency:

  • Cache common prompts and responses at the edge

  • Use cache headers to control TTL based on content type

  • Implement cache warming for frequently requested data

  • Leverage Requesty's automatic caching for model responses

Request Routing

Optimize how requests flow through your edge deployment:

  • Use Workers KV to store user preferences and routing rules

  • Implement geographic routing to preferred models

  • Create custom fallback chains using Requesty's fallback policies

  • Route based on request complexity or user tier

Performance Monitoring

Track and optimize your edge deployment:

  • Monitor Worker execution times

  • Track cache hit rates

  • Analyze model usage patterns

  • Use Requesty's analytics to understand cost and performance

Real-World Use Cases

Global Chat Applications

Deploy chat interfaces that automatically route to the fastest available model. Users in Asia might be served by models with lower latency in that region, while Requesty's routing optimizations ensure consistent quality.

Content Generation Platforms

Cache frequently generated content at the edge while using Requesty's smart routing to balance cost and quality. Popular templates can be served instantly from cache, while unique requests leverage the best available model.

API Services

Build AI-powered APIs that scale globally. Use Workers to handle authentication and rate limiting at the edge, while Requesty manages model selection, failover, and cost optimization behind the scenes.

Cost Optimization Tips

Running Requesty at the edge can significantly reduce your AI infrastructure costs:

  • Edge Caching: Cache responses at Cloudflare's edge to avoid repeated API calls

  • Smart Routing: Let Requesty's smart routing automatically select cost-effective models

  • Request Batching: Aggregate similar requests at the edge before forwarding

  • Conditional Requests: Use ETags and conditional headers to minimize data transfer

With these optimizations, many Requesty users see up to 80% cost savings compared to direct model access.

Security Considerations

Edge deployments require careful security planning:

  • API Key Management: Store Requesty API keys securely in Worker secrets

  • Request Validation: Validate and sanitize all inputs at the edge

  • Rate Limiting: Implement edge-based rate limiting to prevent abuse

  • Security Guardrails: Leverage Requesty's security features for content filtering and compliance

CORS and Access Control

Configure CORS headers appropriately in your Worker to control which domains can access your AI endpoints. Combine this with Requesty's built-in security features for comprehensive protection.

Advanced Patterns

Multi-Region Failover

Create sophisticated failover patterns by combining Cloudflare's global network with Requesty's model routing:

1. Primary edge location handles the request 2. If latency exceeds threshold, failover to secondary edge 3. Requesty automatically fails over between models 4. Response cached at multiple edge locations

A/B Testing

Use Workers to implement A/B testing for different AI models or prompts:

  • Route percentage of traffic to experimental models

  • Compare performance metrics in real-time

  • Gradually roll out new models or configurations

  • Track results using Requesty's request metadata

Getting Started with Requesty

Ready to build your edge-deployed AI infrastructure? Here's how to get started:

1. Sign up for Requesty to get your API key 2. Set up your Cloudflare Worker project 3. Configure your Worker to proxy requests to Requesty 4. Implement caching and optimization strategies 5. Deploy globally and monitor performance

With Requesty's unified API supporting 160+ models, you can switch between Claude 4, DeepSeek R1, GPT-4o, and more without changing your code. Combined with Cloudflare Workers, you get a globally distributed, highly available AI infrastructure that scales automatically.

Conclusion

Edge deployments with Cloudflare Workers and Requesty create a powerful combination for building global AI applications. By bringing compute closer to users and leveraging intelligent routing and caching, you can deliver exceptional performance while controlling costs.

The combination of Cloudflare's global edge network and Requesty's unified LLM gateway gives you:

  • Global low-latency AI responses

  • Automatic failover and load balancing across 160+ models

  • Up to 80% cost savings through intelligent caching and routing

  • Enterprise-grade security and compliance features

  • Simple integration with existing applications

Whether you're building a chat application, content generation platform, or AI-powered API, edge deployments with Requesty provide the performance, reliability, and cost-efficiency you need to succeed.

Start building your edge-deployed AI infrastructure today with Requesty's unified LLM gateway. Join the 15,000+ developers already using Requesty to power their AI applications at scale.