Building an AI Gateway on Fastly Compute

Why It Matters

Edge routing centralizes multi‑provider management, reducing latency and improving reliability while preserving existing provider contracts. It gives enterprises a scalable way to control cost and failover without code changes.

Key Takeaways

• Edge gateway routes LLM calls before provider contact
• Fastly Compute adds ~200‑300 ms classification latency
• Policy stored in KV, updated without redeploy
• Isolation via WebAssembly secures multiple API keys
• Optional header bypasses classification for ultra‑low latency

Pulse Analysis

The rise of agentic AI applications has exposed the fragility of traditional, hard‑coded LLM integrations. When a provider experiences a hiccup or a rate limit is hit, entire multi‑step workflows can stall, inflating latency and eroding user experience. By shifting the routing decision to the edge, developers can intercept requests before they reach any provider, applying consistent policies that balance performance, cost, and availability across OpenAI, Anthropic, and others. This architectural shift aligns with the broader move toward edge‑first compute, where proximity to the user translates directly into lower round‑trip times.

Fastly Compute provides the ideal platform for such a gateway. Its WebAssembly sandbox delivers sub‑millisecond cold starts and strict isolation, essential for handling multiple API keys safely. A lightweight diffusion‑based model, Mercury 2, classifies incoming chat completions in roughly 200‑300 ms, a marginal addition compared to typical provider latencies of one to several seconds. Routing tiers—fast, balanced, quality, reasoning—are stored in Fastly’s KV Store, allowing operators to adjust mappings or add new providers without redeploying code. Metadata headers expose the routing decision, aiding debugging and compliance.

For enterprises, this edge AI gateway offers tangible business value: reduced operational risk, finer cost control, and the ability to react instantly to pricing or performance changes from LLM vendors. Future enhancements such as automatic failover, semantic caching, and streaming passthrough could further tighten latency budgets and improve resilience. As production AI workloads continue to scale, edge‑native routing solutions like Fastly’s promise a more reliable, cost‑effective foundation for the next generation of intelligent applications.