Building Global Web Apps? Reduce Latency with Edge Computing

A practical, developer-facing introduction to edge computing — running lightweight application logic at hundreds of globally distributed points of presence rather than in a handful of centralized cloud regions.

The article frames edge computing as a latency optimization, not a universal upgrade. A request from Sydney to Virginia takes ~800 ms round-trip; the same logic at a nearby edge location drops to 50–100 ms. The biggest gains come when users are far from your origin server.

A decision table maps use cases to infrastructure: edge for auth, personalization, geo-routing, session management, and real-time data sync; CDN for static pages; traditional cloud for heavy compute, video processing, AI inference, and complex database queries.

Latency breakdowns compare traditional serverless (400–650+ ms including cold starts and regional network latency) against edge execution (~90–100 ms with near-zero cold starts). Real-world benchmark data shows P99 global latency improving from ~1,200 ms to 75–200 ms after moving to edge.

The piece covers three major platforms (Cloudflare Workers, Vercel Edge, AWS Lambda@Edge) with a cost comparison at 10M requests/month — Cloudflare Workers lead at ~$5, with others ranging $7–20. A worked Cloudflare Worker example shows geo-based content delivery for e-commerce (region-specific pricing, promotions, shipping) adding 5–15 ms while eliminating a 200–400 ms backend round-trip.

Limitations include stateless execution (requiring KV stores for persistence), storage costs, and runtime constraints (~128 MB memory, execution time limits, restricted Node.js APIs). Edge functions are ill-suited for compute-heavy workloads, unsupported languages, or strict data-locality requirements.

The article touches on emerging AI patterns at the edge — inference routing by user tier, prompt preprocessing to block invalid requests before they reach GPUs, and API-key verification to prevent wasted LLM calls — with the core principle that edge handles lightweight logic while cloud handles inference.

The recommended approach: start small, move one API endpoint to the edge, measure real performance across regions, and decide with data rather than theory.