Edge computing moves computation closer to end users, dramatically reducing latency. This guide explores edge platforms, use cases, and implementation patterns.
Understanding Edge Computing
Traditional cloud functions run in specific regions. Edge functions run globally, close to your users:
Traditional: User (Tokyo) ──────────────────> Server (US-East)
~200ms latency
Edge: User (Tokyo) ──> Edge Node (Tokyo)
~20ms latency
Edge Platforms Compared
Cloudflare Workers
Vercel Edge Functions
Deno Deploy
Common Edge Use Cases
1. Geolocation-Based Routing
2. A/B Testing at the Edge
3. Authentication at the Edge
4. Image Optimization
5. Rate Limiting
Edge Data Storage
KV Storage (Key-Value)
Durable Objects (Stateful)
D1 (SQLite at the Edge)
Edge Limitations
Understanding constraints is crucial:
| Constraint | Cloudflare | Vercel Edge | Deno Deploy |
|---|---|---|---|
| CPU time | 10-50ms | 25s | 50ms |
| Memory | 128MB | 128MB | 512MB |
| Bundle size | 1MB | 2MB | No limit |
| Subrequests | 50 | 25 | 1000 |
Working Within Limits
Deployment Workflow
Monitoring Edge Functions
Conclusion
Edge computing enables ultra-low latency for global users. Start with simple use cases like geolocation or caching, then expand to more complex patterns as you understand the constraints. The key is knowing when edge makes sense versus traditional serverless.