Infrastructure and Networks
Why your AI stack probably needs an edge layer
Cloud inference is fine — until your users are in Lagos, Jakarta or São Paulo, and the round trip starts to show. Adding an edge inference layer (Cloudflare Workers AI, Fastly Compute, Vercel Edge) can cut global p95 latency by 40 to 70% without touching your model. This primer walks through the architecture, the cost trade-offs, cold-start behaviour, and the three cases where edge is clearly worth it — plus an honest note on why edge vector databases are still half-baked and what to use instead.
Why it matters
If your AI product serves global users and you're only running cloud inference, you're leaving performance — and retention — on the table.
Network impact
LatencyEdge inference reduces global p95 latency 40-70% by moving compute closer to users.
SecurityEdge nodes have smaller attack surface but require careful secrets management — never store API keys at edge.
ScalabilityEdge scales automatically with CDN capacity; no pre-provisioning needed for traffic spikes.
What to do
- Check your user geography in analytics — where are your slowest users?
- Benchmark your current p95 latency by region
- Prototype one endpoint on Cloudflare Workers AI (free tier available)
- Evaluate if your model fits edge constraints (size, context window)
- Document your edge deployment architecture before scaling it