Building & shipping
AI Application Deployment Guide
What changes when you deploy an AI feature versus a traditional web feature, and what stays the same.
Deploying an AI application borrows most of its practice from ordinary web deployment — but a few things are different enough to plan for deliberately: variable latency, per-request cost, and provider dependency.
This guide covers the deployment decisions specific to AI features, on top of standard engineering practice you likely already know.
Key takeaways
Latency and streaming
Model responses can take anywhere from under a second to many seconds, especially for longer generations or multi-step agent tasks. Streaming responses back to the user as they are generated, rather than waiting for the full output, dramatically improves perceived performance.
Design your interface around this reality rather than assuming instant, uniform response times like a typical database query.
Cost-aware scaling
Unlike most infrastructure costs, AI feature costs scale directly with usage in a very visible way — every request has a real, trackable cost. Log token usage and cost per request from the start, set budgets, and build alerts before a spike becomes a surprise bill.
Consider caching common queries and using smaller models for simpler tasks to control costs without sacrificing quality where it matters.
Safe rollouts and fallback design
Roll out new AI features to a small percentage of users first, and monitor quality metrics, not just uptime. Because model behavior can shift with provider updates, treat AI features as needing more ongoing monitoring than typical static code.
Always design a fallback: a cached response, a simpler rule-based answer, or a clear "try again" state for when the model or provider is unavailable.
Put this into practice