Inference Latency 101: What Every AI Startup Should Know Before Launch

Model quality gets all the attention during development because it's what you're optimizing for in evaluation. Latency gets all the attention after launch, because it's what determines whether users actually wait for the answer. For a startup company shipping its first AI feature, understanding the basics of inference latency before launch saves a painful post-launch scramble.

Latency has three main components: time to first token, generation speed, and network overhead. Time to first token is usually what users perceive as 'speed' — a response that starts streaming in 400ms feels fast even if the full answer takes four seconds to complete, while a response that sits silent for four seconds before appearing all at once feels slow even if the total time is identical. If your product can stream, use it; it's the single highest-leverage latency fix available.

Generation speed is mostly a function of model size and hardware, and it's where the classic startup tradeoff shows up: a larger model is often more capable but measurably slower and more expensive per request. Before defaulting to the largest available model, test whether a smaller, fine-tuned one meets your quality bar at a fraction of the latency and cost — for most narrow product use cases, it does.

Network overhead is the one teams forget entirely. Every hop between your user, your API, and your inference endpoint adds latency, and for a startup company with users spread across regions, running inference in a single data center can add hundreds of milliseconds before the model even starts generating. Running inference closer to your users — on an edge network rather than a single region — is often a bigger latency win than any model-level optimization.

The teams that get this right treat latency as a product requirement with a number attached, not a vague goal. Pick a p95 target before launch, measure it in production from day one, and you'll catch regressions before they show up in your churn numbers instead of after.