Designing Performant APIs with Litestar

APIs development start simple, feel fast, and ship quickly. Then the API grows: more endpoints, more traffic, more data, and suddenly performance becomes unpredictable. Latency creeps in, async code blocks without anyone noticing, and every new feature feels a little riskier to add.

This talk is about building Python APIs that stay fast as they evolve.

We’ll look at where performance issues really come from in real-world APIs: request lifecycle overhead, hidden blocking calls, dependency injection costs, serialization, and poorly scoped middleware. Instead of chasing micro-optimizations or benchmark numbers, we’ll focus on design decisions that make performance predictable.

Using Litestar as a concrete example, you’ll see how explicit control over routing, dependencies, and async execution helps avoid common performance traps. We’ll walk through realistic API patterns and show how to structure handlers and dependencies so performance problems don’t slowly accumulate.

You’ll leave with practical techniques you can apply immediately, whether you’re building a new API or maintaining one in production. Even if you don’t use Litestar today, the design principles in this talk will help you reason more clearly about performance in any Python API.