Why We Put AI Behind a FastAPI Service

When a team wants to add AI to an existing product, the instinct is often to build it straight into the main application. That works for a demo. For anything you plan to keep, we usually recommend a separate service that your app calls over an API, and we usually build it in Python with FastAPI.

Why not just add it to the app?

Three reasons come up again and again.

• Different tools. Most machine learning libraries are written for Python. If your app is PHP, Node or .NET, you end up fighting your stack to use them.
• Different workload. Model calls are slow and can be expensive. They need queues, retries and timeouts that a normal page request doesn’t.
• Different release rhythm. You’ll change prompts and models far more often than you change your checkout. Keeping them apart means a new model version doesn’t need a full app release.

What FastAPI gives you

FastAPI describes every endpoint with typed schemas, so request and response shapes are checked automatically and documentation is generated for you. Front-end developers and partners can read the docs and start calling it the same day. It also supports async work, which matters when a model takes several seconds to answer.

Things we build in from the start

• Authentication and per-user limits, so one noisy client can’t run up your API bill.
• Background jobs and status endpoints for slow tasks, so users see progress instead of a spinner that never ends.
• Streaming for chat-style responses, so the first words appear quickly.
• Caching, because many requests repeat and there’s no reason to pay twice for the same answer.
• Tests that run in CI, including a set of real example inputs to catch regressions when a prompt changes.

Keeping costs predictable

With a model API, the bill grows with usage, so we log tokens and cost per feature from day one. That makes it easy to spot which feature is expensive and to set sensible limits before the invoice does it for you.

Does it work with our current stack?

Yes. The service is just an HTTP API. Your existing application makes a request and gets JSON back, whatever it’s written in. We can host it in your cloud account, on a managed platform or on your own servers, and we document how it’s deployed so your team can run it without us.

If you’re planning an AI feature and want to know what the backend would look like, tell us what the app does today and what you’d like it to do. We’ll sketch the service and give you a fixed estimate.

You can read more about our AI Backend, APIs & MLOps service on the main site.