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FastAPI Development Services

An async-first API framework in which request validation and documentation both come from the same type definitions.

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How we work

FastAPI is an API-first Python framework designed around type hints and async/await from the start, with neither added later. A route's function signature, typed with Pydantic models, produces the request validation, the response schema and the OpenAPI documentation all at once, which removes a whole category of drift between what an API claims to accept and what it actually enforces.

We reach for FastAPI over Django when a project is API-only with no need for an admin panel or server-rendered templates, and over a synchronous framework generally when the workload involves a lot of concurrent I/O: calling other services, waiting on a model to respond, or handling many simultaneous connections. It has become a default choice for serving machine learning models and building microservices in Python-heavy organizations specifically because of that async performance profile combined with Pydantic's validation.

Fit

When FastAPI is the right choice, and when it is not

Good fit

You are building an API-only backend with no need for server-rendered pages or a built-in admin panel
The service needs to handle high concurrency efficiently, such as an API that spends most of its time waiting on an LLM, a database, or another downstream service
You are serving a machine learning model and want request validation, response schemas and interactive API documentation generated from the same Pydantic models used elsewhere in the Python codebase
The project is a microservice in a larger architecture where a lean, fast-starting service matters more than a full framework's conventions

Consider something else

The application needs an admin panel, user-facing server-rendered pages, or a lot of standard CRUD and permission scaffolding that a batteries-included framework like Django already provides
You are building a traditional multi-page web application rather than an API consumed by a separate frontend or another service
The team wants an opinionated, prescribed project structure; FastAPI gives you the routing and validation layer but leaves architecture decisions (folder structure, dependency injection patterns) more open than Django or NestJS
The workload benefits little from async I/O because it is dominated by CPU-bound work; async does not speed up computation that is not waiting on I/O, and a synchronous framework performs comparably in that case

What we build

What we build with FastAPI

AI and ML model-serving APIs
Endpoints that load a model once at startup and serve predictions or generations per request, with Pydantic validating input shapes before they reach the model.
High-concurrency microservices
Lean services in a larger architecture that need to handle many simultaneous requests efficiently, built with async route handlers and async database drivers end to end.
Auto-documented public and partner APIs
OpenAPI and interactive Swagger documentation generated directly from route definitions and Pydantic models, staying accurate because the docs cannot drift from the actual validation logic.
Backend-for-frontend and aggregation layers
Services that call several downstream APIs concurrently using async I/O and combine the results into one response shaped for a specific frontend.

Ecosystem

Libraries and tools we pair it with

Pydantic v2 for request and response validation
SQLAlchemy with async drivers, or SQLModel, for database access
Uvicorn or Gunicorn with Uvicorn workers as the ASGI server
httpx for making async calls to other services
Celery or arq for background task queues alongside the API
pytest with pytest-asyncio for testing async routes

What async actually buys a FastAPI service

Async/await lets a single worker process handle another request while one is waiting on I/O, such as a database query or a call to another API, instead of that worker sitting idle until the wait finishes. The benefit is proportional to how much of a request's time is spent waiting rather than computing: an endpoint that mostly waits on a slow downstream API benefits enormously, while an endpoint doing heavy computation with little I/O sees almost no benefit from async at all.

A common mistake is writing async route handlers that call a synchronous, blocking library underneath, which defeats the purpose entirely: the blocking call still ties up the event loop for its full duration. We audit every dependency in a FastAPI project's request path (the database driver, the HTTP client, the cache client) to confirm it has a genuine async implementation, not just an async-looking function wrapped around blocking code.

FastAPI as the serving layer for a machine learning model

Serving a model in production is a different problem from training one, and FastAPI has become a common default for the serving layer because Pydantic validates the input shape a model expects before the model ever sees malformed data, and the automatic documentation gives other teams a clear contract for calling the model without reading the model code itself.

We load models once at application startup rather than per request, which avoids paying model load time on every call, and we keep the model inference itself out of the async event loop when it is CPU-bound and not I/O-bound, running it in a thread pool or a separate worker so a slow inference does not block other concurrent requests to the same process.

Load models once at startup, not per request
Validate input against a Pydantic schema before it reaches the model
Run CPU-bound inference off the event loop so it does not block concurrent requests

Services

Services that use FastAPI

Industries

Where we use it

FAQ

Questions about FastAPI

Is FastAPI faster than Django for building APIs?

For I/O-bound, high-concurrency workloads, FastAPI's async model typically has an edge because a worker can handle another request while one waits on I/O. For an application that also needs an admin panel, server-rendered pages and Django's other batteries, the comparison is less about raw speed and more about which framework covers more of what the project actually needs.

Do we need an admin panel with FastAPI, and how do we get one?

FastAPI does not include one the way Django does. For projects that need both a high-throughput API and an internal admin interface, we sometimes pair a FastAPI service with a separate lightweight admin tool, or build the admin functionality as its own set of protected routes rather than expecting FastAPI to generate one automatically.

How does FastAPI generate API documentation automatically?

It reads the Pydantic models and type hints already defined on each route and builds an OpenAPI schema from them, which powers an interactive Swagger UI at a default URL. Because the documentation is generated from the same models that validate requests, it cannot drift out of sync with what the API actually accepts the way hand-written documentation can.

Is FastAPI a good fit for serving an AI model in production?

Yes, it is one of the most common choices for that specific job, mainly because Pydantic validates model input before it reaches the model and the async model handles concurrent requests well while the model itself may be running elsewhere or on a queue. The model inference step often still needs to run off the main event loop if it is CPU-bound.

What database tools work best with FastAPI?

SQLAlchemy with an async driver, or SQLModel for a lighter, Pydantic-integrated layer, are what we default to. Using a synchronous database driver inside an async FastAPI route defeats much of the framework's concurrency benefit, so we confirm the driver is genuinely async before committing to it.