FastAPI development for fast, well-documented Python APIs
FastAPI is a Python framework for building APIs using standard type hints. It validates requests automatically, generates interactive OpenAPI documentation and supports asynchronous code, making it a popular choice for AI and data services.
FastAPI is quick to develop with, performs well for a Python framework and produces self-documenting APIs that frontend and partner developers can use immediately. It is ideal for wrapping machine learning models or language-model workflows in a clean HTTP interface. It is deliberately lightweight, so features such as an admin panel, user management and database migrations must be added separately. For a full web application with many screens, Django or Laravel may be more efficient.
What we build with FastAPI
- APIs for machine learning models
- Language-model and retrieval services
- Microservices and internal APIs
- Data access layers for dashboards
- Webhook and integration endpoints
FastAPI questions
Is FastAPI production ready?
Yes. It is widely used in production and runs on standard ASGI servers behind a load balancer or in containers.
Does FastAPI generate API documentation?
Yes. It produces an OpenAPI specification and interactive documentation automatically from your code and type hints.
Why is FastAPI good for serving machine learning models?
FastAPI is Python-native, so models built with common data science libraries can be served directly, and its async support handles many concurrent requests efficiently. Typed inputs and outputs make prediction endpoints clear and well validated.
How does FastAPI validate incoming data?
FastAPI uses Python type hints and Pydantic models to validate and convert request data automatically, returning clear error messages when input is wrong. The same models drive the documentation.
Does FastAPI support async code?
Yes. FastAPI is built on an asynchronous foundation, so endpoints can await database calls and external APIs without blocking, which helps with I/O-heavy workloads.
Which databases can FastAPI use?
FastAPI is database-agnostic and works with PostgreSQL, MySQL, MongoDB and others through established ORMs and drivers. We pick the data layer that fits your project.
How is authentication handled in FastAPI?
FastAPI has built-in security utilities for OAuth2, bearer tokens and API keys, applied through dependencies so protection is consistent across endpoints.
How do you test a FastAPI service?
FastAPI includes a test client for calling endpoints directly, and we use pytest for unit and integration tests. Dependencies can be overridden in tests to isolate external services.
Where can a FastAPI service be deployed?
FastAPI runs behind an ASGI server and is usually containerised for cloud platforms, Kubernetes or serverless environments. We deploy into your own cloud account.
When is Django a better choice than FastAPI?
If you need a full web application with an admin interface, user management and server-rendered pages, Django provides more out of the box. FastAPI is better when the job is a focused, high-performance API.
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Have a project in mind? Let’s talk.
Tell us what you are building. We reply within one business day with next steps — and we are happy to sign an NDA first.