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Build a production-shaped FastAPI service

Stop writing single-file toy APIs. Learn the project layout, the Pydantic type contracts, and the lifespan pattern that working FastAPI services actually use.

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Taught by an engineer who has shipped this

  • ISO 27001

    Led the engineering work behind the certification of a regulated EU platform.

  • Series A platform

    Architected the case-management product that became the business a €11.6M round was raised on.

  • 3x faster deploys

    Cut time-to-deploy by migrating to Kubernetes on GCP with deploy-on-merge.

Scaffold a production-shaped FastAPI service with typed endpoints, Pydantic request and response models, and a lifespan that loads a model once. Learn the layout, the type contracts, and the running story end to end.

Build a production-shaped FastAPI service with typed endpoints, Pydantic models, and a lifespan that loads a model once.

What you'll ship

Real projects, not toy demos.

  • A FastAPI service split across main, router, service, and models
  • Typed GET and POST endpoints with response models and validation
  • Pydantic request and response schemas with nested and optional fields
  • An async lifespan that loads an LLM client once and shares it
  • Dependency injection with Depends and background tasks
  • A Dockerized, reloadable development loop driven by a Makefile

What you'll learn

You finish able to:

  • Split a FastAPI app across main, router, service, and models for clarity
  • Write typed GET and POST endpoints with path and query parameters
  • Model request and response payloads with Pydantic, including nested types
  • Load expensive clients once with an async lifespan context manager
  • Share resources across routes with Depends dependency injection
  • Offload slow work with BackgroundTasks without blocking the response
  • Run the service locally with uvicorn reload and package it with Docker

Curriculum

From a blank project to a typed, containerized service.

  1. 01

    Why FastAPI

    See why typed Python changes the API story, tour the framework, and learn the project layout you will use all course

  2. 02

    Your first endpoints

    Write typed GET and POST routes with Pydantic bodies, response models, and HTTPException

  3. 03

    Type contracts with Pydantic

    Build rich Pydantic models with fields, defaults, response coercion, nested types, and Optional

  4. 04

    Lifespan and dependencies

    Load expensive clients once in a lifespan, share them with Depends, and offload work with BackgroundTasks

  5. 05

    Running it

    Run the service locally with uvicorn, expose auto-docs, add CORS, and package it with Docker

Who it's for

Is this for you?

Python developers

who want to build real HTTP services without fighting Flask boilerplate

Backend engineers

moving to typed Python and curious how FastAPI compares to Django

AI engineers

who need a production shape to wrap their model code behind an API

FAQ

Common questions.

  • Do I need async experience?

    No. The course introduces async def and await where it matters, and explains when sync is perfectly fine.

  • Why FastAPI and not Flask or Django?

    FastAPI gives you typed request and response contracts, auto-generated OpenAPI docs, and native async support without extra plugins. The course explains the tradeoff in the first lesson.

  • Is this tied to a specific LLM provider?

    No. The service pattern is generic. The lifespan example uses an LLM client because that is a common real-world case, but the same shape works for any expensive resource.