Modern Python framework for building APIs - and yes, it's actually fast
We switched to FastAPI from Flask a while back. Best decision. It's faster, cleaner code, and the automatic documentation alone saves us hours. If you're building APIs in Python, this is what you should use. Period.
FastAPI is modern Python web framework for building APIs. Unlike older frameworks like Flask or Django, FastAPI was designed from scratch to be fast, easy to use, and production-ready. It uses Python's type hints to provide automatic validation, documentation, and better code quality. If you're building an API in Python today, FastAPI is probably your best choice.
What makes FastAPI special is the combination of speed and developer experience. It's one of the fastest Python frameworks available - comparable to Node.js and Go frameworks. This is because it uses Starlette for the web parts and Pydantic for data validation, both of which are highly optimized. The async/await support means your API can handle many concurrent requests without blocking.
The automatic API documentation is honestly game-changing. As you write your endpoints with type hints, FastAPI generates interactive Swagger UI and ReDoc documentation automatically. You can test your API directly in the browser without Postman. Documentation always stays in sync with your code because it's generated from the code. This alone saves hours of work.
FastAPI uses Pydantic for data validation. You define your request and response models as Python classes with type hints. FastAPI validates incoming requests automatically, gives clear error messages if validation fails, and serializes responses correctly. This catches so many bugs before they reach production. Coming from Flask where you had to validate everything manually, this is huge improvement.
We use FastAPI for all new Python APIs. It's replaced Flask and Django REST Framework in our stack. The performance is better, the code is cleaner, and development is faster. Our clients are happy because APIs respond quickly and are well-documented. Our developers are happy because FastAPI is pleasant to work with. Win-win situation.
One of the fastest Python frameworks, comparable to Node.js
Clean code, less bugs, happy developers
Python typing helps catch errors at development time
Interactive API documentation generates automatically
Built-in async support for handling many concurrent connections efficiently
Automatic request validation using Python type hints, catches errors early
Swagger UI and ReDoc generated automatically from your code
Uses Python 3.8+ features like type hints for better code quality
Built-in test client makes API testing straightforward
If you know Python, you can learn FastAPI in a few days
We deploy FastAPI applications to AWS in two main ways. Docker containers on ECS is most common for regular APIs. Lambda is great for sporadic traffic or cost optimization.
Package FastAPI app in Docker container. Deploy to AWS ECS for automatic scaling and management. This is our most common deployment method for FastAPI apps.
Run FastAPI as serverless function using AWS Lambda with Mangum adapter. Pay only for actual usage. Perfect for APIs with variable traffic or low usage.
Our typical FastAPI deployment pipeline runs tests, builds Docker image, pushes to registry, and deploys to AWS. Entire process takes 3-5 minutes from code push to production.
Push to GitHub. CI runs pytest, mypy type checking, and linting automatically.
Docker image built with FastAPI app, dependencies, and configuration.
Image pushed to AWS ECR with semantic version tag and latest tag.
ECS rolling update with health checks. Old containers replaced gracefully.
FastAPI apps packaged in Docker with Python 3.11, dependencies, and Uvicorn server. Multi-stage builds for smaller images.
Use SQLAlchemy async for PostgreSQL. Connection pooling and proper session management included.
Redis caching for frequent queries. Async Redis client integrated with FastAPI dependency injection.
These are actual client projects where FastAPI solved real problems. Real challenges, real solutions, real results.
Client had Flask API that couldn't handle 10k+ concurrent users. Response times were 2-3 seconds under load. Database connections maxing out.
Migrated to FastAPI with async database driver (asyncpg). Deployed on ECS with auto-scaling. Added Redis caching for frequent queries. Implemented connection pooling.
Response times under 100ms. Handles 50k+ concurrent users. 70% reduction in AWS costs due to better resource usage. Client very happy.
Data science team had ML models in Jupyter notebooks. Needed production API to serve predictions. Models took 5-10 seconds to load.
Built FastAPI wrapper around ML models. Used async endpoints for concurrent requests. Loaded models at startup. Deployed on ECS with GPU support.
Models serve predictions in under 200ms. Can handle 1000+ predictions per second. Automatic API docs help frontend team integrate.
Monolithic Django app was slow to deploy and hard to scale. Every change required full redeployment. Teams blocked each other.
Split into microservices using FastAPI. Each service in own Docker container. Independent deployment pipelines. Used API gateway for routing.
Deploy times from 30 minutes to 3 minutes. Teams deploy independently 10+ times per day. Individual services scale based on load.
Backend services for mobile and web apps with clean, documented endpoints
Small focused services that do one thing well and scale independently
Handling and transforming large amounts of data quickly with async workers
Serving machine learning models to applications with low latency predictions
Performance: It's genuinely fast. Not "fast for Python" - actually fast compared to any framework in any language. Async support means it handles lots of concurrent requests without breaking a sweat. We've seen FastAPI APIs handle 50k+ concurrent connections on modest hardware.
Developer Experience: Writing FastAPI code is pleasant. The type hints help your IDE give better suggestions and catch errors as you type. The automatic validation catches bugs before they hit production. The auto-generated documentation is always accurate because it's generated from your code. All these small things add up to big productivity gains.
Production Ready: FastAPI is mature and battle-tested. Companies like Uber, Microsoft, and Netflix use it in production. The ecosystem is excellent with libraries for everything - databases, caching, authentication, testing. When something goes wrong, you can find solutions quickly because community is large and active.
When to Use: If you're building an API in Python, just use FastAPI. Unless you have a specific reason not to (like needing Django admin panel), it's probably the best choice right now. We've migrated multiple projects from Flask to FastAPI and never regretted it. Faster performance, cleaner code, happier developers.
We've built dozens of APIs with FastAPI. Some handle millions of requests per month. Never had performance issues. Never looked back at Flask or Django REST Framework. That's our experience after 3+ years using it in production.