Key takeaways
- FastAPI is popular for speed of development, type hints with automatic validation, async support and free interactive docs.
- Sebastián Ramírez released it in 2018; it builds on Starlette and Pydantic.
- Install with pip install "fastapi[standard]" and run fastapi dev main.py; docs appear at /docs and /redoc.
- FastAPI has no built-in ORM; SQLModel and SQLAlchemy are the usual choices.
- It runs in production at many companies; deploy with a proper server setup, migrations, logging and tests.
FastAPI is a modern Python web framework for building APIs, popular because it is fast to write and fast to run: you declare inputs with Python type hints, and FastAPI validates requests, converts data and generates interactive documentation automatically. It is the backend I used across my NextUpgrad applications, including RAG.NextUpgrad and FaceVision's embeddings service. This page covers its popularity and history, installation, the docs, how it achieves performance, ORM choices, production use, how it compares with Node.js, and FastAPI Cloud.
Why is FastAPI so popular?
FastAPI is popular because it combines very quick development with strong correctness: type hints drive request validation, editor autocompletion and documentation, while async support gives good performance. Developers write less code and catch errors earlier.
Other reasons: automatic OpenAPI documentation with interactive Swagger UI, clear error messages for invalid requests, dependency injection for things like database sessions and authentication, and excellent documentation. It also fits the AI era well: most machine learning and LLM work happens in Python, so FastAPI is the natural way to wrap models, RAG pipelines and agents in a web API. For students, its tutorial is one of the best introductions to building APIs properly.
When was FastAPI invented?
FastAPI was created by Sebastián Ramírez and first released in December 2018. It builds on Starlette for web handling and Pydantic for data validation.
Also asked as: when fastapi invented
It grew quickly over the following years as teams adopted it for machine learning services and microservices, and it became one of the most used Python web frameworks alongside Django and Flask. Its design reflected newer Python features, such as type hints and async, that older frameworks predated. The project is open source under the MIT licence and maintained on GitHub with a large community.
How do I install FastAPI?
Create a virtual environment, install FastAPI with its standard extras, write a small app and run it with the FastAPI CLI.
Also asked as: how to install fastapi · fastapi vs nodejs
pip install "fastapi[standard]"
# main.py
from fastapi import FastAPI
app = FastAPI()
@app.get("/health")
def health():
return {"status": "ok"}
fastapi dev main.py # development server with auto-reload
Open http://127.0.0.1:8000/docs to see the interactive documentation. For production, use fastapi run or a server setup such as Uvicorn with multiple workers behind a reverse proxy. Pin versions in a requirements.txt file, keep the virtual environment out of Git, and read settings such as database URLs from environment variables, so the same code runs on your laptop and in deployment.
What are FastAPI's docs?
FastAPI automatically generates an OpenAPI schema for your API and serves interactive documentation at /docs, using Swagger UI, and alternative documentation at /redoc. They update as you change your code.
Also asked as: what does fastapi docs
The docs list every endpoint, its parameters, request bodies and response models, derived from your type hints and Pydantic models, and let you send test requests from the browser. Add descriptions, examples and tags in your code to make them clearer. For public APIs you can disable them or protect them in production. The underlying OpenAPI schema, at /openapi.json, can generate client libraries for front ends and other services.
How does FastAPI achieve high performance?
FastAPI runs on ASGI, an asynchronous server interface, using Starlette and servers such as Uvicorn, so it can handle many concurrent requests efficiently when endpoints use async and non-blocking I/O. Pydantic's fast validation core adds to this.
Performance in practice depends on your code: blocking calls, such as a synchronous database driver or heavy CPU work inside an async endpoint, slow everything down. Use async database drivers or define those endpoints with plain def, which FastAPI runs in a thread pool. For CPU-heavy tasks, such as running a large model, use background workers or separate model servers. Most real bottlenecks are databases and external API calls, not the framework.
Does FastAPI have an ORM?
No. FastAPI does not include an ORM; it leaves data access to you. Most projects use SQLModel or SQLAlchemy for SQL databases, or an ODM such as Beanie for MongoDB.
Also asked as: does fastapi has orm
This keeps FastAPI focused and flexible: you choose the database layer that suits your project and can use raw SQL when needed. A common pattern is a dependency that provides a database session to each request and closes it afterwards, plus Alembic for schema migrations. Keep database models separate from API schemas when they differ, so you never accidentally expose fields like password hashes in responses.
What is the best ORM for FastAPI?
SQLModel is the most natural choice for many projects because it is made by FastAPI's author and combines SQLAlchemy and Pydantic, so one class can serve as both the database table and the API schema. SQLAlchemy 2.0 is the most powerful and flexible option for complex applications.
Choose by need:
- SQLModel: simple CRUD apps and learning; less boilerplate.
- SQLAlchemy 2.0: complex queries, large codebases, full control; async support available.
- Tortoise ORM: an async-first, Django-like ORM.
- Beanie: MongoDB with Pydantic models.
Whatever you pick, use migrations with Alembic for SQL databases, connection pooling, and tests against a real database, not only mocks.
Can FastAPI be used in production?
Yes. FastAPI is used in production by many companies and startups for APIs and machine learning services. Production readiness depends on how you deploy and operate it, not on the framework.
A production checklist:
- Run with
fastapi runor Uvicorn workers behind a reverse proxy, or in containers. - Use environment variables for configuration and secrets.
- Add structured logging, metrics and health checks.
- Validate inputs strictly and set request size and timeout limits.
- Handle authentication and rate limiting.
- Run database migrations and keep backups.
- Write tests with FastAPI's TestClient and run them in CI.
RAG.NextUpgrad and my other NextUpgrad apps shipped this way, with Docker, CI/CD, structured logging and metrics.
FastAPI vs Node.js: which should I use?
Use FastAPI when your backend is close to Python's ecosystem, such as machine learning, data processing, RAG and AI agents. Use Node.js when your team works in JavaScript or TypeScript across front end and back end, or you rely on its large web ecosystem.
Both handle high concurrency well with async I/O. Node.js frameworks such as Express, Fastify and NestJS are mature and widely used; TypeScript gives strong typing comparable to FastAPI's type hints. FastAPI's automatic validation and docs are excellent out of the box. Many teams use both: a Next.js front end with its own server routes, and a FastAPI service for AI features, communicating over a clean API.
Is FastAPI Cloud free?
FastAPI Cloud is a deployment platform from the team behind FastAPI, introduced to make deploying FastAPI apps simple. Its availability, plans and any free tier depend on its current offering, so check its official website.
FastAPI itself is free and open source, and you can deploy it on many platforms with free tiers for small projects, such as container hosts and serverless platforms that support Python. For student projects, a free tier on a platform such as Render, Railway or a cloud provider's free allowance is usually enough. Watch for cold starts on free tiers, keep secrets in environment variables, and add a health endpoint so the platform can check your app.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
