Comparing
FastAPI framework, high performance, easy to learn, fast to code, ready for production
Data validation using Python type hints
| Metric | fastapi | pydantic |
|---|---|---|
| Ecosystem | Python | Python |
| Difficulty | Beginner ✓ | Intermediate |
| Weekly downloads | 0 | 0↑ |
| Latest version | v0.135.2 | v2.12.5 |
| Recipes | 1 | 2↑ |
fastapi only
Shared
pydantic only
Use FastAPI when building REST or GraphQL APIs in Python, especially when you need async support, automatic OpenAPI/Swagger docs, request validation, or are building microservices. It's the best choice for new Python API projects.
Use Pydantic for parsing API responses, validating config files, defining request/response schemas in FastAPI, or any time you receive data from an external source and want guarantees about its shape and types.
FastAPI is API-only — it has no templating, admin interface, or ORM. For full-stack web apps with server-rendered HTML, Django is better. For tiny internal tools, Flask's smaller learning curve might be preferable.
Pydantic adds overhead on every model instantiation — not ideal for tight inner loops parsing millions of small objects. For pure config files with no user-facing validation, dataclasses or simple TypedDicts may be lighter.
fastapi
pydantic