Comparing
Distributed task queue for async and scheduled background jobs in Python.
FastAPI framework, high performance, easy to learn, fast to code, ready for production
| Metric | celery | fastapi |
|---|---|---|
| Ecosystem | Python | Python |
| Difficulty | Intermediate | Beginner ✓ |
| Weekly downloads | 3.2M↑ | 0 |
| Latest version | v5.4.0 | v0.135.2 |
| Recipes | 3↑ | 1 |
celery only
Shared
fastapi only
Sending emails or notifications, processing uploads, running scheduled cron-style jobs, chaining multi-step workflows, fan-out parallel processing.
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.
Simple scripts that don't serve web traffic — just use asyncio or threading. If you need sub-millisecond latency, Celery's overhead is too high. For tiny projects, consider RQ (simpler) or FastAPI BackgroundTasks.
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.
celery
fastapi