We are planning to move our monolithic AI application to a microservices architecture. Many developers in my circle suggest that FastAPI is the best backend for AI applications because it is lightweight. However, Django has so many "batteries included." Is it worth giving up built-in admin panels and ORM features just for the performance gains of a micro-framework?
3 answers
For AI microservices, lean is usually better. When you're deploying models inside Docker containers, you want the smallest footprint possible to save on memory and startup time. Django carries a lot of overhead that you simply don't need if you're just serving an inference endpoint. FastAPI’s compatibility with AnyIO and its low memory usage makes it much easier to scale horizontally on Kubernetes. We use it for our computer vision service and it’s been incredibly stable under high loads compared to our old Django Rest Framework setup.
How do you handle database migrations and admin tasks without Django's built-in tools? Doesn't that add a lot of extra work?
I’d choose FastAPI every time for the native support of background tasks, which is great for long-running AI processes.
Spot on, Sharon. Background tasks are perfect for things like sending post-inference emails or updating logs without blocking the user.
We use Alembic for migrations and SQLAlchemy as the ORM, Ronald. While it takes a bit more initial setup than Django, it gives us much more granular control over our database connections, which is vital when handling high-concurrency AI tasks. For an admin panel, we either use a small library like FastAPI-Admin or just build a quick internal dashboard. The trade-off in performance and flexibility is well worth the extra bit of boilerplate.