Django or FastAPI? The six things we look at before deciding

“Django or FastAPI” is usually the wrong question. They are not competitors; they are answers to two different problems. Django answers “I need to build a whole web product end to end.” FastAPI answers “I need a fast, type-safe API layer.”

The six sections below are the criteria we actually use when starting a project, in order of weight.

1. The shape of the product

This alone decides half the question.

Django wins decisively when the product contains user accounts, permissions, an admin interface and form flows. It brings all of that out of the box. You can stand up an internal operations tool in three days with Django; doing the same in FastAPI means hand-building everything from authentication to the admin UI.

FastAPI wins where the interface is provided by another layer — React, Next.js, a mobile app — and the backend only returns data. The backend of a mobile app, a payment integration service, a model inference layer: this is FastAPI’s natural territory.

Practical rule: if an admin panel is required, Django. If not, probably FastAPI.

2. Concurrency and real-world performance

FastAPI is fast, but on most projects that speed does not come from where people think it does.

The framework’s own processing time is a tiny fraction of a typical request. The real time goes into database queries, external API calls and template rendering. If your page returns in 400 ms and 380 ms of that is the database, changing framework buys you a slice of the remaining 20 ms.

FastAPI’s genuine advantage shows up in concurrency: during long-running external calls — LLM requests, third-party APIs, file processing — the async model carries far more simultaneous connections on the same hardware. In an AI workflow, not having the server idle while waiting on the model is a serious difference.

Django has supported async views since 4.1, but async support in the ORM is still not at the maturity of FastAPI plus SQLAlchemy. Where the workload is heavily async, that gap is felt.

3. The data layer

Django’s ORM is a productivity engine: migrations are part of the framework, the model is defined in one place, and the admin panel is generated from it. Complex queries occasionally push you down to raw SQL, but it comfortably covers 90% of daily work.

In FastAPI the data layer is your choice, which in practice means SQLAlchemy 2.0 plus Alembic. More setup work, more control in return. For complex relational queries and type safety, SQLAlchemy 2.0’s modern API is genuinely good.

If you plan to use vector search (pgvector), time series or unusual data models, SQLAlchemy’s flexibility makes life easier.

4. Type safety and the API contract

FastAPI leads clearly here, and it matters more as the team grows.

FastAPI generates the OpenAPI schema automatically from Pydantic models. The practical consequence: the frontend team can generate its client from the schema, a contract change produces a compile error, and the documentation stays current with the code. Most of the “backend renamed a field and nobody said anything” class of bug disappears.

You can reach the same outcome on the Django side with Django REST Framework plus drf-spectacular, but that is an extra layer and an extra maintenance burden.

5. Team and handover

Django’s imposed structure looks like a constraint and is actually a gift. Django projects resemble one another: where models.py lives, what views.py does, where settings sit — all predictable. A new developer inheriting the project finds their way in a day.

FastAPI imposes nothing. A well-organised FastAPI project is a pleasure; a badly organised one is a pile of files that is hard to navigate. In a small experienced team that freedom is an advantage. In a growing team, or one that changes hands often, it is a risk.

For a product we will deliver to a client and that another team may inherit, this point alone can swing the decision to Django.

6. Maintenance cost

Django’s LTS releases get three years of security support and the upgrade path is well documented. On long-lived internal systems with a low rate of change, that is a real advantage.

FastAPI moves fast; version transitions are usually smooth, but you are the one holding the ecosystem — SQLAlchemy, Pydantic, Alembic — together. The Pydantic v1 → v2 migration was an unbudgeted cost for a lot of teams.

Decision table

Situation	Our pick
Admin panel and content management needed	Django
Backend for a mobile app or SPA	FastAPI
Long external calls, LLM workflows	FastAPI
Complex permission and role model	Django
Commerce, orders, invoicing flows	Django
High-concurrency, lightweight service	FastAPI
Small team, long-lived product, will be handed over	Django
Thin API layer added to an existing system	FastAPI

Using both

The third option we reach for often: Django as the main product, FastAPI as a side service.

Orders, users and the admin panel stay in Django; AI inference, document processing or endpoints that need heavy concurrency move to a separate FastAPI service. The two share the same PostgreSQL database or a message queue.

This removes the “which is better” question entirely and lets you use the strengths of both. The extra cost is deploying and monitoring two services — and with a properly built CI/CD pipeline that cost is small.

In short

Framework choice is not a matter of identity; it is constraint matching. Look at the shape of the product, account for the size of the team, and think about who will read this code in five years.

If you are undecided and your product includes an admin panel, start with Django. Adding a FastAPI service later is easy. Hand-writing everything Django gives you is not.

If you need to make this call on your own project, write to us — we will look at your scope and give you a reasoned recommendation.