DRF vs FastAPI -- An Honest Comparison From Someone Who Ships Both

I can usually tell which framework the author uses by reading the first paragraph of a "DRF vs FastAPI" comparison. If DRF gets all the nuance ("well, it depends on your use case, DRF's serializers handle edge cases that...") and FastAPI gets the dismissive summary ("it's fast but lacks maturity"), the author ships DRF. Flip the nuance, flip the author.

I ship both. Not because I am a framework diplomat, but because we run five production APIs on DRF and three on FastAPI at CODERCOPS, and the honest truth is that neither framework is better -- they are better at different things. The Django REST Framework comparison posts that declare a winner are optimizing for engagement, not for helping you make a good decision.

This post is the comparison I wanted to read before I started using both. No benchmarks designed to make one look bad. No "just use X" conclusions. Just the trade-offs, with enough detail for you to map them onto your specific situation.

The Philosophical Difference (It Matters More Than You Think)

Before we compare features, understand the design philosophies. This is not fluff -- it explains almost every trade-off between the two.

Django REST Framework is built on Django's philosophy: convention over configuration. It gives you serializers, viewsets, routers, authentication classes, permission classes, throttling, pagination, and filtering out of the box. The cost is that you learn Django's way of doing things.

FastAPI is built on a different philosophy: explicit over implicit. It gives you a request/response cycle with automatic validation via Pydantic, and gets out of your way. You build the rest yourself (or pick libraries).

Here is the same endpoint in both:

DRF:

# serializers.py
class ProjectSerializer(serializers.ModelSerializer):
    class Meta:
        model = Project
        fields = ["id", "title", "description", "status", "created_at"]
        read_only_fields = ["id", "created_at"]

# views.py
class ProjectViewSet(viewsets.ModelViewSet):
    queryset = Project.objects.all()
    serializer_class = ProjectSerializer
    permission_classes = [IsAuthenticated]
    filterset_fields = ["status"]
    search_fields = ["title", "description"]
    ordering_fields = ["created_at", "title"]
    pagination_class = PageNumberPagination

# urls.py
router = DefaultRouter()
router.register("projects", ProjectViewSet)
urlpatterns = router.urls

That is ~20 lines for a full CRUD API with authentication, filtering, search, ordering, pagination, and automatic URL routing for list, create, retrieve, update, and delete. The ModelViewSet generates five endpoints from one class.

FastAPI:

# schemas.py
class ProjectCreate(BaseModel):
    title: str = Field(max_length=200)
    description: str
    status: str = "active"

class ProjectResponse(BaseModel):
    id: int
    title: str
    description: str
    status: str
    created_at: datetime

    model_config = ConfigDict(from_attributes=True)

# main.py
@app.get("/projects/", response_model=list[ProjectResponse])
async def list_projects(
    status: str | None = None,
    search: str | None = None,
    order_by: str = "-created_at",
    page: int = 1,
    page_size: int = 20,
    db: AsyncSession = Depends(get_db),
    user: User = Depends(get_current_user),
):
    query = select(Project)
    if status:
        query = query.where(Project.status == status)
    if search:
        query = query.where(
            or_(
                Project.title.ilike(f"%{search}%"),
                Project.description.ilike(f"%{search}%"),
            )
        )
    # ... ordering, pagination logic
    result = await db.execute(query.offset((page - 1) * page_size).limit(page_size))
    return result.scalars().all()

@app.post("/projects/", response_model=ProjectResponse, status_code=201)
async def create_project(
    project: ProjectCreate,
    db: AsyncSession = Depends(get_db),
    user: User = Depends(get_current_user),
):
    db_project = Project(**project.model_dump(), owner_id=user.id)
    db.add(db_project)
    await db.commit()
    await db.refresh(db_project)
    return db_project

# ... retrieve, update, delete endpoints (another ~40 lines each)

That is ~40 lines for just list and create, and you still need retrieve, update, and delete. Total for full CRUD: ~120 lines.

DRF gave you 5 endpoints in 20 lines. FastAPI needs ~120 lines for the same thing.

But here is the counter-argument that FastAPI advocates will (correctly) make: every line in the FastAPI version is explicit. You can read the code and know exactly what query is being executed, what filtering logic is applied, and how pagination works. In the DRF version, ModelViewSet does a lot behind the scenes, and understanding exactly what happens on a PATCH request to /projects/5/ requires knowing DRF's internals.

Both arguments are valid. The question is which trade-off you prefer.

The Performance Question

Let me get this out of the way because it dominates every comparison post: yes, FastAPI is faster. No, it probably does not matter for your use case.

Here are benchmarks from our production systems, not synthetic "hello world" tests:

Scenario	DRF (gunicorn, 4 workers)	FastAPI (uvicorn, 4 workers)	Difference
Simple JSON response (no DB)	4,200 req/s	8,900 req/s	FastAPI 2.1x
Single DB query (list 20 items)	1,800 req/s	2,100 req/s	FastAPI 1.2x
Complex query (joins, filters)	420 req/s	480 req/s	FastAPI 1.1x
Response with serialization (nested)	650 req/s	780 req/s	FastAPI 1.2x
File upload + processing	85 req/s	92 req/s	FastAPI 1.1x

The "2.1x faster" headline comes from the no-database scenario, which is meaningless for real applications. Once you add a database query -- which every real endpoint has -- the difference shrinks to 10-20%. The database is the bottleneck, not the framework.

At 1,800 requests per second, DRF handles 155 million requests per day on four workers. If you are hitting that limit, your problem is not the framework -- it is your database, your network, or your architecture. Switching from DRF to FastAPI will not solve it.

When performance actually matters: high-throughput async workloads where the bottleneck is I/O concurrency, not raw request handling. If you are aggregating data from 5 external APIs per request, FastAPI's native async gives you genuine concurrency benefits. But Django 6.0 has async views too, so the gap is narrowing.

Developer Experience: The Things That Matter Daily

Performance benchmarks get clicks, but developer experience determines productivity. Here is what working with each framework actually feels like:

Type Safety and Validation

FastAPI's biggest win. Pydantic models for request/response validation are elegant, fast, and integrate with IDE autocompletion. You define a schema, and FastAPI validates input, generates OpenAPI docs, and provides type hints -- all from one source of truth.

DRF's serializers are powerful but older in design. They predate Python type hints, so you define fields imperatively instead of declaratively. DRF 4.x (currently in development) brings Pydantic-style type hints, but as of February 2026, most teams are on DRF 3.x with its traditional serializer syntax.

Edge case where DRF wins: nested write operations. Creating a project with inline team member assignments in a single POST request? DRF's serializers handle this with create() and update() method overrides. In FastAPI, you write this logic manually in every endpoint that needs it.

Documentation

Both generate OpenAPI/Swagger docs automatically. FastAPI's are slightly better out of the box because the type hints translate directly to schema definitions. DRF's drf-spectacular achieves parity, but it is a third-party package and requires configuration.

Authentication

DRF wins decisively. Django's auth system is built-in: session auth, token auth, OAuth (via django-oauth-toolkit), SAML, LDAP. DRF wraps all of these with a unified authentication_classes interface.

FastAPI has no built-in auth. You use python-jose for JWT, authlib for OAuth, and wire it together yourself with dependency injection. It works, but you are assembling it from parts instead of using an integrated system.

For most web applications, authentication is table stakes, not a differentiator. DRF gives you this for free. FastAPI makes you build it.

Database Integration

DRF + Django ORM is a complete package. Migrations, admin interface, queryset API, model relationships, database-level constraints. You define a model and get a migration, an admin page, and an API endpoint with minimal code.

FastAPI + SQLAlchemy is powerful but requires more manual setup. Alembic for migrations (less automatic than Django's makemigrations), no admin interface (unless you add SQLAdmin), and more explicit query construction.

FastAPI + Tortoise ORM is closer to the Django experience but less mature and with a smaller ecosystem.

The Ecosystem Gap

This is where the comparison gets practical. Here is what you need for a typical production API and how each framework provides it:

Need	DRF Solution	FastAPI Solution
CRUD operations	ModelViewSet (built-in)	Manual endpoints
Filtering	django-filter (mature)	Manual query params or fastapi-filter
Search	SearchFilter (built-in)	elasticsearch-py or manual
Pagination	PageNumberPagination (built-in)	Manual or fastapi-pagination
Admin interface	Django Admin (built-in)	sqladmin (third-party)
Migrations	makemigrations / migrate (built-in)	Alembic (manual)
Auth: Session	Built-in	Manual
Auth: JWT	djangorestframework-simplejwt	python-jose + manual
Auth: OAuth	django-oauth-toolkit	authlib
Rate limiting	UserRateThrottle (built-in)	slowapi (third-party)
Caching	Django cache framework (built-in)	fastapi-cache or manual
Background tasks	Django Tasks framework (built-in)	arq, celery, or BackgroundTasks (basic)
File uploads	FileField + storage backends	python-multipart + manual
Email	django.core.mail (built-in)	fastapi-mail or manual
Testing	TestCase, APIClient (built-in)	httpx + pytest
WebSockets	Django Channels	Built-in WebSocket class

Count the "built-in" vs. "manual" entries. That is the ecosystem gap. DRF gives you a production-ready API with less assembly. FastAPI gives you more control but more assembly.

When We Choose DRF

At CODERCOPS, we default to DRF when:

1. The project is a full web application, not just an API. Django gives you templates, static files, admin, auth, forms, and an ORM. If you need any of these (and most projects do), starting with FastAPI means either adding Django-like functionality piecemeal or running two frameworks.

2. The data model is complex with many relationships. Django's ORM handles complex model relationships (many-to-many, polymorphic, multi-table inheritance) better than SQLAlchemy for typical web application patterns. When you have 15 models with relationships, DRF's serializers and viewsets save enormous amounts of time.

3. The team is primarily Django developers. This sounds obvious, but it matters more than people admit. A team that knows Django's patterns will ship faster with DRF than with FastAPI, even if FastAPI is theoretically "simpler." Familiarity compounds.

4. You need an admin interface. Django Admin is one of the most underrated features in web development. For internal tools, admin panels, and content management, nothing in the Python ecosystem comes close. If your project has any "back office" needs, DRF with Django Admin saves weeks of development.

5. The project will grow beyond just an API. Projects have a way of expanding. "Just an API" becomes "an API plus a webhook handler plus a background job processor plus an admin dashboard." Django handles all of these. FastAPI handles the first one.

When We Choose FastAPI

We reach for FastAPI when:

1. The project is a pure API with no web interface. If you are building a microservice that receives JSON and returns JSON, with no admin, no templates, no forms, FastAPI's minimalism is a feature, not a limitation. You do not pay for Django's batteries you are not using.

2. Async I/O is a core requirement. An API that aggregates data from multiple upstream services, a WebSocket server, a streaming endpoint for LLM responses. FastAPI was built async-first, and while Django supports async, FastAPI's async story is more mature and idiomatic.

3. ML model serving is the primary function. When the API's job is to accept input, run inference, and return a prediction, FastAPI's simplicity shines. No ORM, no admin, no middleware stack -- just a thin HTTP layer around your model. (Though as we discuss in Django as your AI backend, if the ML features are part of a larger Django app, keeping them in Django is often better.)

4. The team is TypeScript-heavy and coming from Express.js. FastAPI's decorator-based routing and type-first approach feels familiar to developers coming from Express or NestJS. The learning curve from TypeScript to FastAPI is gentler than from TypeScript to DRF.

5. You need the fastest possible prototype. For a hackathon, a proof of concept, or a v0 that will be rewritten, FastAPI gets you from zero to deployed API faster. Fewer decisions, fewer files, less ceremony.

The Hybrid Approach (What We Actually Recommend)

Here is the thing nobody says in framework comparison posts: you do not have to choose one.

We run several projects where Django handles the web application, admin, auth, and background jobs, and FastAPI handles a specific high-performance API endpoint or ML inference service. They share a database and deployment infrastructure.

# docker-compose.yml -- the "framework polygamy" approach
services:
  django:
    build: ./django-app
    command: gunicorn myproject.wsgi --workers 4 --bind 0.0.0.0:8000
    ports:
      - "8000:8000"
    environment:
      - DATABASE_URL=postgres://db:5432/myapp

  fastapi-ml:
    build: ./ml-service
    command: uvicorn main:app --workers 2 --bind 0.0.0.0:8001
    ports:
      - "8001:8001"
    environment:
      - DATABASE_URL=postgres://db:5432/myapp
    deploy:
      resources:
        limits:
          memory: 2G  # ML models need more RAM

  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf
    depends_on:
      - django
      - fastapi-ml

# nginx.conf -- route by path
upstream django {
    server django:8000;
}

upstream fastapi {
    server fastapi-ml:8001;
}

server {
    listen 80;

    # ML inference endpoints go to FastAPI
    location /api/v1/inference/ {
        proxy_pass http://fastapi;
    }

    # Everything else goes to Django
    location / {
        proxy_pass http://django;
    }
}

This is not a cop-out or a failure to decide. It is using each framework where it excels. Django for the 80% of your application that is standard web development. FastAPI for the 20% that needs async performance or minimalist API serving.

The important nuance: this only makes sense when the FastAPI service is genuinely different in character from the Django app. If you are splitting just because "FastAPI is faster for APIs," you are adding operational complexity for a 10-20% throughput improvement. Not worth it.

The Decision Framework

Here is how we actually decide on new projects. Not a flowchart (those always oversimplify), but a weighted checklist:

Strong DRF signals (3+ of these = use DRF):

• Project has a web interface beyond the API
• Complex data model with 10+ related models
• Need Django Admin for internal management
• Team has Django experience
• Auth requirements beyond simple JWT (SSO, SAML, social auth)
• Background tasks are part of the application
• Project will likely grow beyond its initial scope

Strong FastAPI signals (3+ of these = use FastAPI):

• Pure API with no web interface
• Async I/O is central to the workload
• ML inference is the primary function
• Team comes from TypeScript/Express background
• Microservice that does one thing
• WebSocket-heavy application
• Speed of initial development is critical (prototype/MVP)

If you score high on both lists: use the hybrid approach. Django for the application, FastAPI for the specialized endpoints.

If you are genuinely undecided: use DRF. It has a larger ecosystem, more community resources, and handles scope creep better. You can always extract a FastAPI microservice later. Going the other direction -- adding Django's features to a FastAPI app -- is much harder.

That is the one opinion I will state plainly: when in doubt, start with Django. Not because it is better, but because it handles the widest range of "things we did not plan for" that every project eventually encounters.

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This is the final post in our Django in 2026 series. Previously: Django as your AI backend. Start from the beginning: Django 6.0's Tasks Framework.

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Building a Python API and trying to choose the right framework? At CODERCOPS we have shipped production applications on both DRF and FastAPI -- individually and side by side. We will give you an honest recommendation based on your team, your requirements, and your timeline. Reach out to chat, or explore our other engineering deep dives for more battle-tested patterns.