Django vs FastAPI: When to Use Which in 2026

The Short Answer

Use Django when you need a full-featured web application with admin panels, ORM, authentication, and a mature ecosystem. Use FastAPI when you need high-performance async APIs, microservices, or real-time data processing. In my development practice, I use both regularly — the choice depends on the project.

Architecture Comparison

Django is a "batteries-included" framework. FastAPI is a modern, minimal framework built on Starlette and Pydantic.

# Django: Everything built in
# settings.py -> urls.py -> views.py -> models.py -> templates/
# ORM, admin, auth, sessions, CSRF, migrations -- all included

# FastAPI: Assemble what you need
# main.py -> routers/ -> schemas/ -> dependencies/
# You choose: SQLAlchemy/Tortoise ORM, your auth lib, etc.

Performance Benchmarks

FastAPI is faster in raw request throughput, especially for I/O-bound operations:

# Simple JSON endpoint benchmarks (requests/sec):
# FastAPI (uvicorn, 4 workers):  ~12,000 req/s
# Django (gunicorn, 4 workers):  ~3,500 req/s
# Django + async views:          ~6,000 req/s

# But for real applications with DB queries:
# FastAPI + SQLAlchemy async:    ~2,800 req/s
# Django + ORM:                  ~2,200 req/s
# The gap narrows significantly with real workloads

When Django Wins

• Admin panel — Django Admin is unmatched for back-office tools
• ORM with migrations — Alembic works, but Django migrations are more mature
• Full web apps — templates, forms, sessions, CSRF protection
• Rapid prototyping — from zero to working product in days
• Large ecosystem — DRF, Celery integration, thousands of packages

# Django: From model to full CRUD API in minutes
class Product(models.Model):
    name = models.CharField(max_length=200)
    price = models.DecimalField(max_digits=10, decimal_places=2)
    category = models.ForeignKey(Category, on_delete=models.CASCADE)

class ProductSerializer(serializers.ModelSerializer):
    class Meta:
        model = Product
        fields = '__all__'

class ProductViewSet(viewsets.ModelViewSet):
    queryset = Product.objects.all()
    serializer_class = ProductSerializer
    # Full CRUD with filtering, pagination, auth.

When FastAPI Wins

• High-concurrency APIs — async/await for parallel I/O operations
• Microservices — lightweight, fast startup, minimal overhead
• Real-time features — WebSocket support is first-class
• Type safety — Pydantic models provide automatic validation and docs
• Auto-generated docs — OpenAPI/Swagger UI out of the box

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class ProductCreate(BaseModel):
    name: str
    price: float
    category_id: int

@app.post("/products/", response_model=ProductResponse)
async def create_product(product: ProductCreate):
    # Automatic request validation via Pydantic
    # Automatic OpenAPI documentation
    db_product = await Product.create(**product.dict())
    return db_product

My Decision Framework

# Use Django when:
# * You need an admin panel
# * Building a monolithic web app
# * You need rapid prototyping
# * Your team knows Django
# * You need DRF ecosystem

# Use FastAPI when:
# * Building a microservice
# * You need async I/O (external APIs, WebSockets)
# * Performance is critical
# * You want auto-generated API docs
# * Building an ML/AI serving layer

Can You Use Both?

Yes! In several of my projects, Django handles the main application (admin, business logic, templates) while FastAPI runs specific high-performance services. For example, in an AI aggregator project, Django managed user accounts and billing, while FastAPI handled the async AI model routing.

The best framework is the one that solves your problem with the least complexity. Want help choosing the right stack for your project? Let's discuss it.