Why I Bet on FastAPI Over Django (After 10 Years of Using It)

A senior developer's honest take on switching from Django to FastAPI in 2025

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The Switch That Surprised Me

Ten years ago, I started my career building web applications with Django. It was 2015, Django was mature, and the ecosystem was rich. I built everything from e-commerce platforms to SaaS dashboards, and Django served me well. The admin panel was magic, the ORM was intuitive, and the "batteries included" philosophy meant I could ship fast.

But today? Almost everything I build – whether it's client work or my own products – runs on FastAPI.

This isn't a "Django is dead" story. It's about choosing the right tool for how we build software in 2025. Let me show you the data, share my real experience, and explain why this shift happened.

The Numbers Don't Lie

First, let's look at the momentum:

GitHub Stars (September 2025):

• FastAPI: ~89,300 ⭐
• Django: ~84,900 ⭐
• Flask: ~70,300 ⭐

Release Timeline:

• Django: July 2005 (20 years old)
• FastAPI: December 2018 (7 years old)

FastAPI overtook Django in GitHub stars despite being 13 years younger. That's not just hype – that's developers voting with their commits.

Performance Reality:
According to TechEmpower benchmarks, Uvicorn/Starlette (FastAPI's foundation) consistently ranks among the fastest Python web frameworks. While real-world performance depends on your specific use case, the foundation matters when you're building API-heavy applications.

Async-First vs. Async-Added

This is where the philosophical difference becomes practical.

FastAPI was built on ASGI, Starlette, and Pydantic from day one. Everything is async-native:

# FastAPI - async is natural
@app.post("/process-data")
async def process_data(data: DataModel):
    result = await external_api_call(data)
    await save_to_database(result)
    return {"status": "processed"}

Django added async support in version 3.0, and it's genuinely good today. But it's still layered on top of a sync-first architecture:

# Django - async works but feels added-on
@sync_to_async
def get_user_sync(user_id):
    return User.objects.get(id=user_id)

async def process_user(request):
    user = await get_user_sync(request.user.id)
    # Mix of sync/async patterns

My Experience: When building IO-heavy applications – APIs that call external services, Slack bots, streaming endpoints, cloud integrations – FastAPI feels cleaner. The async patterns are consistent throughout the stack.

Type Hints as a Superpower

This might be FastAPI's killer feature: it treats Python type hints as a first-class citizen.

FastAPI leverages types for:

• Request/response validation
• Automatic API documentation
• Editor autocompletion
• Runtime parsing

# FastAPI - types do the work
from pydantic import BaseModel

class CreateUserRequest(BaseModel):
    email: str
    name: str
    age: int

@app.post("/users")
async def create_user(user: CreateUserRequest):
    # Automatic validation, docs, and IDE support
    return {"user_id": 123, "email": user.email}

Django doesn't leverage type hints for validation or documentation:

# Django - manual validation
def create_user(request):
    data = json.loads(request.body)
    # Manual validation needed
    if 'email' not in data or '@' not in data['email']:
        return JsonResponse({'error': 'Invalid email'})
    # ... more manual checks

Concrete Benefit: I spend less time debugging "what's this payload shape again?" issues. The types are the documentation, and they're enforced at runtime.

Batteries-Included vs. Pick-Your-Stack

This is the classic trade-off, and both approaches have merit.

Django ships with:

• ORM (Django Models)
• Admin interface
• Authentication system
• Form handling
• Template engine
• URL routing

FastAPI is minimal:

• You choose your ORM (SQLAlchemy, SQLModel, Tortoise)
• You pick your admin solution
• You select auth strategies
• You decide on task queues

The Trade-off:

• Django is faster for CRUD-heavy back offices and content management
• FastAPI is better for API-first products where you want control over your stack

My Take: For internal tools and admin-heavy applications, Django's batteries are still unmatched. But for modern SaaS backends, I prefer choosing my components.

Real-World Usage

FastAPI isn't just for side projects. Here's who's using it:

Netflix uses FastAPI for Dispatch, their incident management system. When Netflix has an outage, FastAPI helps coordinate the response.

Uber leverages FastAPI in Ludwig for serving machine learning models. High-performance ML inference demands async capabilities.

Microsoft uses FastAPI for several Azure services and internal tools.

Django still powers massive applications:

• Instagram's backend (though they've heavily customized it)
• Mozilla's websites
• The Washington Post
• Pinterest (parts of it)

Both frameworks have proven they can scale. The question is which one fits your project better.

Developer Velocity: My Personal Experience

After building with both frameworks extensively, here's where I see differences:

Scaffolding Speed:

• FastAPI: Less boilerplate, more explicit composition
• Django: More setup, but more decisions made for you

API Documentation:

• FastAPI: Auto-generated OpenAPI docs that developers actually use
• Django: DRF can generate docs, but it requires more setup

Testing:

• FastAPI: httpx + pytest feels natural and fast
• Django: Great testing framework, but more ceremony

Example - Simple POST Endpoint:

# FastAPI (8 lines)
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

@app.post("/items/")
async def create_item(item: Item):
    return {"item": item, "status": "created"}

# Django + DRF (20+ lines across multiple files)
# models.py
from django.db import models
class Item(models.Model):
    name = models.CharField(max_length=100)
    price = models.DecimalField(max_digits=10, decimal_places=2)

# serializers.py
from rest_framework import serializers
class ItemSerializer(serializers.ModelSerializer):
    class Meta:
        model = Item
        fields = '__all__'

# views.py
from rest_framework import generics
class CreateItemView(generics.CreateAPIView):
    serializer_class = ItemSerializer

# urls.py
from django.urls import path
urlpatterns = [path('items/', CreateItemView.as_view())]

The Caveat: Django's admin interface still rules for internal tools. If you need a quick admin panel, Django wins hands down.

Performance Realities

Let's be honest about performance:

CPU-bound workloads: Both frameworks hit the same Python bottleneck. If you're doing heavy computation, the framework choice won't save you.

IO-bound workloads: This is where FastAPI shines. Async concurrency handles database calls, API requests, and file operations more efficiently.

Real numbers from my experience:

• API serving mostly database queries: Similar performance
• API with external service calls: FastAPI handled 3x more concurrent requests
• WebSocket connections: FastAPI's async nature made this much simpler

Django ASGI is improving rapidly, but FastAPI's defaults are simpler to configure and reason about.

Migration Notes

If you're a Django developer considering FastAPI:

Good news: The learning curve is gentle. You already understand web concepts, ORMs, and Python patterns.

Key difference: Django uses "magic" (conventions, metaclasses, implicit behavior). FastAPI uses explicit composition. You make more decisions upfront, but the code is more predictable.

Team considerations:

• Junior developers might prefer Django's conventions
• Senior developers often appreciate FastAPI's explicitness
• Both have excellent documentation and community support

Decision Framework

Here's how I choose between them today:

Choose Django when:

• Building a monolith with admin interface needs
• Team prefers conventions over configuration
• Content-heavy applications (CMS, blogs, e-commerce)
• Rapid prototyping of CRUD applications
• You need Django's mature ecosystem (packages, hosting, etc.)

Choose FastAPI when:

• Building API-first services
• Async workflows are central to your app
• You want modern Python patterns (type hints, async/await)
• Machine learning or data science integration
• Microservices architecture
• You prefer explicit over implicit

Deployment in 2025

Both frameworks deploy well, but the patterns differ:

FastAPI:

# Simple container deployment
uvicorn main:app --host 0.0.0.0 --port 8000
# Or with Gunicorn for production
gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker

Django:

# Traditional deployment
gunicorn myproject.wsgi:application
# Or ASGI for async features
daphne myproject.asgi:application

Both work great with Docker, Kubernetes, and modern hosting platforms. FastAPI's async nature can be more resource-efficient for IO-heavy workloads.

The Honest Conclusion

Django isn't obsolete. It's still rock-solid for many use cases, and the ecosystem is unmatched for certain types of applications.

But for API-first products in 2025, FastAPI gives me:

• Faster iteration cycles
• Cleaner developer experience
• Better async patterns
• Fewer integration headaches
• More predictable code

That's why I bet on it for my new projects.

The real winner? Python developers. We have two excellent frameworks that excel in different areas. Choose based on your project needs, not framework wars.

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Want to learn FastAPI hands-on? I built FastAPI Interactive where you can code FastAPI directly in your browser with real-time testing and feedback. Perfect for Django developers making the switch.

Ready to build your next product with FastAPI? Check out Craft Your Startup - a production-ready FastAPI + React SaaS boilerplate that handles auth, payments, and deployment so you can focus on your core features.

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What's your experience switching between Python frameworks? Share your thoughts in the comments below.