Look, I'm tired of the framework holy wars.

Django fanboys swearing it's the only mature choice. FastAPI evangelists claiming async makes everything magically faster. Reddit threads devolving into "well actually" competitions.

Nobody's building the same damn thing in both frameworks and showing real numbers.

So I did it. Built the exact same REST API twice. Same features, same endpoints, same database, same deployment setup.

One in Django REST Framework. One in FastAPI.

Spoiler alert: the results aren't what you think.

The Experiment: One API, Two Frameworks

Here's what I built — a basic but real SaaS backend:

• User authentication (JWT)
• CRUD operations for resources
• File uploads
• Background tasks
• Database with relationships
• API documentation
• Basic admin panel

Nothing fancy. Not a todo app. Not "hello world with dreams." An actual MVP you could ship.

Django version: 3 days of development FastAPI version: 6 hours of development

Wait, before you scream "FastAPI wins!" — let me finish the story.

Day 1: Django Setup (The Old Guard)

Started with Django because I knew it better. Django REST Framework, PostgreSQL, Celery for background tasks.

The good news? Everything's documented. Everything has a "right way" to do it. Stack Overflow has answers from 2012 that still work.

# Django REST Framework - The Familiar Pain
from rest_framework import serializers, viewsets
from rest_framework.permissions import IsAuthenticated

class UserSerializer(serializers.ModelSerializer):
    class Meta:
        model = User
        fields = ['id', 'username', 'email']
class UserViewSet(viewsets.ModelViewSet):
    queryset = User.objects.all()
    serializer_class = UserSerializer
    permission_classes = [IsAuthenticated]

Clean. Simple. Works exactly as expected.

But then you hit the Django walls:

Problem 1: ORM queries are synchronous. Every. Single. One.

Your API endpoint waits for the database. The database waits for nothing. Your response time suffers. "Just use select_related!" Yeah, I know. Still slower than it should be.

Problem 2: Settings hell.

I spent 45 minutes debugging why my Celery tasks weren't running. Turns out I had a typo in CELERY_BROKER_URL. Django's error messages are... helpful sometimes.

Problem 3: The serializer dance.

Want different fields for read vs write? New serializer. Want nested relationships? Another serializer. Want to validate complex business logic? Override validate() and hope you didn't break something.

By day 3, I had a working API. Tested, documented, deployable. But man, it felt heavy.

Day 4: FastAPI Speedrun (The New Hotness)

Okay, let's try FastAPI. Everyone says it's faster. Everyone says it's easier. Everyone is wrong about "easier" but we'll get to that.

# FastAPI - The New Way
from fastapi import FastAPI, Depends, HTTPException
from pydantic import BaseModel

app = FastAPI()
class User(BaseModel):
    id: int
    username: str
    email: str
@app.get("/users/{user_id}", response_model=User)
async def get_user(user_id: int):
    # Your logic here
    return user

First impression? This is beautiful.

Type hints everywhere. Automatic API docs (seriously, try hitting /docs - it's gorgeous). Async native. Pydantic validation that actually makes sense.

I rebuilt the entire API in 6 hours.

SIX. HOURS.

How?

Speed Factor 1: No ORM wrestling

I used SQLAlchemy async. Yes, async ORM queries. No more blocking on database calls. FastAPI + async SQLAlchemy = actually fast.

Speed Factor 2: Pydantic is a cheat code

Django serializers: define model, define serializer, wire them up, handle validation, add custom logic.

Pydantic: define model with types. Done. Validation automatic. Serialization automatic. JSON schema generation automatic.

# This just works
class UserCreate(BaseModel):
    username: str = Field(..., min_length=3, max_length=50)
    email: EmailStr  # validates email automatically
    password: str = Field(..., min_length=8)

@validator('password')
    def password_strength(cls, v):
        if not any(char.isdigit() for char in v):
            raise ValueError('Password needs a number')
        return v

Speed Factor 3: Dependency injection that doesn't suck

Django: middleware, context processors, decorators, magic request objects.

FastAPI: Depends(). That's it. Clear, explicit, testable.

async def get_current_user(token: str = Depends(oauth2_scheme)):
    # Verify token, return user
    return user

@app.get("/profile")
async def get_profile(current_user: User = Depends(get_current_user)):
    return current_user

By hour 6, I had a working API. Faster to build. Cleaner code. Better docs.

But then reality kicked in.

The Performance Tests (Where Things Got Interesting)

Deployed both APIs. Same VPS. Same PostgreSQL instance. Same Nginx config.

Hit them with wrk - a load testing tool that doesn't lie.

Simple GET request (fetch user by ID):

• Django: ~45ms average
• FastAPI: ~12ms average

Holy shit. FastAPI smoked Django.

Complex query (fetch user + related posts + comments):

• Django: ~180ms average
• FastAPI: ~95ms average

Still crushing it.

File upload (10MB file):

• Django: ~850ms
• FastAPI: ~820ms

Basically the same. Network bound, not framework bound.

Background task trigger:

• Django + Celery: task queued, immediate response
• FastAPI + background tasks: task runs in background, immediate response

Both handled this fine. Django's Celery setup is more robust for complex task queues though.

Under load (1000 concurrent requests):

• Django: started timing out at ~600 concurrent
• FastAPI: handled all 1000, response times degraded but no timeouts

Async wins under load. Not surprising.

The Development Experience (Where Django Fights Back)

Okay, so FastAPI is faster. Case closed?

Not even close.

Admin Panel:

Django: Built-in admin panel. Customize it. Works great. FastAPI: Uh… you're gonna build that yourself. Or use a third-party thing. Good luck.

For internal tools, Django's admin is a MASSIVE advantage. I spent 30 minutes in Django getting a functional admin panel. Would've taken me 2–3 days in FastAPI.

Authentication:

Django: django-allauth, social auth, email verification, password reset - all solved problems. FastAPI: Build it yourself or piece together OAuth libraries. It works, but you're doing more plumbing.

Database Migrations:

Django: python manage.py makemigrations - it just works FastAPI: Alembic. Which is great, but you need to understand it. Django's migrations are more magic, which is sometimes better.

Documentation:

FastAPI: Automatic interactive docs at /docs - absolute killer feature Django: You're writing that documentation yourself, buddy

Testing:

Django: TestCase, fixtures, factory boy - mature ecosystem FastAPI: TestClient is great, but the async testing story is still maturing

Ecosystem:

Django: 15+ years of packages, tutorials, Stack Overflow answers FastAPI: Growing fast, but you'll hit undocumented edge cases

The Real Cost Analysis (Money Talks)

Let's talk about what matters: money and time.

Development Speed:

• Simple CRUD API: FastAPI wins (6 hours vs 3 days)
• Complex business logic: About equal
• Admin/internal tools: Django wins by miles

Server Costs:

• Same traffic, FastAPI used 40% less CPU
• Could run on cheaper instances
• Saved ~$80/month on hosting

Maintenance:

• Django: Upgrade Django, upgrade DRF, fix deprecations
• FastAPI: Upgrade FastAPI, upgrade Starlette, upgrade Pydantic, fix async issues

Hiring:

• Django devs: Easy to find, well-understood patterns
• FastAPI devs: Harder to find, async experience needed

When Django Actually Wins

Real talk: I'm using Django for my current SaaS product.

Why? Because:

1. The admin panel is worth its weight in gold

Internal tools, customer support, data management — the Django admin handles it. Building equivalent functionality in FastAPI would cost me weeks.

2. Django ORM is good enough

Yeah, it's synchronous. Yeah, async is faster. But for most apps? The Django ORM is fine. My queries take 20–40ms. That's not the bottleneck.

3. The ecosystem is mature

Need payment processing? django-stripe. Email? django-anymail. CMS? wagtail. Social auth? allauth.

FastAPI has libraries too, but Django's ecosystem is battle-tested.

4. Deployment is solved

Gunicorn + Nginx + Django = thousands of tutorials Uvicorn + Nginx + FastAPI = fewer guides, more edge cases

If you're building an internal tool, B2B SaaS, or anything with complex admin needs — use Django. The admin panel alone justifies it.

When FastAPI Actually Wins

I'm using FastAPI for my ML API service.

Why? Because:

1. Async is real

When you're calling external APIs, running ML models, or doing heavy I/O — async matters. FastAPI handles concurrent requests way better.

2. Type safety catches bugs

Pydantic validation caught so many bugs before production. Django serializers are good, but Pydantic is better.

3. Performance under load

For public APIs with unpredictable traffic, FastAPI's async architecture is worth it.

4. Modern Python

Type hints, async/await, modern patterns — FastAPI feels like Python 3.10+. Django still has Python 2 era patterns.

If you're building a public API, microservice, or anything that needs high throughput — use FastAPI.

The Honest Recommendation (No BS)

Stop asking "which is better?"

Ask "which solves my problem?"

Use Django if:

• You need an admin panel
• You're building a traditional web app
• You want mature, battle-tested solutions
• You're a team of Django devs already
• Your bottleneck isn't the framework

Use FastAPI if:

• You're building an API-only service
• You need high performance under load
• You're comfortable with async Python
• You want automatic API documentation
• You're integrating with ML models or external APIs

Use both if:

• Django for main app + admin
• FastAPI for API endpoints + microservices
• They can coexist just fine

I'm literally doing this in production. Django handles the web app and admin. FastAPI handles the public API and ML inference. Different tools for different jobs.

The Technical Details (For the Nerds)

Let me break down the actual implementation differences:

Database Queries:

# Django - Synchronous
def get_user_posts(user_id):
    user = User.objects.get(id=user_id)
    posts = user.posts.all()
    return posts

# FastAPI - Asynchronous  
async def get_user_posts(user_id: int):
    async with get_session() as session:
        result = await session.execute(
            select(User).options(selectinload(User.posts))
            .where(User.id == user_id)
        )
        user = result.scalar_one()
        return user.posts

FastAPI version is longer. But it doesn't block. Under load, that matters.

Validation:

# Django REST Framework
class UserSerializer(serializers.Serializer):
    username = serializers.CharField(max_length=50)
    email = serializers.EmailField()
    
    def validate_username(self, value):
        if len(value) < 3:
            raise serializers.ValidationError("Too short")
        return value

# FastAPI with Pydantic
class User(BaseModel):
    username: str = Field(..., min_length=3, max_length=50)
    email: EmailStr
    
    @validator('username')
    def username_alphanumeric(cls, v):
        assert v.isalnum(), 'must be alphanumeric'
        return v

Both work. Pydantic feels more Pythonic to me.

Dependency Injection:

# Django - middleware and decorators
@login_required
@require_http_methods(["GET", "POST"])
def my_view(request):
    user = request.user
    # do stuff

# FastAPI - explicit dependencies
async def get_current_user(token: str = Depends(oauth2_scheme)):
    # verify token
    return user
@app.post("/items")
async def create_item(
    item: ItemCreate,
    user: User = Depends(get_current_user)
):
    # user is guaranteed to exist and be valid
    pass

FastAPI's dependency injection is clearer. You know exactly what's being injected.

The Resources That Helped (No Affiliate BS)

When I was learning this stuff, these resources actually helped:

For Django/Python fundamentals, I keep coming back to Grokking the Java Interview — wait, wrong language. But the interview prep mindset is the same. Actually, scratch that.

Real resources that helped:

• FastAPI docs (genuinely excellent)
• Django docs (comprehensive but dry)
• Real Python tutorials (both frameworks)
• Building actual projects (shocking, I know)

For Spring Boot folks transitioning to Python frameworks, Grokking the Spring Boot Interview has similar patterns. Dependency injection concepts translate well.

If you're doing this seriously and need production-ready starting points, I've got some stuff that might help:

Spring Boot Microservices Boilerplate — not Python, but if you're comparing frameworks, this is the Java equivalent of what I built.

Next.js SaaS Starter Template — for when you need a frontend to connect to your API.

AI SaaS Starter Kit — if you're building ML-powered stuff with FastAPI.

Spring Boot Production Checklist — deployment lessons that apply to Python apps too.

Top 85 Java Interview Q&A — framework patterns are universal.

Not trying to sell you stuff. Just sharing what I actually use when building production apps instead of tutorial projects.

What I'd Do Differently Next Time

If I rebuilt this experiment:

1. Test with a real database load

My tests were light. Real production has millions of rows, complex joins, bad queries written by tired devs at 2 AM.

2. Measure developer productivity over months

FastAPI was faster to start. But maintaining async code? That's a different story.

3. Factor in team experience

A team of Django experts will be faster in Django than stumbling through FastAPI async.

4. Consider the admin panel cost more seriously

I underestimated how much time Django's admin saves. Building equivalent admin tools in FastAPI would've cost me 2–3 weeks.

The Framework Wars Are Stupid

Here's the truth nobody wants to hear:

Your framework choice probably doesn't matter.

Your database design matters more. Your API design matters more. Your code quality matters more. Your deployment strategy matters more.

I've seen beautiful FastAPI code and terrible Django code. I've seen elegant Django projects and horrifying FastAPI hacks.

The framework doesn't write good code. You do.

That said, FastAPI is legitimately faster for high-throughput APIs. And Django's admin is legitimately a killer feature for business apps.

Pick the tool that matches your problem. Not the tool that's trending on Hacker News.

The Bottom Line

Built the same app in Django and FastAPI.

FastAPI was 5x faster to develop. 3–4x faster in performance. Better docs out of the box.

But I'm still using Django for most projects because the ecosystem and admin panel are worth the performance tradeoff.

Your mileage will vary. Test your own use case. Build something real. Make an informed decision.

Just don't be the person who picks a framework because of a Medium article.

(Including this one.)

Built something with Django or FastAPI? Drop your experience in the comments. Especially the disasters. Those make better stories.

Still arguing about which is "better"? Build the same thing in both and come back with data. Opinions without code are just noise.

Now stop reading framework comparisons and go build something.

🔧 The tools I actually use

Over the last few years, I kept rebuilding the same things again and again — SaaS starters, Spring Boot setups, interview prep notes, production checklists.

So I finally cleaned them up and turned them into real, production-ready resources instead of letting them rot in private repos.

If you're building or preparing seriously (not tutorial-hell stuff), these might save you days:

👉 Backend to SaaS Bundle — Production Ready Stack Everything together: backend, frontend, and AI in one bundle

Or individually:

Spring Boot Production Checklist (free) The exact things I double-check before shipping Java services to prod

Spring Boot Microservices Boilerplate — Production Ready Starter Kit A real starter — not "hello world + dreams"

AI SaaS Starter Kit (Next.js + OpenAI) The foundation I use when spinning up AI products fast

Ship an AI SaaS MVP — The No-BS Checklist A practical checklist to ship an AI MVP without over-engineering

Python for Production — Cheatsheet The Python stuff that actually matters in real systems

Spring Boot Troubleshooting — When Things Break in Production A battle-tested guide for debugging Spring in the real world

Production Engineering Cheatsheet The fundamentals nobody teaches until things break

Next.js SaaS Starter Template Clean, minimal, production-ready frontend boilerplate

Spring Boot Microservices Starter Kit v2 A production-ready backend stack in minutes, not weeks

Cracking the AWS & DevOps Interview (PDF) Focused on what actually gets asked — not trivia

Deploy Anything to AWS in 30 Minutes (PDF) A practical DevOps guide to get things live fast

Top 85 Java Interview Questions & Answers Curated real interview questions, not textbook theory

I'm not selling dreams here — just tools I built because I was tired of repeating the same work.

Build something this week. Don't just read about it.

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