FastAPI vs Flask (2026): Which Python Web Framework Should You Choose?

Name

Soren Atelier

Updated on 2/10/2026

You're starting a new Python API project and need to choose a framework. Flask has been the go-to lightweight framework since 2010, powering millions of applications. FastAPI arrived in 2018 and has rapidly gained adoption with its automatic documentation, built-in validation, and async support. Picking the wrong one means either fighting against limitations later or adopting complexity you do not need.

This guide compares both frameworks on what matters: performance, developer experience, type safety, async handling, ecosystem, and practical use cases.

Quick Comparison

Hello World Comparison

Flask

from flask import Flask
 
app = Flask(__name__)
 
@app.route('/')
def hello():
    return {'message': 'Hello, World!'}
 
if __name__ == '__main__':
    app.run(debug=True)

FastAPI

from fastapi import FastAPI
 
app = FastAPI()
 
@app.get('/')
def hello():
    return {'message': 'Hello, World!'}
 
# Run with: uvicorn main:app --reload

Both are minimal and clean. FastAPI uses decorator methods (@app.get, @app.post) instead of Flask's @app.route with methods= parameter.

Type Safety and Validation

This is FastAPI's strongest advantage.

Flask: Manual Validation

from flask import Flask, request, jsonify
 
app = Flask(__name__)
 
@app.route('/users', methods=['POST'])
def create_user():
    data = request.get_json()
 
    # Manual validation
    if not data or 'name' not in data:
        return jsonify({'error': 'name is required'}), 400
    if not isinstance(data.get('age'), int) or data['age'] < 0:
        return jsonify({'error': 'age must be a positive integer'}), 400
    if 'email' not in data or '@' not in data['email']:
        return jsonify({'error': 'valid email is required'}), 400
 
    # Process valid data
    return jsonify({'id': 1, **data}), 201

FastAPI: Automatic Validation

from fastapi import FastAPI
from pydantic import BaseModel, EmailStr
 
app = FastAPI()
 
class UserCreate(BaseModel):
    name: str
    age: int
    email: EmailStr
 
@app.post('/users')
def create_user(user: UserCreate):
    # Validation happens automatically before this function runs
    # Invalid requests get a detailed 422 error response
    return {'id': 1, **user.model_dump()}

FastAPI uses Python type hints with Pydantic models to validate requests automatically. No manual checking, no boilerplate, and error responses include exactly which field failed and why.

Async Support

FastAPI: Native Async

from fastapi import FastAPI
import httpx
 
app = FastAPI()
 
@app.get('/data')
async def get_data():
    async with httpx.AsyncClient() as client:
        # Non-blocking: other requests can be handled while waiting
        response = await client.get('https://api.example.com/data')
        return response.json()

Flask: Sync by Default

from flask import Flask
import requests
 
app = Flask(__name__)
 
@app.route('/data')
def get_data():
    # Blocking: thread is tied up until response arrives
    response = requests.get('https://api.example.com/data')
    return response.json()

FastAPI handles async natively, making it ideal for I/O-bound workloads (API calls, database queries, file operations). Flask requires additional libraries and configuration for async support.

API Documentation

FastAPI generates interactive API docs automatically:

from fastapi import FastAPI
from pydantic import BaseModel
 
app = FastAPI(title="My API", version="1.0.0")
 
class Item(BaseModel):
    name: str
    price: float
    in_stock: bool = True
 
@app.get('/items/{item_id}')
def get_item(item_id: int):
    """Retrieve an item by its ID."""
    return {'item_id': item_id, 'name': 'Widget'}
 
@app.post('/items')
def create_item(item: Item):
    """Create a new item in the inventory."""
    return {'id': 1, **item.model_dump()}
 
# Visit http://localhost:8000/docs for Swagger UI
# Visit http://localhost:8000/redoc for ReDoc

Flask requires extensions like flask-swagger-ui or flask-restx for similar functionality, and even then the docs require more manual configuration.

Performance

FastAPI runs on ASGI (Uvicorn/Hypercorn), while Flask runs on WSGI (Gunicorn/Waitress):

FastAPI is roughly 2-3x faster for JSON APIs. The gap widens for I/O-bound workloads where async handling prevents thread blocking.

Ecosystem and Extensions

Flask Ecosystem (Mature)

FastAPI Ecosystem (Growing)

Flask has more extensions for every use case. FastAPI's ecosystem is smaller but growing rapidly, and many Python libraries work with both frameworks.

When to Choose Each

Choose FastAPI When:

• Building REST APIs or microservices
• Type safety and automatic validation matter
• You need async support for I/O-heavy workloads
• Auto-generated API documentation is valuable
• Building new projects without legacy constraints
• Performance is a priority

Choose Flask When:

• Building full-stack web apps with templates (Jinja2)
• You need a massive extension ecosystem
• Your team is already experienced with Flask
• The project is simple and doesn't need async
• You need maximum flexibility in architecture
• Rapid prototyping with minimal setup

Data Science API Example

For data scientists deploying ML models as APIs, FastAPI's type validation is particularly useful:

from fastapi import FastAPI
from pydantic import BaseModel
import numpy as np
 
app = FastAPI()
 
class PredictionRequest(BaseModel):
    features: list[float]
    model_name: str = "default"
 
class PredictionResponse(BaseModel):
    prediction: float
    confidence: float
 
@app.post('/predict', response_model=PredictionResponse)
def predict(req: PredictionRequest):
    # Model inference
    features = np.array(req.features)
    prediction = float(features.mean())  # Placeholder
    return PredictionResponse(
        prediction=prediction,
        confidence=0.95
    )

For building and testing these data pipelines interactively before deployment, RunCell (opens in a new tab) provides an AI-powered Jupyter environment where you can prototype model serving logic with AI assistance.

FAQ

Is FastAPI faster than Flask?

Yes, FastAPI is typically 2-3x faster than Flask for JSON API responses. FastAPI runs on ASGI with Uvicorn, while Flask uses WSGI. The performance gap increases for I/O-bound workloads where FastAPI's native async support prevents thread blocking.

Is FastAPI replacing Flask?

FastAPI is not replacing Flask, but it is becoming the preferred choice for new API-focused projects. Flask remains dominant for full-stack web applications and projects with complex extension needs. Both frameworks are actively maintained and growing.

Can I use Flask extensions with FastAPI?

No, Flask extensions are not compatible with FastAPI since they use different underlying protocols (WSGI vs ASGI). However, many Python libraries work with both frameworks, and FastAPI has its own growing ecosystem of tools.

Is FastAPI good for beginners?

FastAPI requires understanding Python type hints and Pydantic models, which adds some learning overhead. Flask is simpler for absolute beginners. However, FastAPI's automatic validation and documentation reduce the total code you need to write, which some beginners find easier overall.

Should I switch from Flask to FastAPI?

Only if you have a compelling reason: need for async, better performance, or automatic validation. Migrating a working Flask app has costs. For new projects, evaluate both. For existing Flask projects that work well, the migration effort may not be justified.

Conclusion

FastAPI and Flask serve different needs. FastAPI is the better choice for new API projects that benefit from type safety, automatic validation, async support, and auto-generated documentation. Flask is the better choice for full-stack web applications, rapid prototyping, and teams with existing Flask expertise. Neither is universally better -- the right choice depends on your project requirements, team skills, and whether you need Flask's mature ecosystem or FastAPI's modern features.