FastAPI Guide for Building APIs

By Vaibhav Gupta | July 14, 2026 | Free Resource

Table of Contents

1. FastAPI Basics

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def read_root():
 return {"Hello": "World"}

2. Endpoints

@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
 return {"item_id": item_id, "q": q}

@app.post("/items/")
def create_item(item: Item):
 return item

3. Request/Response

from pydantic import BaseModel

class Item(BaseModel):
 name: str
 description: str = None
 price: float
 tax: float = None

4. Authentication

from fastapi import Depends, HTTPException
from fastapi.security import OAuth2PasswordBearer

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")

@app.get("/users/me")
def read_users_me(token: str = Depends(oauth2_scheme)):
 return {"token": token}

5. Deployment

# Run with uvicorn
uvicorn main:app --reload

# Deploy to Render
# 1. Create Dockerfile
# 2. Push to GitHub
# 3. Connect to Render
# 4. Deploy

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Frequently Asked Questions

What is FastAPI used for? FastAPI is a modern Python web framework for building REST APIs and microservices. It is used for ML model serving, backend services, and data APIs because it is fast, typed, and auto-generates OpenAPI documentation.
Why is FastAPI faster than Flask? FastAPI is built on Starlette and Pydantic. It handles requests asynchronously with async/await, validates data with Pydantic models, and achieves performance close to Node.js and Go � often 3-5x faster than Flask for I/O-heavy workloads.
Do I need to know Python to learn FastAPI? Yes, basic Python is enough to start. FastAPI builds on Python type hints and decorators, so if you know functions and classes, DSWallah's FastAPI guide and mentor-led Python course take you the rest of the way.
Can I deploy ML models with FastAPI? Yes � this is one of its most popular uses. You wrap your model's predict function in an endpoint, and FastAPI handles validation, async requests, and auto docs. Deploy it free on Render or Vercel with Docker.

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