FastAPI + PostgreSQL: Build a Production-Ready REST API the Right Way

Tired of Messy Backend Code? This Stack Will Change the Way You Build APIs

FastAPI + PostgreSQL: Build a Production-Ready REST API the Right Way

Learn how to combine the blazing speed of FastAPI with the robustness of PostgreSQL to create a scalable, maintainable backend — perfect for real-world deployment.

The world doesn’t need another toy API tutorial. You’re here because you want to build something real — an API that performs well in production, scales with users, and doesn’t fall apart under pressure.

That’s where FastAPI + PostgreSQL comes in. FastAPI brings speed and developer ergonomics, while PostgreSQL delivers rock-solid data reliability. Together, they’re a power combo for building modern REST APIs.

In this article, I’ll walk you through setting up a complete backend with these two technologies, following production-grade best practices from the start. Whether you’re building a SaaS app, internal tool, or microservice, this setup has you covered.

Why FastAPI + PostgreSQL?

Before diving into the code, let’s answer why this stack is worth your time.

FastAPI highlights:

• Lightning-fast performance (thanks to Starlette + Pydantic)
• Automatic interactive docs via Swagger and ReDoc
• Pythonic syntax with async support out of the box
• Dependency injection system for clean architecture

PostgreSQL strengths:

• Fully-featured, ACID-compliant, open-source relational database
• Great support for JSON, indexing, and full-text search
• Mature ecosystem and widely supported in the cloud (AWS RDS, Supabase, etc.)

Together, they offer:

• Fast API development with type safety and minimal boilerplate
• Strong data consistency with powerful querying and migrations
• Easy integration with ORMs like SQLAlchemy

Project Setup

Let’s build a simple production-ready API that manages a list of tasks (like a to-do app, but cleaner). Here’s how we’ll organize the code:

app/ 
├── main.py 
├── models.py 
├── schemas.py 
├── database.py 
├── crud.py 
├── routers/ 
│   └── tasks.py

Dependencies (with Poetry or pip)

pip install fastapi uvicorn sqlalchemy psycopg2-binary alembic pydantic

Step 1: Configure the Database

database.py

from sqlalchemy import create_engine 
from sqlalchemy.orm import sessionmaker, declarative_base 
 
DATABASE_URL = "postgresql://user:password@localhost/todo_db" 
 
engine = create_engine(DATABASE_URL) 
SessionLocal = sessionmaker(bind=engine, autoflush=False, autocommit=False) 
 
Base = declarative_base()

Step 2: Define Models and Schemas

models.py

from pydantic import BaseModel 
 
class TaskCreate(BaseModel): 
    title: str 
 
class TaskResponse(TaskCreate): 
    id: int 
    is_done: bool 
 
    class Config: 
        orm_mode = True

Step 3: Write CRUD Operations

crud.py

from sqlalchemy.orm import Session 
from . import models, schemas 
 
def create_task(db: Session, task: schemas.TaskCreate): 
    db_task = models.Task(title=task.title) 
    db.add(db_task) 
    db.commit() 
    db.refresh(db_task) 
    return db_task 
 
def get_tasks(db: Session): 
    return db.query(models.Task).all()

Step 4: Create Routes

routers/tasks.py

from fastapi import APIRouter, Depends 
from sqlalchemy.orm import Session 
from .. import crud, schemas, database 
 
router = APIRouter() 
 
def get_db(): 
    db = database.SessionLocal() 
    try: 
        yield db 
    finally: 
        db.close() 
 
@router.post("/tasks", response_model=schemas.TaskResponse) 
def create(task: schemas.TaskCreate, db: Session = Depends(get_db)): 
    return crud.create_task(db, task) 
 
@router.get("/tasks", response_model=list[schemas.TaskResponse]) 
def read_tasks(db: Session = Depends(get_db)): 
    return crud.get_tasks(db)

Step 5: Wire Everything Together

main.py

from fastapi import FastAPI 
from .routers import tasks 
from .database import Base, engine 
 
app = FastAPI(title="Production-Ready API") 
 
Base.metadata.create_all(bind=engine) 
 
app.include_router(tasks.router)

Bonus: Production-Ready Tips

• Environment variables: Use python-decouple or pydantic.BaseSettings to store secrets.
• Gunicorn + Uvicorn workers: Ideal for ASGI apps in deployment.
• Alembic: Use for database migrations.
• Logging & Monitoring: Integrate tools like Sentry, Prometheus, or OpenTelemetry.
• Docker: Containerize for deployment anywhere (Heroku, AWS, GCP).
• CI/CD: Automate testing and deployments via GitHub Actions or GitLab CI.

Conclusion

FastAPI and PostgreSQL give you the foundation to build fast, secure, and maintainable backends — without the complexity of heavier frameworks.

By following clean architecture and integrating production-grade practices early, you future-proof your API and reduce technical debt later.

Next Step:
Try extending this project with user authentication (OAuth2), JWT-based tokens, or async SQLAlchemy (asyncpg) for even more performance.