Does FastAPI Work With PostgreSQL?

Fully CompatibleLast verified: 2026-02-20

FastAPI and PostgreSQL work excellently together, with multiple mature libraries providing seamless integration for building robust, production-grade APIs.

Quick Facts

Compatibility
full
Setup Difficulty
Easy
Official Integration
No — community maintained
Confidence
high
Minimum Versions
FastAPI: 0.68.0
PostgreSQL: 10.0

How FastAPI Works With PostgreSQL

FastAPI doesn't directly integrate with PostgreSQL but relies on excellent third-party ORMs and database drivers. SQLAlchemy with asyncio support (via sqlalchemy.ext.asyncio) is the most popular choice, allowing async/await patterns that align perfectly with FastAPI's asynchronous nature. Alternatively, Tortoise ORM or Databases library provide more lightweight async-first approaches. The developer experience is smooth: define your models, use dependency injection for database sessions, and let FastAPI's validation and automatic OpenAPI documentation handle the rest. Since FastAPI runs on async event loops, using async database drivers (asyncpg for PostgreSQL) prevents blocking and ensures your API scales efficiently. Connection pooling via libraries like asyncpg or SQLAlchemy's async engine keeps database overhead minimal. The combination is ideal for microservices, real-time APIs, and data-intensive applications where concurrent request handling matters.

Best Use Cases

Building RESTful APIs with complex relational data models and ACID transaction requirements
Real-time applications leveraging PostgreSQL's JSON operators and FastAPI's WebSocket support
Microservices that need async database operations without blocking I/O
Enterprise systems requiring advanced PostgreSQL features like full-text search, PostGIS, or custom extensions

Quick Setup

bash
pip install fastapi sqlalchemy asyncpg uvicorn
python
from fastapi import FastAPI, Depends
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
from sqlalchemy.orm import declarative_base
from sqlalchemy import Column, Integer, String

DATABASE_URL = "postgresql+asyncpg://user:password@localhost/dbname"
engine = create_async_engine(DATABASE_URL, echo=True)
AsyncSessionLocal = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
Base = declarative_base()

class User(Base):
    __tablename__ = "users"
    id = Column(Integer, primary_key=True)
    name = Column(String)

async def get_db():
    async with AsyncSessionLocal() as session:
        yield session

app = FastAPI()

@app.get("/users/{user_id}")
async def read_user(user_id: int, db: AsyncSession = Depends(get_db)):
    result = await db.get(User, user_id)
    return {"id": result.id, "name": result.name}

Known Issues & Gotchas

critical

Blocking database calls in async routes will freeze your entire event loop

Fix: Always use async drivers (asyncpg) and async ORM methods; never use psycopg2 or synchronous SQLAlchemy in async routes. Use run_sync() only as a last resort with thread pools.

warning

SQLAlchemy session management can be confusing with FastAPI's dependency injection

Fix: Create a SessionLocal factory and use FastAPI dependencies to yield sessions within a context manager, ensuring proper cleanup.

warning

Connection pool exhaustion when handling many concurrent requests

Fix: Configure pool_size and max_overflow appropriately based on your concurrent request load; monitor with database metrics.

info

N+1 query problems with lazy-loaded relationships

Fix: Use selectinload() or joinedload() in SQLAlchemy queries to eager-load related data and avoid multiple database round-trips.

Alternatives

  • Django + PostgreSQL: Full-featured framework with built-in ORM, better for monolithic applications
  • Node.js (Express/Nest.js) + PostgreSQL: JavaScript ecosystem with libraries like TypeORM or Prisma
  • Go + PostgreSQL: Lightweight, compiled language with pgx driver; excellent for high-performance APIs

Resources

Related Compatibility Guides

Explore more compatibility guides