Does FastAPI Work With MongoDB?

Fully CompatibleLast verified: 2026-02-20

FastAPI and MongoDB work seamlessly together, with multiple mature driver options making it straightforward to build production-ready APIs.

Quick Facts

Compatibility
full
Setup Difficulty
Easy
Official Integration
No — community maintained
Confidence
high
Minimum Versions
FastAPI: 0.68.0
MongoDB: 4.0

How FastAPI Works With MongoDB

FastAPI pairs excellently with MongoDB through libraries like PyMongo (synchronous) or Motor (async). Since FastAPI's main advantage is async support, Motor is the preferred choice for non-blocking database operations. The integration is clean: you instantiate a MongoDB client at startup, pass it through FastAPI's dependency injection system, and use it in route handlers. FastAPI's Pydantic models naturally map to MongoDB documents, making validation and serialization automatic. The async nature means your API won't block on database queries, crucial for handling concurrent requests. You'll typically use Motor's AsyncIOMotorClient to connect at application startup and close at shutdown using FastAPI's lifespan context managers. The document-oriented nature of MongoDB aligns well with FastAPI's data validation philosophy, though you should still use Pydantic schemas as the source of truth for API contracts rather than relying solely on MongoDB's flexible schema.

Best Use Cases

Building real-time APIs with high concurrency where async MongoDB operations prevent blocking
Rapid prototyping and MVP development where MongoDB's schema flexibility matches iterative design
Microservices architectures where lightweight, document-based data models reduce coupling
Content management systems or catalog APIs where hierarchical JSON-like documents map naturally to MongoDB

Quick Setup

bash
pip install fastapi motor pymongo[srv] uvicorn
python
from fastapi import FastAPI, Depends
from motor.motor_asyncio import AsyncIOMotorClient, AsyncIOMotorDatabase
from pydantic import BaseModel
from contextlib import asynccontextmanager

class User(BaseModel):
    name: str
    email: str

mongo_client: AsyncIOMotorClient = None
db: AsyncIOMotorDatabase = None

@asynccontextmanager
async def lifespan(app: FastAPI):
    global mongo_client, db
    mongo_client = AsyncIOMotorClient("mongodb+srv://user:pass@cluster.mongodb.net")
    db = mongo_client["myapp"]
    yield
    mongo_client.close()

app = FastAPI(lifespan=lifespan)

async def get_db():
    return db

@app.post("/users")
async def create_user(user: User, db: AsyncIOMotorDatabase = Depends(get_db)):
    result = await db.users.insert_one(user.dict())
    return {"id": str(result.inserted_id)}

@app.get("/users/{user_id}")
async def get_user(user_id: str, db: AsyncIOMotorDatabase = Depends(get_db)):
    from bson import ObjectId
    user = await db.users.find_one({"_id": ObjectId(user_id)})
    return user

Known Issues & Gotchas

warning

ObjectId serialization in JSON responses causes type errors

Fix: Convert ObjectId to string in Pydantic models using field validators or custom JSON encoders in FastAPI's JSONEncoder

warning

Motor's async operations don't automatically handle connection pooling limits under high load

Fix: Configure maxPoolSize and minPoolSize in connection string; monitor connection usage in production

info

Pydantic v2 changed validation behavior, breaking some existing MongoDB field configurations

Fix: Update Pydantic models to use ConfigDict and field validators instead of deprecated Config class

critical

MongoDB transactions require replica set deployments, not available in standalone mode

Fix: Use MongoDB Atlas or configure a local replica set for multi-document ACID transactions

Alternatives

  • Express.js with Mongoose - synchronous but simpler learning curve for Node.js teams
  • Django with MongoEngine - more batteries-included ORM approach with Python
  • Go with MongoDB Go driver - better performance for ultra-high-concurrency scenarios

Resources

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