Which Companies Use FastAPI? Microsoft, Netflix, Uber & 50+ Enterprise Examples

FastAPI has rapidly grown from a promising framework to an enterprise-grade solution powering critical systems at some of the world's largest companies. Let's explore who's using FastAPI and why they chose it.

Tech Giants Using FastAPI

Microsoft

Microsoft has embraced FastAPI across multiple products and services, making it one of the framework's most prominent enterprise adopters.

Use Cases:

• Azure Cognitive Services: FastAPI powers several AI/ML service endpoints
• Microsoft Graph API helpers: Internal tooling and proxy services
• Windows Development: Internal build and CI/CD orchestration APIs

Why Microsoft Chose FastAPI:

"FastAPI's automatic OpenAPI generation and type safety align perfectly with our API-first development philosophy." — Microsoft Developer Blog

Netflix

Netflix uses FastAPI for their internal machine learning platform and content recommendation infrastructure.

Use Cases:

• Crisis Management Platform: The Dispatch incident management system
• ML Model Serving: Real-time recommendation inference APIs
• Internal Tooling: Developer productivity and automation services

# Netflix's Dispatch is open-source and uses FastAPI
# github.com/Netflix/dispatch
from fastapi import FastAPI, Depends
from dispatch.auth import service as auth_service
 
app = FastAPI(title="Netflix Dispatch")

Why Netflix Chose FastAPI:

• Async support for handling high concurrency
• Built-in schema validation reduces bugs
• Automatic API documentation for internal teams

Uber

Uber leverages FastAPI for their next-generation microservices, particularly in their ML infrastructure.

Use Cases:

• Ludwig ML Platform: FastAPI serves the declarative ML framework
• Michelangelo: Model serving and feature store APIs
• Internal APIs: Data platform services and orchestration

Open Source Example:

# Uber's Ludwig uses FastAPI for its serving API
# github.com/ludwig-ai/ludwig
from ludwig.serve import server as ludwig_api
# FastAPI powers the model prediction endpoints

Stripe

Stripe uses FastAPI for internal tooling and experimental APIs, appreciating its rapid development cycle.

Use Cases:

• Internal developer tools
• Prototype and experimental API services
• Testing infrastructure

Explosion AI (spaCy)

The creators of spaCy, the popular NLP library, build their production services with FastAPI.

Use Cases:

• Prodigy: Data annotation platform API
• spaCy Cloud: Hosted NLP services
• Model Serving: NLP model inference endpoints

Growing Adoption by Category

Financial Services

E-Commerce & Retail

Healthcare & Life Sciences

AI/ML Companies

Why Companies Choose FastAPI

1. Developer Productivity

Modern Python type hints reduce boilerplate and catch errors early:

from fastapi import FastAPI
from pydantic import BaseModel, EmailStr
 
class UserCreate(BaseModel):
    email: EmailStr
    name: str
    age: int | None = None
 
@app.post("/users")
async def create_user(user: UserCreate):
    # Type validation happens automatically
    return {"id": 1, **user.model_dump()}

2. Performance at Scale

FastAPI delivers performance comparable to Node.js and Go:

Framework Benchmarks (requests/second):
├── FastAPI:    45,000 req/s
├── Flask:      10,000 req/s
├── Django:      8,000 req/s
└── Express.js: 50,000 req/s

3. Automatic Documentation

Enterprise teams love auto-generated, always-accurate docs:

@app.get(
    "/orders/{order_id}",
    response_model=OrderResponse,
    summary="Retrieve Order",
    description="Fetch order details by ID with full transaction history",
    responses={
        404: {"description": "Order not found"},
        403: {"description": "Insufficient permissions"}
    }
)
async def get_order(order_id: UUID):
    ...

4. Async Native

Built for modern async workloads from the ground up:

import asyncio
import httpx
 
async def fetch_user_data(user_ids: list[str]):
    async with httpx.AsyncClient() as client:
        tasks = [client.get(f"/users/{uid}") for uid in user_ids]
        responses = await asyncio.gather(*tasks)
        return [r.json() for r in responses]

5. Enterprise Integration

FastAPI integrates seamlessly with enterprise infrastructure:

# OpenTelemetry for distributed tracing
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
 
app = FastAPI()
FastAPIInstrumentor.instrument_app(app)
 
# Prometheus metrics
from prometheus_fastapi_instrumentator import Instrumentator
Instrumentator().instrument(app).expose(app)

Open Source Projects Using FastAPI

Many popular open-source projects are built with FastAPI:

LangChain Serve

# AI application framework
from langserve import add_routes
add_routes(app, chain, path="/chat")

Prefect

# Workflow orchestration platform
# Uses FastAPI for the Prefect Server API

MLflow

# ML lifecycle management
# Model serving endpoints powered by FastAPI

Apache Superset

# Data visualization platform
# Several new API endpoints use FastAPI

Migration Stories

From Flask to FastAPI

A major fintech company reported:

From Django REST Framework

An e-commerce platform shared:

"Moving from DRF to FastAPI reduced our API codebase by 40% while improving response times by 3x. The type safety alone caught issues that would have reached production."

FastAPI in Production: Key Patterns

Pattern 1: Service Mesh Integration

from fastapi import FastAPI, Request
import httpx
 
app = FastAPI()
 
@app.middleware("http")
async def add_trace_context(request: Request, call_next):
    # Forward trace headers for distributed tracing
    trace_id = request.headers.get("X-Trace-ID")
    response = await call_next(request)
    response.headers["X-Trace-ID"] = trace_id
    return response

Pattern 2: Feature Flags

from fastapi import Depends
 
async def check_feature_flag(flag_name: str):
    async def checker():
        return await feature_service.is_enabled(flag_name)
    return checker
 
@app.get("/beta-feature")
async def beta_endpoint(
    enabled: bool = Depends(check_feature_flag("new_algorithm"))
):
    if not enabled:
        raise HTTPException(404)
    return {"result": "beta feature enabled"}

The Growing Ecosystem

FastAPI's adoption has created a rich ecosystem:

• 100,000+ GitHub stars
• 3,000+ packages depend on FastAPI
• 500+ companies publicly confirmed usage
• Top 10 most-used Python web frameworks

Conclusion

From startups to Fortune 500 companies, FastAPI has proven itself as a production-ready framework capable of handling enterprise-scale workloads. Its combination of performance, developer experience, and modern Python features makes it the framework of choice for new API development.

Ready to join them? ForgeAPI provides battle-tested FastAPI templates used by companies shipping to production every day.

Related Articles:

• FastAPI Best Practices for Production
• Building High-Performance APIs with FastAPI