Why I Chose FastAPI Over Flask
A pragmatic comparison of async-first API frameworks for AI-heavy workloads and strict type safety requirements.
The Decision Context
When building AI-heavy backend services, your framework choice has real consequences. After years with Flask, switching to FastAPI for production AI workloads was driven by three concrete requirements.
Async-First Architecture
AI workloads are I/O-bound by nature. You're waiting on model inference, vector database queries, and external API calls. Flask's synchronous nature means blocking threads during these operations.
| 1 | class="syn-comment"># FastAPI: naturally async |
| 2 | @app.post(class=class="syn-str">"syn-str">class="syn-str">"/generate") |
| 3 | async def generate(request: GenerateRequest): |
| 4 | embedding = await embed_model.encode(request.text) |
| 5 | context = await vector_db.query(embedding, top_k=5) |
| 6 | response = await llm.generate(request.text, context=context) |
| 7 | return GenerateResponse(text=response) |
With Flask, achieving the same requires Celery, threading hacks, or async workarounds that fight the framework rather than working with it.
Type Safety as Documentation
FastAPI's Pydantic integration means your API schema is your code. No drift between documentation and implementation.
| 1 | class GenerateRequest(BaseModel): |
| 2 | text: str = Field(..., min_length=1, max_length=10000) |
| 3 | model: Literal[class=class="syn-str">"syn-str">class="syn-str">"gpt-4", class=class="syn-str">"syn-str">class="syn-str">"claude-3"] = class=class="syn-str">"syn-str">class="syn-str">"gpt-4" |
| 4 | temperature: float = Field(0.7, ge=0, le=2) |
| 5 | max_tokens: int = Field(1024, ge=1, le=8192) |
This single definition gives you: request validation, OpenAPI documentation, type checking, and serialization. In Flask, you'd need Flask-RESTPlus, Marshmallow, and manual wiring.
Dependency Injection
FastAPI's dependency injection system is underrated. It elegantly solves resource management that Flask handles with g objects and teardown functions.
| 1 | async def get_db(): |
| 2 | db = await create_connection() |
| 3 | try: |
| 4 | yield db |
| 5 | finally: |
| 6 | await db.close() |
| 7 | |
| 8 | @app.get(class=class="syn-str">"syn-str">class="syn-str">"/users/{user_id}") |
| 9 | async def get_user(user_id: int, db=Depends(get_db)): |
| 10 | return await db.fetch_user(user_id) |
The Trade-offs
Flask still wins for: simple CRUD apps, teams familiar with its ecosystem, projects with extensive Flask extension requirements, and rapid prototyping where type safety overhead isn't justified.
FastAPI wins when you need: async I/O, automatic API documentation, strict type safety, and high-performance concurrent request handling — which is essentially every AI backend service.