FastAPI vs Flask: Key Differences and Use Cases

FastAPI vs Flask: Key Differences and Use Cases

Both FastAPI and Flask are Python web frameworks used for building APIs, but they have different strengths and use cases. Here's a breakdown:

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FastAPI vs Flask with Examples

1. Performance

• FastAPI: High-performance (on par with Node.js and Go) due to its use of ASGI (Asynchronous Server Gateway Interface) and async/await support.
• Flask: Synchronous (WSGI-based) by default, making it slower in high-concurrency scenarios.

🏆 Winner: FastAPI(Better for high-performance and async applications).

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2. Ease of Use & Learning Curve

• FastAPI: Modern but slightly more complex due to typing and async handling.
• Flask: Simple, lightweight, and easy for beginners.

🏆 Winner: Flask (Easier for beginners and small projects).

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3. Type Safety & Data Validation

• FastAPI: Uses Pydantic and type hints to validate and serialize request/response data automatically.
• Flask: No built-in data validation; requires additional libraries like Marshmallow.

🏆 Winner: FastAPI(Built-in validation and type safety).

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4. Async Support

• FastAPI: Natively supports async/await, making it ideal for real-time applications (WebSockets, GraphQL, Background tasks).
• Flask: No native async support; requires third-party tools like Quart or gevent.

🏆 Winner: FastAPI(Best for async applications).

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5. Routing and Dependency Injection

• FastAPI: Supports dependency injection, making code more modular and reusable.
• Flask: No built-in dependency injection.

🏆 Winner: FastAPI(Better architecture for complex applications).

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6. Community & Ecosystem

• Flask: Larger community, more third-party extensions (Flask-SQLAlchemy, Flask-RESTful).
• FastAPI: Growing rapidly but fewer third-party plugins compared to Flask.

🏆 Winner: Flask (Mature ecosystem).

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7. Documentation

• FastAPI: Automatically generates interactive Swagger UI and ReDoc from type hints.
• Flask: Requires tools like Flask-Swagger or Flask-RESTPlus for API documentation.

🏆 Winner: FastAPI(Best documentation experience out-of-the-box).

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8. Use Cases

Use Case	FastAPI	Flask
Simple web apps & APIs	✅ Possible, but overkill	✅ Best choice
Async, real-time apps	✅ Ideal (async support)	❌ Not recommended
Microservices	✅ Excellent	✅ Good
Machine Learning APIs	✅ Great (Fast & efficient)	✅ Good
High-concurrency apps	✅ Ideal	❌ Not optimal

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When to Use What?

• Use FastAPI if:

  • You need high performance and async support.
  • You want automatic API documentation.
  • You require type safety and data validation.
  • You’re building real-time applications (WebSockets, ML APIs, etc.).

• Use Flask if:

  • You need a lightweight and simple web framework.
  • Your project doesn’t require async processing.
  • You prefer a mature ecosystem with more plugins.
  • You’re working on small-to-medium-sized projects.

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Final Verdict

✅ For modern, high-performance APIs → Go with FastAPI

✅ For simplicity and traditional web apps → Stick with Flask

If you're building scalable microservices, async apps, or machine learning APIs, FastAPI is the way to go. But if you're working on a small project or need a well-established framework with lots of extensions, Flask is still a great choice. 🚀

Comments

[Anon]

I would explicitly compare the maturity , maintenance, and how DRY between json and db models each of these are:

Flask + Sqlalchemy + marshmallow (feature complete, mature, DRY)
Vs
Fastapi + sqlmodel (beta, DRY)
Vs
Fastapi + sqlalchemy (feature complete, mature, not DRY)

since that is the core decision

[Muhammad Atif Iqbal]

Great 👍

[Muhammad Atif Iqbal]

In next article I'll discuss these points

[Anon]

I am personally going with flask, sqlalchemy, and marshmallow, but I am keeping an on eye on fastapi + sqlachemy (no sqlmodel).

[Muhammad Atif Iqbal]

That's a solid choice! Flask + SQLAlchemy + Marshmallow is a well-established stack with a lot of flexibility and a mature ecosystem. Flask’s lightweight nature makes it easy to build and scale applications, SQLAlchemy provides powerful ORM capabilities, and Marshmallow is great for data serialization and validation.

Keeping an eye on FastAPI + SQLAlchemy (without SQLModel) is also a smart move. FastAPI’s async capabilities and automatic data validation (via Pydantic) make it an attractive alternative, especially for high-performance applications. However, Flask is still a great choice if you're comfortable with synchronous execution and want more control over the stack without diving into async complexities.

If you ever decide to migrate, FastAPI can integrate well with SQLAlchemy without SQLModel, but Flask’s ecosystem remains stable and highly customizable. It all comes down to the project requirements and whether async processing is a necessity for you. 🚀

[Anon]

I am still getting to know marshmallow (and pydantic), but I wish it would more easily support many variations on serializing trees of data. That is, I think I will have to use different marshmallow schemas for each tree instead of mixing and matching smaller schemas.

For example, let's say we have routes, route_trips, stops, and trip_stops, and I want to have GET endpoints which return a route with all related route_trips and all trip_stops and stops, and I also wanted GET endpoints that returned a stop with all trips using the stop, and a GET endpoint returning all trip_stops for a trip. I think I would need to define a distinct schema for each GET endpoint without any reuse.

[Muhammad Atif Iqbal]

That's a great observation! This is exactly where FastAPI + Pydantic shines over Flask + Marshmallow. Pydantic’s data modeling allows for much better composition and reusability of schemas without having to define entirely separate ones for each endpoint.

Instead of creating multiple schemas with Marshmallow, in FastAPI, you can structure your Pydantic models in a way that allows selective inclusion/exclusion dynamically. Using Config and orm_mode, you can easily control what gets serialized without duplicating schemas.

For instance, in FastAPI:

from pydantic import BaseModel
from typing import List, Optional

class StopBase(BaseModel):
    id: int
    name: str

class TripBase(BaseModel):
    id: int
    name: str

class StopWithTrips(StopBase):
    trips: List[TripBase]

class TripWithStops(TripBase):
    stops: List[StopBase]

class RouteTrip(BaseModel):
    trip: TripWithStops

class Route(BaseModel):
    id: int
    name: str
    route_trips: List[RouteTrip]

Why this is better in FastAPI? 🚀

1. No need for separate schemas for each endpoint – You can reuse TripBase, StopBase, and selectively compose them.
2. Better validation & serialization – Pydantic ensures the structure is always correct, and it works seamlessly with FastAPI’s automatic request validation.
3. Performance – FastAPI’s async capabilities and Pydantic’s efficient parsing make it significantly faster than Flask + Marshmallow.
4. Flexible API Responses – You can return different levels of nesting on demand without modifying your schema structure.

So while Marshmallow requires you to manually define and tweak each schema, FastAPI + Pydantic handles this elegantly and dynamically, making it a much better fit for complex API structures. If your project demands flexibility in serialization and performance, FastAPI is the way to go! 🚀

[Anon]

Thanks!

Assuming composable schema is not possible with marshmallow similar to pydantic, there is still the issue of boilerplate code for pydantic to sqlachemy orm class conversion (assuming sqlmodel is too young to adopt)?

At most, I might adopt pydantic and flask, since I don't care about perf and want max number of extensions available, and wait until fastapi has matched flask in number of extension features implemented

[Muhammad Atif Iqbal]

FastAPI is already closing the gap with Flask in terms of extensions while offering native Pydantic support, eliminating much of the boilerplate required in Flask + SQLAlchemy. Even without SQLModel, you can streamline ORM conversion using pydantic-sqlalchemy or custom helper functions.

While Flask has more extensions, FastAPI’s ecosystem is rapidly growing, and its built-in validation, async support, and dependency injection make it a more future-proof choice. Even if performance isn’t a concern now, FastAPI provides better scalability and less boilerplate, making it the smarter long-term investment. 🚀

[Anon]

I remember seeing that also, but the homepage for pydantic-sqlalchemy says "WARNING: Use SQLModel instead " and then sqlmodel is in a beta phase

[Muhammad Atif Iqbal]

That's true, but SQLModel being in beta doesn’t mean FastAPI users are stuck—you can still use FastAPI + SQLAlchemy + Pydantic efficiently. While pydantic-sqlalchemy suggests using SQLModel, you can still avoid excessive boilerplate by:

1. Manually creating Pydantic models with helper functions.
2. Using SQLAlchemy’s as_dict() pattern for easy serialization.
3. Leveraging Alembic for migrations instead of relying on SQLModel’s features.

SQLModel is promising but not mandatory—FastAPI + SQLAlchemy already works well, and Pydantic still simplifies validation. If SQLModel matures, switching will be easy, but waiting isn’t necessary. 🚀

[Brian Kirkpatrick]

Great article! Some thoughts:

1. Performance: I'm assuming you are evaluating the built-in development server here, because Flask itself is not a web server. The most common pattern I utilize is, defining a WSGI application in Flask then using something like GEvent to do the production-grade hosting of that application--which does come with asynchronous implementations. Added bonus for handler extensions that let you seamlessly add things like WebSocket support. Even FastAPI is not necessarily a server in and of itself. It does use uvicorn out of the box, which is a great option.

3. Routing and Dependency Injection: Not really sure what this evaluation is based on. There are plenty of middleware and routing controls supported for Flask--the ecosystem is quite expansive and the lightweight/minimal nature of the Flask route identification helps out a lot here. And I definitely take issue with the idea that these features automatically translate to better architectures and more complex applications!

6. Community & Ecosystem: Overall, I agree, the Flask ecosystem is more mature--but because FastAPI is so strongly data-centric you get much more seamless integration with a wide variety of database engines and other stateful dependencies, which is where a lot of your complexity would otherwise come from.

7. Documentation: I'm assuming you are specifically referring to the auto-doc features, which is fair but keep in mind FastAPI is centered on stateful transitions so it's a lot easier to procedurally map models, etc., to a formal specification. There are ways to do so with Flask but it's simply not the same intended usecase.

Overall, I find Flask and FastAPI to both be excellent options that don't necessarily compete with each other to the point of exclusion--which one you choose largely comes down to what patterns and practices you are using to sculpt your architecture. If you need a minimal, lightweight, and extensible tool that focuses on loose endpoint-by-endpoint definitions and is easy to get started, use Flask. If you are writing something data-intensive that needs a degree of state management and automated integration with things like databases and modeling tools, use FastAPI.

As an aside, Flask does have some degree of assumption that you'll migrate towards template-based server-side rendering, which I don't use very often and am glad that the feature is purely optional. But if that floats your boat, and if you're looking for something that's a better Django replacement without the MVC constraints, Flask is likely worth considering.

[Muhammad Atif Iqbal]

Thanks for your feedback, Brian! I agree with your points, especially about Flask's flexibility and ecosystem. However, FastAPI truly shines in performance, especially with its async capabilities and seamless integration with Uvicorn, making it more suitable for high-performance, data-intensive applications. The automatic dependency injection and built-in support for modern tools like Pydantic make development faster and more efficient, particularly when handling stateful data. While Flask is fantastic for lightweight apps, FastAPI's speed, scalability, and data-centric design make it a clear choice for more complex, modern applications.

[Muhammad Atif Iqbal]

Great 👍

[Denys Bochko]

nice article, thanks

[Muhammad Atif Iqbal]

Most welcome

[Praveen Rajamani]

Great breakdown of FastAPI vs Flask!

[Muhammad Atif Iqbal]

Thanks