Top 25 FastAPI Interview Questions & Answers (2026 Edition)

FastAPI is a modern, high-performance web framework for building APIs with Python 3.8+ based on standard Python type hints. It is designed to be developer-friendly, offering high speed (comparable to NodeJS and Go) and automatic documentation.

FastAPI's speed comes from its underlying architecture. It is built on Starlette, which is one of the fastest Python frameworks for building web services. Additionally, it uses ASGI (Asynchronous Server Gateway Interface) instead of the older, synchronous WSGI, allowing it to handle many concurrent connections using async and await.

WSGI (Web Server Gateway Interface) is the traditional synchronous standard where one request is handled by one process at a time. ASGI is the successor that supports asynchronous capabilities, allowing for long-lived connections like WebSockets and background tasks without blocking the main thread.

Pydantic is the engine used for data validation and settings management. When you define a Pydantic model, FastAPI uses it to:
1. Validate incoming request data.
2. Serialize outgoing response data.
3. Generate the JSON Schema used for the API documentation.

To start a basic app, you initialize the FastAPI class and define routes using decorators like @app.get().

Dependency Injection (DI) is a way to declare things that your code needs to work. In FastAPI, the Depends() function allows you to share logic (like database sessions, authentication, or common parameters) across multiple routes without duplicating code.

FastAPI uses Python type hints and Pydantic models. If you define a parameter as a specific type (e.g., int), FastAPI will automatically check if the incoming data matches that type and return a 422 Unprocessable Entity error if it doesn't.

Path parameters are variables embedded in the URL path. They are used to identify a specific resource, such as a user ID or a product slug.

Query parameters are key-value pairs that appear after the ? in a URL. They are usually optional and used for filtering, sorting, or pagination.

The Request Body is data sent by the client to your API, usually in JSON format within a POST, PUT, or PATCH request. In FastAPI, you use Pydantic models to define the structure of this body.

Middleware is a function that runs before every request is processed by a specific path operation, and also after every response. It’s useful for logging, adding custom headers, or checking timing.

Cross-Origin Resource Sharing (CORS) is enabled using the CORSMiddleware. This allows your API to be called from different domains (like a frontend hosted on a different server).

Uvicorn is a lightning-fast ASGI server implementation. Since FastAPI is just the framework, it needs a server to "listen" for requests. Uvicorn acts as that bridge, running the Python code when a request hits the server.

One of FastAPI's most loved features is that it uses the OpenAPI standard to generate interactive documentation automatically. It takes your Pydantic models and route definitions to build a UI where you can test the API directly.

Swagger UI is the default interactive documentation interface provided by FastAPI. It is accessible at the /docs endpoint. It allows developers to visualize and interact with the API’s resources without having any of the implementation logic.

ReDoc is an alternative documentation interface provided by FastAPI, accessible at /redoc. While Swagger is focused on "trying out" endpoints, ReDoc is often considered cleaner and more readable for purely informative documentation.

FastAPI provides fastapi.security tools to handle OAuth2, JWT tokens, and API Keys. It simplifies the process of extracting the token from headers and validating the user.

FastAPI is "database agnostic," meaning you can use any ORM (Object-Relational Mapper). Most developers use SQLAlchemy or Tortoise ORM. Connections are usually managed via Dependency Injection to ensure sessions are closed after a request.

Background Tasks allow you to run heavy logic (like sending an email or processing an image) *after* returning the HTTP response to the user. This makes the API feel much faster.

You use the HTTPException class to return specific error codes and messages to the client. You can also create custom exception handlers for more complex scenarios.

WebSockets allow for two-way communication between the client and server. Unlike HTTP, the connection stays open. FastAPI makes this easy with the WebSocket class, which is perfect for chat apps or real-time dashboards.

FastAPI uses File and UploadFile. UploadFile is generally preferred because it uses a "spooled" file (stored in memory up to a limit, then on disk), which is more efficient for large files.

A Response Model is a Pydantic model used to define exactly what data should be returned to the client. This is crucial for security (filtering out passwords) and clarity (ensuring consistent output).

In 2026, the standard way to deploy FastAPI is using Docker containers. You typically run Uvicorn (or Gunicorn with Uvicorn workers) inside the container. Cloud platforms like AWS, Google Cloud, and DigitalOcean are popular choices for hosting.

Flask is great for simple, flexible apps but requires many plugins for modern features. FastAPI is better for large-scale APIs because it has data validation, async support, and documentation built-in. FastAPI is generally faster to develop and much faster to execute.