Pydantic vs Marshmallow: Which to Use?

What are the main differences between Pydantic and Marshmallow?

Pydantic is primarily focused on data validation and settings management using Python type annotations, while Marshmallow is more about object serialization and deserialization. Pydantic is faster and more modern, whereas Marshmallow offers more flexibility in serialization.

In what scenarios should I use Pydantic over Marshmallow?

Use Pydantic when you need strict data validation and type checking, especially in FastAPI applications. It's great for settings management and when performance is a priority.

Are there any specific use cases where Marshmallow excels?

Marshmallow excels in scenarios where you need complex serialization/deserialization, such as working with APIs that require custom data formats or when integrating with legacy systems.

Can I use both libraries in the same project?

Yes, you can use both libraries in the same project. For example, you might use Pydantic for data validation and Marshmallow for serialization, depending on your needs.