What are the main differences between Pydantic and dataclasses in terms of how they define objects and their validation capabilities?

Pydantic defines models by subclassing BaseModel, while dataclasses use the @dataclass decorator on a plain class. Dataclasses mainly group data and are part of the standard library, whereas Pydantic is third-party and provides built-in validation such as type checks and constraints like min_length. Pydantic stands out for validation, which is why it is commonly used with FastAPI.

The main structural difference is in how objects are defined. Pydantic models inherit from Pydantic's BaseModel class. Dataclasses, in contrast, are created with Python's @dataclass decorator on a regular class, and they are part of the standard Python library. Pydantic is a third-party package that is tightly integrated with FastAPI. Their validation capabilities also differ. Dataclasses are useful for grouping related fields together, but they do not by themselves validate the data. Pydantic provides automatic validation of model fields, including type checking, required versus optional values, default values, and constraints such as minimum string length. It also catches many data errors early, especially when used inside FastAPI.