Type Safety in Python: Pydantic vs. Data Classes vs. Annotations vs. TypedDicts

Python's dynamic typing allows developers to quickly prototype without worrying about type definitions, but this flexibility can lead to runtime errors that are difficult to debug. Type annotations, introduced in Python 3.5, help catch type-related errors during development, while data classes and TypedDicts introduced in later versions allow for structured data representation with minimal boilerplate. Pydantic, a comprehensive data validation library, enhances Python's type safety by enforcing type hints at runtime, offering data validation, parsing, serialization, and deserialization, making it ideal for SDK development. Pydantic's use of Rust ensures minimal performance overhead, and its ability to handle complex data validation tasks makes it a preferred choice for projects needing robust type safety, as exemplified by its adoption at Speakeasy. By integrating Pydantic, developers can achieve a balance between flexibility and type safety, ensuring data integrity while maintaining Python's ease of use.