Pydantic Performance: 4 Tips on How to Validate Large Amounts of Data Efficiently

Pydantic's performance for validating large datasets can be significantly improved by following specific practices. Prefer using `Annotated` constraints over `@field_validator` decorators, as this allows validation logic to run in the highly optimized Rust core, yielding substantial speedups. When validating data from a JSON string, use `model_validate_json()` directly to bypass the creation of intermediate Python dictionaries and gain a nearly 2x performance boost. For validating lists of objects, `TypeAdapter` is the most efficient method, outperforming loops or wrapper models by running validation with a single compiled schema.