Choosing a Python validator
A task-based choice between small dict validation and broader validation tools.
Start with the task
Compare the data shapes, type support, and integrations your application needs. Direct Python checks may be enough for a small task.
| What you need | A useful starting point |
|---|---|
| A handful of checks in one place | Direct Python checks; argparse type or choices for simple CLI arguments. |
| Reusable plain-dict validation with zero required runtime dependencies | Zodify: strict types by default, optional conversion, dict-shaped results. |
| Broader typing, serialization, or framework integration | Pydantic. TypeAdapter validates ordinary types without requiring a BaseModel subclass. |
| Validation against a JSON Schema document | A JSON Schema validator that supports the required draft. Zodify exports only a narrow exact subset. |
Try it with your own data
Check both accepted and rejected inputs, the output shape, and how errors fit your application. If performance matters, measure that workload with equivalent behavior; the benchmark guide explains how.