Comparing
Data validation using Python type hints
TypeScript-first schema declaration and validation library with static type inference
| Metric | pydantic | zod |
|---|---|---|
| Ecosystem | Python | npm |
| Difficulty | Intermediate | Beginner ✓ |
| Weekly downloads | 0 | 148.9M↑ |
| Latest version | v2.12.5 | v4.3.6 |
| Recipes | 2↑ | 1 |
pydantic only
Shared
zod only
Use Pydantic for parsing API responses, validating config files, defining request/response schemas in FastAPI, or any time you receive data from an external source and want guarantees about its shape and types.
Use Zod at every trust boundary: parsing API responses, validating form inputs, reading environment variables, or loading config files. Any time external data enters your application, a Zod schema is the right gatekeeper.
Pydantic adds overhead on every model instantiation — not ideal for tight inner loops parsing millions of small objects. For pure config files with no user-facing validation, dataclasses or simple TypedDicts may be lighter.
Zod adds bundle size (around 12KB gzipped). For purely server-side validation where bundle size doesn't matter but performance does, Typebox or Ajv (JSON Schema) are faster. If you're already using Pydantic on the Python side, you may want to generate Zod schemas from it rather than maintain both.
pydantic
zod