Choose based on where your data contract lives and what you need to do with it. Use JSON Schema when you need a language-independent contract, Zod for TypeScript validation with inferred types, Pydantic for Python models and validation, or Aontu when validation is part of a wider document workflow that includes provenance and schema-evolution queries. These are not four interchangeable validators: JSON Schema is a format, Zod and Pydantic are language-centered libraries, and Aontu is a document-and-schema workflow with a CLI.
You can combine them. For example, an application can validate with Zod or Pydantic internally and exchange JSON Schema across service boundaries. The key is to check how each chosen tool represents the contract your other systems must consume.
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How the four choices differ
| Option | What it is | Best starting point | Check before committing |
|---|---|---|---|
| JSON Schema | A language-independent format for describing JSON instances; a separate validator implementation evaluates data against it. | Sharing a JSON contract across languages, tools, or services. | Confirm which draft and validator implementation each consumer supports, and test the keywords and formats you rely on. The official guide demonstrates Draft 2020-12: Creating your first schema. |
| Zod | A TypeScript-first validation library that couples runtime validation with static type inference. | Validating data at a TypeScript application boundary while deriving static types from schema declarations. | Keep TypeScript strict mode enabled, as the documentation requires, and verify JSON Schema conversion for the features downstream systems consume. The official introduction describes browser and Node.js support and JSON Schema conversion: Zod introduction. |
| Pydantic | A Python model and validation library that can generate JSON Schema from models or adapted types. | Defining and validating Python data models, then publishing a schema representation. | Choose whether consumers need a validation or serialization schema; the generated forms can differ for some types, including Decimal. The Pydantic 2.12 documentation explains the modes: Pydantic JSON Schema. |
| Aontu | A document-and-schema workflow exposed through a CLI, including validation and commands for provenance, tracing, schema evolution, and JSON Schema export. | Managing contracts as part of a workflow that needs more than validating one payload. | Assess its document/schema representation, adoption and interoperability requirements, and exact needs at the JSON boundary. Its CLI reference lists commands including vet, why, trace, breaking, subsume, and jsonschema: Aontu package documentation. |
Choose by the job you need done
Choose JSON Schema for a shared contract
Start with JSON Schema when multiple languages or services need to agree on the shape of JSON. The schema is the contract; a validator in each environment is what checks an instance against it. Treat those as separate decisions: select the draft your ecosystem supports, then verify the behavior of each validator against the specific keywords and formats in your schema.
Choose Zod for a TypeScript application boundary
Zod is a natural fit when schema declarations belong in TypeScript and you want runtime parsing or validation alongside inferred static types. Its documentation also describes conversion to JSON Schema, which can help when other systems need a representation of the contract. Conversion does not eliminate the need to test compatibility: confirm that the generated form expresses the features your consumers understand.
Choose Pydantic for Python models
Choose Pydantic when Python types and models are the center of your application’s data layer. It can produce JSON Schema from models or adapted types, but decide which side of the model the external contract should describe. Validation and serialization schemas can differ for certain types, so an integration that consumes one mode should not assume it is equivalent to the other.
Choose Aontu when the contract has a workflow around it
Aontu is worth evaluating when you need document validation plus workflow capabilities such as examining provenance, tracing where information came from, or checking schema evolution. Its CLI includes validation, provenance, evolution, and JSON Schema export commands. That broader scope also means you should assess its own document and schema representation, not only ask whether it can validate a payload.
Handling exact decimal values across JSON
JSON number syntax alone does not guarantee exact decimal handling in an application. Aontu’s exact-money guide explains that ordinary parsing with JSON.parse converts a numeric literal to binary64 before Aontu receives it; a value requiring exact decimal semantics may therefore fail a bigdecimal schema. The guide’s approach is to carry a fixed-scale decimal as a string, validate its lexical shape, and mark that convention in exported JSON Schema: Carry exact money over JSON.
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This is a documented convention, not a special property of JSON strings and not proof that other tools cannot implement the same policy. If you adopt it, define the source of truth, require producers to serialize in the agreed fixed-scale form, validate the string, and have consumers parse it with an exact decimal implementation. A string does not recover precision already lost before serialization, and consumers must treat it as a decimal by agreement rather than assume it is a JSON number.
What to test before standardizing
- Contract scope: Decide whether the source of truth is a cross-language JSON contract, a TypeScript declaration, a Python model, or Aontu’s document/schema representation.
- Interoperability: Check the JSON Schema draft, validator behavior, generated schema mode, or conversion features that every producer and consumer needs.
- Boundary semantics: Test formats and edge cases that matter to your data, especially values such as exact decimals that may be represented differently across runtimes.
- Workflow needs: If provenance, tracing, or schema-evolution checks are requirements, evaluate how the tool supports those tasks alongside ordinary validation.
- Performance claims: No controlled comparative benchmark is established by the cited official documentation. Test the same workload, data shapes, runtime versions, and validation semantics before claiming one option is faster.
Can these tools work together?
Yes. A team might use Zod or Pydantic for application-level validation and publish JSON Schema for other services. In that setup, the library’s declarations and exported schema are related but not automatically identical in behavior: test that the generated contract preserves the constraints consumers rely on. Aontu can also export JSON Schema, but its wider workflow and its own representation should be evaluated separately from the interoperability of that export.
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