Gretel.ai vs Prism

Free tier available

Free and open source

Consumption-based on generated records; enterprise agreements available.

Free and open source. You pay in engineering time and CI minutes.

Free tier with a monthly credit allowance.

Everything, self-hosted

Emerging

Gretel (NVIDIA) · since 2019

Established

Stoplight (SmartBear) · since 2017

• Formal differential-privacy guarantees rather than best-effort masking.
• Synthetic data preserves statistical relationships useful for analytics and model training.
• Privacy and quality scores make the trade-off explicit.

• The mock is generated from the spec, so it cannot silently diverge from the contract.
• Validation proxy mode checks that a real implementation matches its own spec.
• Zero configuration beyond having an OpenAPI file.

• Model training adds time and cost compared to rule-based masking.
• Overkill when simple anonymisation would satisfy the requirement.
• NVIDIA acquisition changes the strategic outlook.

• Useless without an accurate OpenAPI document.
• Limited stateful behaviour — it is spec-driven, not scenario-driven.
• Generated example data is uninteresting unless examples are in the spec.

Regulated organisations that need shareable data with defensible privacy properties.

Design-first API teams who want mocks that are provably in sync with the contract.