Gretel.ai vs Prism

Quote on request

Free and open source

The old credit-based self-serve tier is gone. The successor microservices are licensed through NVIDIA AI Enterprise; NeMo Data Designer can be tried on build.nvidia.com.

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

Everything, self-hosted

Emerging

NVIDIA (formerly Gretel) · since 2019

Established

Stoplight (SmartBear) · since 2017

• Formal differential-privacy guarantees rather than best-effort masking, now via DP-SGD in NeMo Safe Synthesizer.
• Synthetic data preserves statistical relationships useful for analytics and model training.
• Backed by NVIDIA's platform investment rather than a single startup's runway.

• 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.

• No longer buyable as Gretel — you are procuring NVIDIA AI Enterprise, which is far heavier than the old self-serve signup.
• Model training adds time and cost compared to rule-based masking.
• Safe Synthesizer trains on a real dataset, so it does not help with greenfield data that does not exist yet.

• 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 enterprises already standardising on NVIDIA AI Enterprise. Teams that wanted Gretel's self-serve tier should look at Tonic or Neosync instead.

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