Gretel.ai vs Tonic.ai

Quote on request

Quote on request

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.

Enterprise quote-based pricing by data volume and connected databases.

Free trial.

Emerging

NVIDIA (formerly Gretel) · since 2019

Established

Tonic.ai · since 2018

• 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.

• Preserves referential integrity and statistical distribution, so applications behave as they do in production.
• Directly addresses the GDPR and HIPAA exposure created by production data in lower environments.
• Subsetting produces a small but coherent slice rather than a full copy.

• 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.

• Expensive, with enterprise-only pricing.
• Initial configuration of sensitive-field classification takes real effort.
• Adds a pipeline stage that must be maintained as the schema evolves.

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

Organisations that currently copy production data into staging and know they should stop.