Tonic.ai vs Gretel.ai

Quote on request

Quote on request

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

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 trial.

Established

Tonic.ai · since 2018

Emerging

NVIDIA (formerly Gretel) · since 2019

• 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.

• 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.

• 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.

• 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.

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

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