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testcritic

Gretel.ai vs Faker

Feature checklists rarely decide anything. What decides it is the pricing metric, who has to write the tests, and which limitation you can live with.

Gretel.aiTest Data & Mocks
FakerTest Data & Mocks

Entry price

Quote on request

Quote only

Free and open source

Open source

What drives the bill

The metric that actually scales your invoice.

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.

Free tier

Everything, self-hosted

Open source

MIT

Self-hostable

No-code authoring

Languages

PythonCodeless
JavaScriptTypeScriptPythonJavaRubyPHPGo

Platforms

API
WebAPI

Fits teams

Enterprise
Solo / side projectStartupScale-up / mid-marketEnterprise

Adoption

Emerging

NVIDIA (formerly Gretel) · since 2019

Category standard

OSS community · since 2010

Strengths

  • 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.
  • Realistic, locale-aware data catches layout bugs that 'test test test' never will.
  • Seedable, so generated data is reproducible when a test fails.
  • Available in every major language with a similar API.

Limitations

  • 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.
  • Generated data has no referential integrity or business rules — that is your job.
  • Random data makes failures harder to reproduce unless you seed deliberately.
  • Not a substitute for production-shaped data at realistic volume.

Best for

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

Every project that needs fixtures more realistic than placeholder strings.

Full review
Full review

Pricing last reviewed August 2026 and is indicative only — confirm on each vendor’s own page.