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Gretel
Synthetic data platform for generating privacy-safer tabular and text datasets from real sources.
Data and privacy teams that need shareable synthetic datasets and will test them on the actual job.
Buyers who want synthetic data to replace a labeling program, or teams that will not compare outputs to real holdouts.
Verdict
Gretel is a good fit for teams that need synthetic stand-in data when privacy or access blocks the real table. Pricing is mid-range for synthetic-data platforms and usually scales with volume and evaluation needs. Privacy-minded buyers find it credible for stand-in rows; just validate the synthetic set on the downstream task you care about.
Score Breakdown
How Gretel scores in the categories that matter to its buyers.
The Field at a Glance
Where Gretel ranks among Synthetic data vendors we reviewed, by Overall Score and relative typical engagement cost.
Gretel scores 7.1, behind Anonos (7.7) and K2view (7.3) and ahead of Patronus AI (6.7) and Tonic.ai (6.4). Gretel is owned by NVIDIA; the other vendors on this chart are independent.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Shareable stand-in tables | Strong | Works when the team validates results on the real task. |
| Privacy-constrained development | Mixed | Legal and privacy teams still need to approve the approach. |
| Replacing all real eval data | Poor | Keep a real holdout set for evaluation. |
| Rare-event fidelity untested | Poor | Test how well rare events carry over before relying on it. |
Review Excerpts
Excerpts from PeerSpot reviews and from buyer sentiment drawn from public information. Paid reviews and pay-for-play sites were excluded.
“Additionally, my main use case with Gretel.ai involves a production database that contains millions of data points. We need that data for testing but must hide the original data because it has compliance issues. The traditional approach involves getting the data from the database and manually updating it. This is why we use Gretel.ai. It generates synthetic data and tests data settings, providing faster testing and better security with no exposure of customer information to vendors.”
“When I mention better accuracy and performance, I refer to improved model accuracy. Whatever data we are getting has rare scenarios. Gretel.ai can detect those scenarios and generate more examples for training, which reduces manual effort from that perspective, meaning it has better model accuracy and performance.”
“We could give developers a synthetic version of a customer table without sending out the real records.”
Representative buyer view
“We generate synthetic rows, then compare model results against a small real holdout before we trust the dataset.”
Representative buyer view
“Rare events came through less faithfully than common patterns, so we kept real examples for those cases.”
Representative buyer view
Where customer reviews couldn't be found, our analysts attempted to give a voice to buyer sentiment, drawing on real information from review sites, forums, and product documentation.
Based on: PeerSpot's Gretel reviews and TechCrunch's report on NVIDIA's acquisition of Gretel
Company Facts
- Founded
- 2020 (CB Insights)
- Headquarters
- San Diego, California, United States (CB Insights)
- Total funding raised
- $65.5M (CB Insights)
- Status
- Acquired by NVIDIA in March 2025 (CB Insights, TechCrunch)
- Key investors
- NVIDIA, Google Cloud Next, Greylock Partners, Moonshots Capital (CB Insights)
- Website
- gretel.ai
Methodology
This page is an independent evaluation of Gretel for buyers comparing options in synthetic data. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. Gretel did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria fall under it in two groups.
- Buyer outcomes (for synthetic data)
- Synthetic generation
- Privacy use case
- Task validation
- When a mask is enough
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the use-case matrix above.
Scope
Gretel is graded here as synthetic data. Criteria scores can move as more review volume and product checks are added.