Best Synthetic Data Generation Tools for Privacy

If you are shortlisting the best synthetic data generation tools in 2026, the honest starting point is this: the market splits into a small set of mature options — the open-ecosystem Synthetic Data Vault (SDV), the Apache-licensed MOSTLY AI SDK, Gretel (now absorbed into NVIDIA), and Tonic.ai for test-data workflows — and the deciding factor between them is rarely fidelity. It is whether the tool gives you a defensible privacy story, because “synthetic” is not a synonym for “anonymous,” and the research record says so plainly.

This comparison is drawn from vendor documentation, source repositories, and the governance literature, not hands-on benchmarking. Where a claim is a vendor’s, it is labeled as such.

What actually separates these tools

Every serious generator learns a model of your real data and samples new records from it. The differences that matter to a privacy or GRC team are narrower than the marketing suggests:

1. Privacy mechanism. Does the tool support differential privacy (DP) during training, or does it rely on heuristic protections (outlier removal, rare-category suppression) and post-hoc similarity checks? Only DP gives you a quantifiable bound on what the output can leak about any individual record.
2. Deployment model. A library you run inside your own perimeter has a very different data-flow story than a SaaS platform that ingests production data.
3. Data modality. Single-table tabular is a solved problem; multi-table relational integrity, time series, and free text are where tools diverge.
4. Licensing and vendor continuity. Two of the biggest names changed status in the past two years — one was acquired, one moved off a standard open-source license — and both changes affect procurement.

The tools

SDV (Synthetic Data Vault) — the default library choice

SDV began at MIT’s Data to AI Lab in 2016 and is now maintained by DataCebo. It covers single-table, multi-table, and sequential data, with synthesizers ranging from classical statistics (GaussianCopula) to deep learning (CTGAN), plus built-in evaluation, constraint, and anonymization tooling. It remains actively developed — the repository shows over 100 releases, with v1.37.3 shipping in July 2026.

The procurement caveat: SDV is no longer plain open source. The repository is published under the Business Source License, which restricts certain commercial uses. If your legal team last reviewed SDV in its MIT-licensed era, that review is stale.

MOSTLY AI SDK — permissive license, differential privacy built in

The MOSTLY AI SDK is a Python toolkit released under Apache-2.0, which the project describes as fully permissive. It trains generators on tabular or language data, handles single-table, multi-table, and time-series structures, and supports mixed types including categorical, numerical, geospatial, and text through model architectures including TabularARGN, LSTM, and fine-tuned Hugging Face language models.

For readers of this site, the load-bearing feature is that the SDK exposes differential privacy as a training option. That combination — permissive license, local execution, DP available — makes it the strongest starting point for teams that need to show a regulator or a DPO something more rigorous than “the records don’t match.”

Gretel — now NVIDIA, plan accordingly

Gretel built an API-driven platform for generating synthetic training data with privacy features aimed at developers. In March 2025, NVIDIA acquired the company in a nine-figure deal reported to exceed Gretel’s prior $320 million valuation, with the team and technology folded into NVIDIA’s generative AI services suite.

That is not a knock on the technology — it is a vendor-management fact. If Gretel is on your shortlist, you are now evaluating an NVIDIA product line, and any existing DPA, data-residency commitment, or roadmap assumption from the standalone-Gretel era needs re-papering.

Tonic.ai — synthetic data for test environments

Tonic.ai approaches the problem from the software-testing side with three products: Structural de-identifies, masks, and subsets production databases into safe test data while preserving referential integrity; Fabricate generates fully relational synthetic databases and mock APIs from scratch; and Textual detects, redacts, and synthesizes sensitive information in free-text documents. The platform connects to relational databases, data lakes, NoSQL stores, flat files, and SaaS applications, and the company positions it for HIPAA and PCI-scoped environments.

If your driving use case is “developers need production-like data without production access,” this is the category to shop in — it is a different problem than generating ML training data, and conflating the two is how teams end up with the wrong tool.

The caveat every vendor page omits

Synthetic data is listed in NIST SP 800-188, De-Identifying Government Datasets: Techniques and Governance (September 2023), as one of the recognized de-identification techniques — which tells you it is a legitimate control, not that it is automatically sufficient.

The sharper reference is the widely cited study by Stadler, Oprisanu, and Troncoso, Synthetic Data — Anonymisation Groundhog Day. Evaluating generative models against inference attacks, the authors found that synthetic data “either does not prevent inference attacks or does not retain data utility,” and that the privacy-utility tradeoff is hard to predict — concluding synthetic data is “far from the holy grail of privacy-preserving data publishing.” Outliers and rare records are precisely the ones a well-fitted generator can memorize.

The operational takeaway: treat generation without differential privacy as pseudonymization-adjacent, not anonymization, and test the output with membership-inference and attribute-inference attacks before anyone downstream calls it “not personal data.” That distinction has a precise legal meaning and a specific evidential burden, both of which are set out in pseudonymization versus anonymization under GDPR. How regulators will treat that distinction under the EU AI Act and national privacy law is still settling; the policy tracking at https://neuralwatch.org follows that thread. And if you train production models on synthetic data, monitor them like any other model — distribution shift between synthetic training data and real-world inputs is a live failure mode, which is standard ML observability territory of the kind covered at https://sentryml.com.

How to choose

• Python-native team, broad modality needs: SDV — after legal reviews the Business Source License.
• Defensible privacy posture on tabular data: MOSTLY AI SDK, with differential privacy enabled and the utility cost measured.
• Already in the NVIDIA ecosystem: evaluate the former Gretel tooling inside NVIDIA’s generative AI services, with fresh contractual terms.
• Test-data provisioning, not ML training: Tonic.ai’s Structural/Fabricate pairing.

Whichever you pick, budget for validation. The tool generates the data; proving the data protects anyone is still your job.

Generation is also not the only route to a shareable dataset. Where the requirement is a de-identified copy of real records rather than fabricated ones, the relevant shortlist is a different one entirely, covered in the comparison of data anonymization tools across open source and enterprise tiers.

Related on this site

• How Federated Learning Protects Privacy, and Where It Stops