Synthetic tabular data is getting both more productized and more budgeted: MOSTLY AI is pushing faster generation with native differential privacy, while market forecasts tie growth directly to GDPR enforcement, data residency rules, and governance pressure.
MOSTLY AI Launches AI Synthetic Data SDK Powered by TabularARGN
MOSTLY AI said it released its MOSTLY Artificial Intelligence Synthetic Data SDK powered by TabularARGN in January 2025. The company positions the SDK as a production-oriented way to generate high-fidelity synthetic tabular datasets for analytics, validation, and machine learning workflows, with flexible deployment options aimed at organizations that need control over where data processing happens.
Key technical claims include training speeds up to 100 times faster than earlier methods and “native differential privacy” implemented via DP-SGD. The release is framed around privacy-preserving synthetic data generation for regulated environments, including requirements tied to GDPR and data residency constraints.
- DP moves from marketing to implementation detail. If DP-SGD is truly “native” in the SDK, privacy guarantees become something teams can test, configure, and audit—rather than a vendor promise.
- Speed changes the operating model. A claimed 100× training speed-up can make iterative privacy/utility tuning feasible in CI-style pipelines instead of one-off research runs.
- Deployment flexibility maps to residency and procurement. Options that support data-local generation can reduce cross-border transfer exposure and simplify internal risk reviews for synthetic data projects.
AI-Generated Synthetic Tabular Dataset Market Expands to $1.88 Billion in 2025
OpenPR’s market analysis reports the AI-generated synthetic tabular dataset market grew from $1.36 billion in 2024 to $1.88 billion in 2025, described as a 37.9% compound annual growth rate. The same analysis projects the market reaching $6.73 billion by 2029.
The report attributes demand to governance and risk drivers: GDPR enforcement, data residency rules, penalties tied to privacy regulation, and vendor-risk evaluations. In other words, synthetic data is being positioned less as an experimentation tool and more as a compliance and risk-mitigation mechanism that can unblock data sharing and model development under tighter controls.
- Compliance is becoming the primary budget line. When growth drivers are explicitly regulatory (GDPR, residency, penalties), buyers will expect evidence: privacy guarantees, documentation, and repeatable controls.
- Vendor-risk scrutiny will rise with spend. As synthetic data becomes a governance control, procurement and security teams will push for clearer answers on leakage risk, DP settings, and evaluation methodology.
- Data teams should expect “prove it” requirements. Market expansion tends to standardize checklists—utility metrics, re-identification testing, and audit artifacts—before synthetic data is allowed into production ML workflows.