MOSTLY AI is positioning synthetic data as a production-grade privacy layer with a new SDK touting faster training and native differential privacy. In parallel, market sizing points to synthetic tabular data moving from “nice-to-have” to budget line item, largely driven by regulatory and vendor-risk pressure.
MOSTLY AI Launches AI Synthetic Data SDK Powered by TabularARGN
MOSTLY AI says it released its MOSTLY Artificial Intelligence Synthetic Data SDK powered by TabularARGN in January 2025. The company positions the SDK as a high-fidelity synthetic tabular data generator for analytics, validation, and machine learning workflows, with “flexible deployment options” aimed at organizations that need to control where data and models run.
Key technical claims include training speeds up to 100× faster than earlier methods and “native differential privacy” implemented via DP-SGD. The thrust is operational: generate synthetic datasets that are usable for downstream work while also supporting privacy and governance requirements (including data residency constraints and GDPR-driven compliance expectations).
- Engineering tradeoffs shift: If the 100× speed claim holds in practice, teams can iterate on synthetic-data quality (utility metrics, bias checks, model validation) without waiting days for retraining.
- DP becomes a product feature, not a research project: Bundled DP-SGD can reduce bespoke implementation risk, but it also forces teams to operationalize privacy budgets, evaluation protocols, and documentation.
- Deployment flexibility maps to real constraints: Data residency and internal security policies often block “send data to SaaS” workflows; an SDK posture can fit regulated environments better than a pure hosted service.
AI-Generated Synthetic Tabular Dataset Market Expands to $1.88 Billion in 2025
A market analysis cited by OpenPR estimates 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—with projections reaching $6.73 billion by 2029. The analysis attributes growth to a familiar set of governance drivers: GDPR enforcement, data residency rules, privacy regulation penalties, and more stringent vendor-risk evaluations.
The practical read-through is that synthetic data is increasingly being purchased as a compliance and risk-mitigation control, not only as an ML acceleration tool. That framing tends to change who owns the decision (privacy/compliance and security alongside data science) and how success is measured (auditability, policy alignment, and defensible risk reduction).
- Budget and procurement dynamics are shifting: When synthetic data is justified by regulatory exposure and third-party risk, it competes with security tooling and governance programs—not just ML platform spend.
- Expect tougher evidence requirements: “High fidelity” claims will increasingly need measurable utility and privacy evaluation, plus documentation that stands up to internal audit and external scrutiny.
- Vendor-risk reviews will shape architectures: Data teams should anticipate questions on residency, DP guarantees, model training isolation, and how synthetic outputs are validated before sharing.