Two signals from the same OpenPR report: MOSTLY AI is pushing synthetic data generation toward faster, DP-enabled production workflows, while market forecasts suggest compliance pressure is turning synthetic tabular data into a mainstream procurement line item.
MOSTLY AI Launches AI Synthetic Data SDK Powered by TabularARGN
MOSTLY AI announced the release of its MOSTLY Artificial Intelligence Synthetic Data SDK powered by TabularARGN (released 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.
Key claims include training speeds up to 100× faster than earlier methods, native differential privacy via DP-SGD, and flexible deployment options. The emphasis is on enabling organizations to use synthetic data while maintaining differential privacy guarantees and meeting constraints such as GDPR and data residency requirements.
- DP moves from “checkbox” to implementation detail: Native DP-SGD matters because teams can standardize privacy controls in the training loop instead of bolting on ad hoc anonymization.
- Faster training changes the operating model: If the 100× speed claim holds in your workload, synthetic generation can shift from a periodic batch job to an iterative, CI-like pipeline that supports frequent model and data validation.
- Deployment flexibility aligns with residency constraints: Options that support where data must live (and where models can run) reduce friction for regulated environments and cross-border programs.
AI-Generated Synthetic Tabular Dataset Market Expands to $1.88 Billion in 2025
The AI-generated synthetic tabular dataset market is reported to have grown 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.
Drivers cited include GDPR enforcement, data residency rules, privacy regulation penalties, and increased vendor-risk evaluations. In practice, that framing suggests synthetic data is increasingly being purchased not only for model development speed, but as a governance and risk-mitigation mechanism when direct use of sensitive data is constrained.
- Procurement is being pulled by compliance, not novelty: Growth tied to enforcement and penalties indicates synthetic data is becoming a budgeted control for privacy and residency risk.
- Vendor risk reviews will get stricter: Expect more scrutiny on how “synthetic” is produced (DP guarantees, leakage testing, documentation) as third-party risk teams formalize evaluation criteria.
- Governance teams need measurable assurances: Market expansion will reward vendors and internal platforms that can evidence privacy properties and support audit-ready reporting.