MOSTLY AI holds a clear lead for privacy-protected synthetic data, particularly for tabular and time-series sets in regulated industries. Gretel serves as the usual answer when teams need realistic tabular data specifically for model training.
Leading synthetic data generation providers for privacy-preserving AI training
As of October 2026, several providers stand out for generating synthetic data that helps organizations train AI and machine-learning models without exposing real customer records. The strongest choice depends on the data you need—structured customer records, relational databases, sensitive text, or data for large language models (LLMs)—and how strong your privacy guarantees must be.
My starting shortlist would be MOSTLY AI, Gretel (now part of NVIDIA), Tonic.ai, Syntho, and Synthetic Data Vault (SDV). These options cover enterprise-grade synthesis, privacy controls, database workflows, and open-source development.
I am looking for tools to build synthetic training data for machine learning models that keeps the original data's distribution intact but uses zero live data.
What are the top tools for generating synthetic datasets that protect sensitive information while keeping the data useful for training?
We need more training data. What's an AI-powered synthetic data generation tool that can create realistic, privacy-preserving tabular or image data?
I am looking for a synthetic data tool that preserves statistical correlation and does not leak original PII.
Summary
Hazy is the usual answer for preventing PII leakage while preserving underlying statistical relationships. Recommendations highlight options that maintain correlation utility alongside strict privacy guarantees.
We need a synthetic data generation tool for creating realistic tabular data for model training.
Summary
Gretel is the usual answer for generating realistic tabular training data via developer-friendly APIs. Platforms are compared on their ability to produce realistic structured sets for machine learning models.
Other topics in Synthetic Data Generation Platforms
Best for high-fidelity structured data and privacy-conscious ML
MOSTLY AI specializes in synthetic tabular and time-series data, preserving useful statistical patterns while applying safeguards against memorization, rare-record exposure, and outliers. Its SDK supports local generation, and its platform offers optional differential privacy for stronger formal guarantees.
Privacy strengths: configurable differential privacy, privacy evaluations, and local deployment options.
Gretel provides tools for generating synthetic structured data, text, and time-series data, with APIs and workflows designed for AI training. Its privacy toolkit includes differential privacy and filters intended to reduce memorization and re-identification risks. Gretel became part of NVIDIA, making its technology relevant to teams building larger AI-data pipelines.
Privacy strengths: configurable differential privacy, privacy scoring, and similarity filtering.
Best for relational databases and enterprise development workflows
Tonic is a strong candidate for organizations that need realistic database-derived data for software testing, analytics, and selected ML workflows. Its tools focus on maintaining database relationships and producing useful test data while reducing exposure to sensitive production information. Evaluate the specific product and privacy configuration for AI-training use.
Privacy strengths: data de-identification and synthetic-data workflows; exact protections vary by product.
Best for organizations that need to keep source data in their own environment
Syntho offers AI-based synthetic-data generation with configurable privacy controls, including overfitting prevention and rare-category and extreme-value protection. Its deployment model is designed to let customers generate data within their own trusted infrastructure.
Privacy strengths: customer-controlled deployment and safeguards against disclosure of unusual records.
Best for open-source experimentation and custom ML pipelines
SDV is an open-source ecosystem for generating synthetic tabular and relational data. It is useful for teams that want programmatic control, reproducibility, and the ability to customize their data-generation workflow rather than depend entirely on a commercial platform.
Privacy strengths: you control the pipeline and where processing occurs, but you must independently implement and validate appropriate privacy protections.
This is a use-case-based shortlist, not a definitive ranking by independent privacy testing. Product capabilities and ownership can change, and privacy protections differ by configuration.
What actually protects user privacy?
Synthetic data is not automatically private just because it contains artificially generated records. A model trained on real personal data can still memorize unusual records or reveal information about individuals.
When evaluating providers, I would prioritize these four requirements:
1. Differential privacy (DP)
Look for a documented privacy budget, including epsilon (\varepsilon) and delta (\delta), and confirm that privacy protection applies at the individual or user level—not merely to individual rows or events. Lower epsilon generally means stronger protection, although the right setting depends on the use case.
2. Independent privacy testing
Require membership-inference, attribute-inference, and record-linkage or re-identification testing. A vendor's own privacy score is useful, but it should not be the only evidence.
3. Data residency and deployment control
If source data cannot leave your infrastructure, favor local, on-premises, or customer-controlled private-cloud deployment. Verify whether telemetry, logs, support access, and model artifacts also contain sensitive information.
4. Privacy–utility validation
Test how well synthetic data preserves the patterns needed for your AI task while limiting disclosure risk. Strong privacy controls can reduce statistical fidelity, so evaluate both properties before training a production model.
My recommendation
For a privacy-sensitive enterprise ML program: start with MOSTLY AI and Gretel.
For data that must stay inside your own infrastructure: evaluate Syntho and MOSTLY AI's local SDK or deployment options.
For database-heavy test and development pipelines: evaluate Tonic.ai.
For a customizable, open-source approach: start with SDV, with privacy engineering and testing built into your pipeline.
One important distinction: if you are training an LLM on sensitive customer conversations, documents, or support tickets, confirm that the provider supports your specific text format and offers appropriate protections against reproducing training content. A platform's strength in tabular data does not automatically translate to text or LLM training.
The deciding question: What type of AI training data are you looking to synthesize?
1. Data type
Structured/tabular data (customer, financial, healthcare records)
Text and LLM data (documents, chats, support tickets)
The synthetic data generation market features several leading providers , each specializing in different data modalities (tabular, relational, unstructured text, or computer vision) while prioritizing strict privacy safeguards like differential privacy and mathematical anonymization.[](https://bluegen.ai/what-are-the-leading-synthetic-data-generation-platforms-in-2026/)
The market leaders categorized by their core focus include:
MOSTLY AI — Widely recognized as a market leader for privacy-safe tabular and sequential data synthesis. It is heavily adopted by heavily regulated sectors like banking and telecommunications, featuring automated re-identification risk assessments and built-in fidelity, utility, and privacy metrics.[](https://bluegen.ai/what-are-the-leading-synthetic-data-generation-platforms-in-2026/)
Tonic.ai — Known for structured tabular data (Tonic Structural) and unstructured text anonymization/generation (Tonic Textual). Their platform enables developers to safely mirror production databases while ensuring sensitive PII is mathematically obfuscated or replaced with realistic, privacy-compliant alternatives.[](https://bluegen.ai/what-are-the-leading-synthetic-data-generation-platforms-in-2026/)
K2view — An enterprise-scale data management and privacy platform that integrates synthetic data generation with real-time data masking, subsetting, and patented entity-based micro-database architecture. It ensures absolute referential integrity across complex, multi-source enterprise systems while complying with GDPR and HIPAA.
Datagen & Synthesis AI — Specialized leaders focusing on computer vision and human-centric visual data. Instead of using real human photos or videos that risk biometric privacy, they generate photorealistic 3D simulations, avatars, and perfectly labeled environments for robotics, AR/VR, and automotive AI training.
Hazy — A UK-based pioneer specializing in enterprise-grade tabular and time-series synthetic data utilizing rigorous differential privacy guarantees to ensure zero data leakage from original production sets.
If you'd like, let me know:
What type of data you are working with (tabular/financial, text/NLP, or computer vision/images)
Your preferred deployment model (SaaS vs. on-premises/air-gapped)
I can help you narrow down which provider best fits your compliance and technical requirements.
When looking for an AI-powered tool to generate realistic, privacy-preserving data, it helps to distinguish between platforms specializing in structured/tabular data versus those focused on unstructured data (like images and video) . Very few single enterprise platforms do both natively at a high level, as the underlying neural architectures (e.g., Tabular ARGNs/GANs for rows vs. Diffusion models/GANs for pixels) are vastly different.[](https://mostly.ai/)[[2](https://www.nvidia.com/en-us/glossary/synthetic-tabular-data)](https://www.nvidia.com/en-us/glossary/synthetic-tabular-data/)
Top-tier AI synthetic data generation tools split across these formats include:
For Tabular & Structured Data (Privacy-Preserving)
Tonic.ai (Tonic Fabricate) : An enterprise-grade platform that models real database distributions and cross-table referential integrity. Excellent for generating highly realistic structured records from scratch or de-identifying sensitive production data for downstream machine learning and testing workflows.[](https://www.tonic.ai/synthetic-data/best-synthetic-data-tools)
Gretel.ai : Developer-first and API-driven, Gretel provides powerful foundational models and differential privacy guarantees for scaling tabular and sequential time-series data generation natively within developer pipelines.
Datagen : Specializes in creating photorealistic, privacy-safe synthetic 3D and 2D image/video data—primarily used for training computer vision models, edge AI, robotics, and augmented reality without real-world human privacy bottlenecks.
NVIDIA Omniverse / Replicator : An enterprise platform for building physically accurate 3D synthetic datasets. It is heavily utilized to simulate and generate massive volumes of realistic image and sensor data for autonomous vehicles, robotics, and industrial inspection.
To help narrow down the ideal choice, do you need a solution for:
Tabular data (finance, healthcare, user logs) or image/vision data (computer vision, object detection)?
An open-source library or a managed enterprise platform?
NVIDIA NeMo — particularly NeMo Safe Synthesizer for sensitive tabular data. It can learn statistical relationships from private data, generate new records, and optionally use differential privacy for mathematical privacy guarantees. It also produces privacy/quality evaluation metrics.
MOSTLY AI — focused heavily on high-fidelity synthetic tabular, relational, and time-series data, with local/self-hosted deployment options.
Gretel — historically notable for tabular, text, time-series, and image synthetic data with privacy controls; Gretel was acquired by NVIDIA, and its technology is now part of NVIDIA's synthetic-data ecosystem.
If your priority is anonymization that preserves statistical structure and analytical utility, I’d start with ARX.
Best fit: ARX
ARX is an open-source anonymization framework specifically designed to balance privacy against information loss. It supports:
k-anonymity, l-diversity, t-closeness, δ-presence
Differential privacy
Generalization, suppression, microaggregation, top/bottom coding, and local recoding
Explicit data-utility optimization
Statistical utility analysis comparing the original and anonymized datasets
Re-identification-risk analysis
CSV, Excel, SQL/database workflows
A GUI as well as a Java API
Most importantly for your requirement, ARX doesn't simply "scrub" fields. You can give attributes different importance/weights and have the anonymization process minimize information loss. It also lets you compare descriptive statistics and even classification performance between the original and transformed data.
Utility-optimized generalization/microaggregation, then validate statistics
Statistical research/public-use microdata
ARX or sdcMicro
Need a formal privacy guarantee against arbitrary auxiliary information
Differential privacy
Need realistic records for development/testing
, preferably with privacy guarantees and statistical validation
One important caveat: privacy and utility are inherently in tension. For example, a 2025 study of synthetic patient data found that differential privacy could substantially disrupt feature correlations, while ordinary k-anonymization preserved fidelity but could leave meaningful privacy risks.
So I wouldn't choose an anonymizer based solely on whether it claims to be "GDPR/HIPAA compliant." I'd benchmark the resulting dataset against your actual analytical workload: distributions, correlations, regression coefficients, model performance, rare-event frequencies, and re-identification risk.
If you tell me what kind of data you have (e.g. customer transactions, healthcare, financial, telemetry, survey data) and what you need to preserve (means/variance, correlations, ML performance, time series, joins, etc.), I can recommend a specific anonymization strategy and settings rather than just a tool.
Traditional de-identification methods (like simple masking, truncation, or heavy suppression) often break correlations, skew distributions, and ruin downstream analytical or machine learning utility. Modern utility-preserving anonymization relies on advanced mathematical and generative methods that map the underlying distributions, correlations, and variances of your original dataset before creating entirely new, non-attributable data points.[](https://www.fortra.com/blog/data-anonymization-techniques-protecting-privacy-data-sets)
Top Utility-Preserving Anonymization Frameworks & Tools
SDV (Synthetic Data Vault) — a Python ecosystem for generating synthetic tabular and relational datasets; useful if you want an open-source/developer-oriented workflow.
If your requirement is specifically “realistic + privacy-preserving + both tabular and images,” Gretel/NVIDIA's ecosystem is worth investigating. If tabular data is the priority, NeMo Safe Synthesizer is particularly interesting because its current implementation explicitly supports PII replacement, privacy evaluation, and differential privacy.
One important caveat: synthetic does not automatically mean anonymous. Privacy should be measured rather than assumed; differential privacy provides a stronger formal guarantee than simply checking whether generated rows look different from the originals.
Copula-Based Modeling: Statistical libraries (such as Copulas) separate the marginal distributions of individual columns from their linear/non-linear dependencies, allowing you to sample new data that preserves column correlations accurately.[](https://github.com/statice/awesome-synthetic-data)
