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.
If your goal is training or fine-tuning AI models on sensitive data while minimizing exposure of real users, the market has a handful of serious providers. The important distinction is that “synthetic” does not automatically mean private: the strongest offerings combine synthesis with controls such as differential privacy, PII removal, memorization testing, and secure/private deployment.
Leading providers
Provider
Particularly strong for
Privacy approach
AI-training fit
NVIDIA / NeMo
Enterprise AI, tabular data, LLM/agent training
NeMo Safe Synthesizer supports PII replacement, synthetic generation, privacy evaluation and optional differential privacy (DP); Data Designer generates training data from scratch or seed data
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
Very broad — tabular, text, instruction/agent data
MOSTLY AI
High-fidelity tabular/relational data
Built-in privacy protections plus configurable differential privacy, epsilon tracking, rare-value and outlier protection
Excellent for structured ML data
Gretel / NVIDIA
Multimodal synthetic data, particularly text and tabular
Differential privacy, privacy filters and defenses against re-identification/joinability attacks
Strong for LLM and multimodal workflows
Tonic.ai
Enterprise databases, unstructured documents and LLM training
PII detection/redaction, synthetic replacement, differential privacy and de-identification
Strong for production-derived enterprise data
SAS Data Maker
Regulated enterprise analytics
Synthetic-data generation plus statistical quality evaluation and enterprise governance
Strong for enterprise/regulated environments
Synthetic Data Vault (SDV)
Developers/researchers wanting an open-source option
Privacy must be designed and evaluated by the user; useful synthesis algorithms but not inherently a privacy guarantee
Excellent DIY/developer option
1. NVIDIA NeMo
NVIDIA has become particularly interesting because it covers two somewhat different problems.
NeMo Data Designer is for generating large training datasets from scratch or from relatively small seed sets. It handles field dependencies, batching, validation and statistical diversity.
For genuinely sensitive source data, NeMo Safe Synthesizer is the more relevant product. It creates synthetic versions of tabular datasets with no one-to-one mapping to source records, supports PII replacement, and can use DP-SGD for formal differential-privacy guarantees.
MOSTLY AI is one of the strongest options if your data is structured: customer records, transactions, healthcare tables, longitudinal records, etc.
It has explicit differential-privacy controls, including an epsilon budget, gradient clipping and noise controls. It also has protections against rare categories, extreme values and unusually long sequences that could expose individuals.
It offers an open-source SDK as well as its enterprise platform.
Gretel was built specifically around privacy-preserving synthetic data and supports tabular, text and other data modalities. Its documentation describes differential privacy as a way to prevent models from memorizing individual training examples, while its privacy mechanisms also address re-identification and joinability attacks.
Current 2026 market sources identify Gretel as part of NVIDIA following NVIDIA's acquisition, so I'd evaluate it today largely within the NVIDIA ecosystem rather than as an entirely separate vendor.
Tonic is particularly compelling if your starting point is messy enterprise production data rather than a clean ML dataset.
Its products can synthesize relational databases while maintaining relationships, and Tonic Textual can detect sensitive entities in documents, email and other unstructured data and replace them with synthetic values while retaining context.
Tonic also documents differential-privacy capabilities and offers synthetic datasets specifically for AI training and evaluation.
I wouldn't treat a vendor's statement that synthetic data is "anonymous" or "privacy-safe" as sufficient by itself.
For particularly sensitive user data, I'd look for:
Differential privacy with a disclosed/controllable ε and δ.
Membership-inference and record-reconstruction testing.
Nearest-neighbor / memorization testing to determine whether synthetic records are too close to real ones.
PII detection before generation, rather than assuming the generator will never learn sensitive identifiers.
On-prem/private-cloud deployment when raw data cannot leave your environment.
Separate evaluation of utility vs. privacy—higher fidelity can sometimes increase disclosure risk.
A clear statement about whether the provider retains source data or uses it to train its own models.
For example, MOSTLY AI explicitly exposes DP parameters and privacy budgets, while NVIDIA's Safe Synthesizer lets users combine PII replacement, synthetic generation, privacy evaluation and differential privacy.
Practical shortlist
If I were building an evaluation set rather than ranking vendors:
LLM/text training: Tonic.ai, Gretel/NVIDIA, NVIDIA NeMo Data Designer
Enterprise production databases: Tonic.ai
Highly controlled enterprise AI infrastructure: NVIDIA NeMo
Open-source/developer experimentation: MOSTLY AI SDK / SDV
Regulated analytics environments: SAS Data Maker
One particularly important distinction is synthetic data generated from scratch versus synthetic data learned from your users' actual records. The latter can be much more useful, but it's also where formal privacy mechanisms such as differential privacy become especially valuable.
If you tell me whether you're interested in LLM training, computer vision, healthcare, financial data, or general enterprise/customer data, I can narrow this to the 3–5 providers most relevant to that use case and compare privacy guarantees, deployment model, data modalities, and pricing.
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)
