Contents
Figure 1: The demo always works — your data is the actual test
Your compliance team just said "no" to sharing production data. Again. You need realistic data for testing, analytics, or model training, and legal needs a six-month review before anyone touches a real record.
Synthetic data skips that wait, but only if you pick the right platform. Enterprise teams buy tools that looked perfect in a demo and collapse on messy, multi-table databases. This guide helps you avoid that mistake.
What Enterprise Teams Actually Need
A hobbyist needs a Python library. An enterprise needs a lot more. Before you compare vendors, write down your non-negotiables.
- Privacy guarantees you can prove. Auditors want metrics and documentation, not promises.
- Multi-table support. Real enterprise data lives in dozens of linked tables, and the links must survive.
- Governance and access controls. Your security team will ask about these in the first meeting.
- Integration with your stack. A great generator that can't connect to your warehouse wastes your time.
- Predictable cost. Surprise consumption bills make finance teams unhappy.
Every vendor claims to check all five boxes. Test those claims on your own data — the benchmarking process in the synthetic data benchmarks article is exactly the test harness for it.
The Market Just Got Reshuffled
Before you read any "top tools" list, check its publication date. The vendor landscape changed recently: NVIDIA acquired Gretel and SAS acquired Hazy, and both deals reshaped this market.
That matters because many older roundups still treat Gretel as a standalone service. Gretel no longer runs as a standalone SaaS product, and its old web address now redirects to NVIDIA. NVIDIA's NeMo Data Designer carries that work forward.
Hazy followed a similar path: Hazy now sits inside SAS Data Maker. If you shortlisted either vendor last year, reread their current offerings before you book a demo.
FYI, the Gretel tutorial on this site described Gretel as a managed service. That description no longer holds — treat the picture here as the correct one.
The Top Platforms, Compared
No platform wins every category, and anyone who tells you otherwise is selling something.
MOSTLY AI: Best for High-Fidelity Tabular Data
MOSTLY AI focuses on privacy-safe tabular data and publishes accuracy reports with every run. It publishes statistical privacy metrics for each job and offers an Apache 2.0 open-source SDK. Auditors love that kind of paper trail.
- Strength: Strong fidelity and a self-service experience for ML and analytics teams.
- Strength: The open-source SDK lets you test before you commit.
- Watch out: One review flags weaker native support for unstructured text than Tonic.ai or NVIDIA NeMo.
IMO, MOSTLY AI makes the safest starting point for teams that mainly work with structured tables.
Tonic.ai: Best for Test Data and Developer Workflows
Tonic solves a slightly different problem. It starts from a real production database and produces a de-identified copy that keeps referential integrity, so developers can query and test against it. That makes it a favorite for engineering teams.
- Tonic Structural handles de-identification of production databases.
- Tonic Fabricate generates synthetic data from a schema you define.
- Tonic Textual extends the approach to unstructured text.
If your biggest pain is "our staging environment has fake-looking junk," Tonic deserves a serious look.
NVIDIA NeMo (Formerly Gretel): Best for AI Pipelines at Scale
NeMo fits teams that already live in the NVIDIA ecosystem. Access runs through NVIDIA's enterprise and cloud channels, and pricing stays custom with no self-serve free tier. That setup suits big AI programs and frustrates small experiments.
If you generate synthetic text or training data for large language models, this platform makes sense. If you just need a tidy customer table, it probably overshoots — the chatbot training article covers what large conversational generation actually requires.
SAS Data Maker (Formerly Hazy): Best for Regulated Industries
Hazy built its reputation in financial services. The product targets regulated industries, though setup can run complex and licensing can challenge smaller teams. Banks and insurers with strict regulatory drivers fit the profile best.
Expect a long sales cycle and a serious implementation project. You trade speed for compliance depth, and many regulated teams happily make that trade.
K2view: Best for Enterprise Test Data Management
K2view takes a lifecycle approach. It covers source extraction, subsetting, pipelining, and synthetic test data operations in one standalone solution. That breadth appeals to organizations with complex, interconnected systems.
The tradeoff is weight: a full lifecycle platform asks for more setup than a focused generator.
YData, Syntho, and Synthesized: Worth a Look
These three round out the shortlist.
- YData Fabric: Combines data profiling with synthetic generation — inspect quality before and after generation.
- Syntho: Emphasizes feature-based enterprise licensing instead of consumption pricing. Finance teams tend to like that.
- Synthesized: Targets complex enterprise systems such as SAP and Oracle.
Don't Forget Open Source
SDV (Synthetic Data Vault) deserves a mention even in an enterprise guide. It's an open-source Python library for tabular, relational, and time-series data. Smart teams use it as a free benchmark to measure what paid tools actually add.
Quick Reference: Which Platform Fits Which Job?
| If you need... | Start with... |
|---|---|
| High-fidelity tabular data with privacy scores | MOSTLY AI |
| Realistic, referentially intact test databases | Tonic.ai |
| Large-scale AI and LLM data pipelines | NVIDIA NeMo |
| Compliance depth for banking and insurance | SAS Data Maker |
| End-to-end test data management | K2view |
| A free baseline to compare against | SDV |
Treat this table as a starting point, not a verdict. Your data decides the winner.
How to Choose Without Regret
Skip the feature checklists vendors send you. Run a proof of concept instead.
- Define the job. Name the exact use case: test data, ML training, or data sharing.
- Pick a messy, representative dataset. Include missing values, rare categories, and linked tables.
- Shortlist two or three vendors. More than that burns your calendar for little gain.
- Score fidelity, utility, and privacy. Run the same checks on every tool, using a held-out slice of real data.
- Measure runtime and effort. A tool that needs weeks of tuning costs real money, even if the license looks cheap.
- Ask security and legal early. They veto deals late, and nobody enjoys that surprise.
Pro tip: ask each vendor to run your data, not their demo dataset. The reaction tells you a lot. :)
A Word on Pricing
Pricing varies widely and changes often, and sources disagree with each other. One 2026 comparison lists MOSTLY AI with a free tier of five credits per day, plus per-credit rates for team and enterprise plans. Another review calls its marketplace tier expensive.
Don't trust any number in a blog post, including this one. Get written quotes, and ask what happens when your data volume doubles. Consumption-based and license-based models behave very differently at scale.
Red Flags to Watch For
Some warning signs show up again and again.
- No published privacy metrics. If a vendor can't show you privacy scores, walk away.
- Single-table demos only. Enterprise data never looks that simple.
- Vague answers about referential integrity. Broken links between tables ruin test data.
- Perfect fidelity scores. Perfection often signals memorized rows, which defeats the purpose — the same memorization trap the fundamentals article warns about.
Good vendors welcome tough questions, and weak ones dodge them.
Recommended Books
| Cover | Book | Description | Get it |
|---|---|---|---|
 |
Designing Data-Intensive Applications | the integration and multi-table reality your platform has to survive. | View on Amazon |
 |
The Data Governance Playbook | what your security and compliance teams will actually ask in that first meeting. | View on Amazon |
 |
Privacy Engineering | how to evaluate "privacy guarantees you can prove" instead of marketing claims. | View on Amazon |
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Frequently Asked Questions
Which synthetic data platform should an enterprise start with?
Match the platform to the job: MOSTLY AI for high-fidelity tabular data with published privacy scores, Tonic.ai for referentially intact test databases, NVIDIA NeMo for large-scale AI and LLM data pipelines, SAS Data Maker for regulated industries like banking and insurance, and K2view for end-to-end test data management across complex systems. SDV is the free open-source baseline every comparison should include.
What happened to Gretel and Hazy?
The market reshuffled: NVIDIA acquired Gretel and SAS acquired Hazy. Gretel no longer runs as a standalone SaaS product — its old web address redirects to NVIDIA, and NVIDIA's NeMo Data Designer carries that work forward. Hazy now sits inside SAS Data Maker. If you shortlisted either vendor a year ago, reread their current offerings before booking a demo, and check any roundup's publication date.
How should enterprises evaluate synthetic data vendors?
Skip the feature checklists and run a proof of concept. Define the exact job (test data, ML training, or data sharing), pick a messy representative dataset with missing values, rare categories, and linked tables, shortlist two or three vendors, score fidelity, utility, and privacy identically on held-out real data, measure runtime and tuning effort, and involve security and legal early. Ask each vendor to run your data, not their demo dataset.
What are the red flags when choosing a synthetic data platform?
No published privacy metrics — if a vendor can't show privacy scores, walk away. Single-table demos only, since enterprise data is never that simple. Vague answers about referential integrity, because broken links between tables ruin test data. And perfect fidelity scores, since perfection often signals memorized rows, which defeats the purpose of synthetic data.
Wrapping This Up
The best synthetic data platform for your enterprise depends on your data and your goal. MOSTLY AI fits tabular fidelity, Tonic.ai fits test data, NVIDIA NeMo fits large AI pipelines, and SAS Data Maker fits regulated industries. SDV gives you a free baseline for every comparison.
Remember that the market shifted: Gretel now lives inside NVIDIA and Hazy inside SAS. Check any roundup's date before you trust it.
So here's the next step. Pick two platforms, run them on the same messy dataset this month, and score the results side by side. You'll spend a few days and save yourself a year of regret. Then tell me which one surprised you — someone always gets surprised.
