The best synthetic data tools depend on what the data's for. Software and QA teams need production-like test data that keeps real customer records out of test, while data science teams need training data that keeps the statistical shape of the original. Pick that job first, then compare generation methods, where the tool runs and who supports it.
Synthetic data has moved from research papers into everyday engineering work, and the number of tools has grown with it. Below are ten of them, in no particular order, grouped by the job they do rather than the size of the company behind them. One thing first: a couple of the names have changed shape, so read the section on ownership before you shortlist.
Test data or training data? Decide before you shortlist
Most of the confusion in this category comes from one phrase doing two jobs. Synthetic data for a QA team means realistic rows that let you run an app, a migration or an integration test without copying real customers into a test environment. Synthetic data for a data scientist means a dataset whose patterns, correlations and edge cases sit close enough to the original that a model trained on it still learns something useful.
Those are different problems. Test data tools care about keeping relationships between tables intact, matching the schema of the system you're testing and refreshing environments on demand. Training data tools care about statistical fidelity and how much of the original could leak through. A tool that's excellent at one can be awkward at the other, so write down which job you're hiring for before you book a single demo.
And if you only need fake names, emails and dates to fill a demo screen, you don't need a platform at all. A mock data generator will do that in minutes.
Four jobs, ten tools
Test data platforms. Tonic.ai generates realistic data, either de-identified from production patterns or built from scratch, and it pitches the same platform at testing and AI training. Synthesized is aimed at enterprise systems, including SAP, and at checking AI agents, producing test data and whole environments inside your own infrastructure. Both are built for teams who already have a production database and need a safe copy of its shape.
General synthetic data generators. Betterdata turns production data into privacy-preserving synthetic data designed to be shared across businesses and borders. Syntheticus uses generative AI to produce synthetic data for analytics and business use. YData pairs synthetic data generation with data quality and pipeline tools for data science teams, and the vendor says it can be deployed on any infrastructure. Two more platforms belong in this group, and they're covered in the next section because their names now point to each other.
Pipelines for agentic AI. One big name sits apart from the rest. It's built for teams creating synthetic conversations and agent workflows to train or check AI systems, not for filling a test database.
Open-source and lightweight tools. SDV, the Synthetic Data Vault, is a Python library for synthetic tabular data maintained by DataCebo, so you can run it yourself and read the code. Mockaroo sits at the other end of the scale: pick your columns, choose CSV, JSON, SQL or Excel, and download a dataset to test or demo software. It'll also mock an API, which is handy when the real one isn't ready.
Check who's behind the product
Here's the thing most roundups miss. Two of the names in this category have changed shape. The MOSTLY AI website now describes itself as powered by Syntho, which also has its own entry on this list, and Gretel's old web address now lands on an NVIDIA page about synthetic data for agentic AI, which is where NVIDIA NeMo comes in.
None of that means the products are worse. It does mean that a review written a year or two ago may describe a company, a roadmap or a support team that has since changed. If you're building a test data pipeline that'll run for years, you want to know who owns the product, who answers the support ticket and whether the version you trial is the one that'll still be maintained.
So ask three plain questions in every demo: who owns this product today, what's changed since the last major release, and how long will the version we're buying be supported. Get the answers in writing.
Ten synthetic data tools side by side
| Tool | Type | Good for |
|---|---|---|
| Betterdata | Programmatic synthetic data | Sharing privacy-preserving data across businesses and borders |
| Mockaroo | Mock data generator | Quick CSV, JSON, SQL or Excel test datasets |
| MOSTLY AI | Synthetic data platform and SDK | Generating and sharing privacy-safe synthetic data |
| NVIDIA NeMo | Synthetic data pipelines | Training data for conversational and agentic AI |
| SDV | Open-source Python library | Synthetic tabular data you generate yourself in Python |
| Synthesized | Test data management | Production-faithful test data for enterprise systems and agents |
| Syntheticus | Synthetic data generation | Generative AI synthetic data for analytics |
| Syntho | Synthetic test data platform | Masking, rule-based and AI-generated data in one place |
| Tonic.ai | Test and AI data platform | Realistic test data from production patterns or from scratch |
| YData | Data quality and synthetic data | Managing data assets and pipelines for data science |
Every tool, A to Z
Betterdata
Best for: Teams turning production data into privacy-preserving synthetic data they can share across businesses and borders, which the vendor calls highly realistic.
Mockaroo
Best for: Developers who need quick custom CSV, JSON, SQL or Excel datasets to test and demo software, plus mocked APIs; the vendor names a free plan.
MOSTLY AI
Best for: Data teams wanting a platform to generate, analyse and share privacy-safe synthetic data, with an open-source SDK alongside it.
NVIDIA NeMo
Best for: AI builders creating generation pipelines for conversational and agentic AI, aimed at what the vendor calls domain-specific synthetic data for agentic workflows.
SDV
Best for: Python users generating synthetic tabular data with the open-source Synthetic Data Vault library, maintained by DataCebo.
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Synthesized
Best for: Enterprise QA teams needing production-faithful test data and environments, including for SAP systems and AI agent checks, created inside their own environment.
Syntheticus
Best for: Analytics and business teams wanting synthetic data built with generative AI at scale, which the vendor says closely mimics real-world data.
Syntho
Best for: Test data managers who want synthetic data masking, rule-based generation and AI-generated synthetic data in one platform, as the vendor puts it.
Tonic.ai
Best for: Development and AI teams needing realistic data, de-identified from production patterns or generated from scratch, for testing and model training.
YData
Best for: Data science teams who want to manage data assets, generate synthetic data and build pipelines in one product the vendor describes as Kubernetes-native.
My advice for data and QA teams
Start small and real. Take one table or one service you know well, run it through two shortlisted tools and compare the output against what your tests or models need. A demo dataset always looks good; your own messy schema won't. Check relationships across tables, how the tool handles rare values and free-text fields, and whether you can schedule refreshes so test environments don't drift.
Keep compliance where it belongs. Synthetic data can reduce how much real personal data moves around your business, but the rules on what counts as personal data and how it can be processed vary by country and sector. Check with your regulator and your own data protection lead before you treat any synthetic dataset as outside those rules. If you're also preparing training data by hand, my guide to data annotation tools covers the labelling side, and my explainer on synthetic data is a gentler starting point if the topic's new to your team.
Questions to settle before you load real records
- Is this for test data, training data or both, and which matters more?
- Will the tool run inside our own infrastructure, or will production data leave it?
- How does it keep relationships between tables intact?
- Can we measure how close the synthetic output is to the original, and how much could be traced back to real people?
- Who owns and supports the product today, and what happens to support if that changes?
Do you make a synthetic data tool? Tool makers can ask to be considered for a featured place on this list.
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Frequently asked questions
What's synthetic data used for?
Mostly two things: realistic test data, so developers and QA teams don't need copies of real customer records, and training data for machine learning models when real data is scarce, sensitive or unbalanced.
Is synthetic data the same as masked data?
Not quite. Masking changes or hides values in a real dataset, so each row still maps to a real record. Synthetic data is generated, so rows don't belong to real people, though some platforms combine both methods.
Can synthetic data replace real data for training models?
It can fill gaps and protect privacy, but most teams still check a model against real data before it goes live. Treat synthetic data as a supplement and measure how well it matches the original.
Do I need a platform or an open-source library?
If you have Python skills and a few datasets, a library can be enough. If you need scheduled refreshes, many connected databases and vendor support, a platform will save time.
Does synthetic data remove privacy obligations?
Don't assume so. Rules vary by country and sector, and poorly generated data can still reveal information about real people. Check with your regulator and your data protection lead.
How do I judge the quality of synthetic data?
Compare it with the original on what matters to you: distributions, relationships between columns, rare cases and whether your tests or models behave the same way on both.
