Executive Summary
Synthetic data is only useful if it is realistic enough to trust and different enough to share. Every product in this guide is a trade-off between the two.
Synthetic data is generated to match the patterns of real data without containing real records. Teams use it to share data across departments and partners, to fill test environments without copying production, and to train or evaluate AI models when real data is scarce or sensitive. The data privacy guide covers consent and subject rights; the test automation guide covers test execution. This guide covers producing the data.
The approaches differ by use case and data type. Tabular synthetic data platforms serve analytics and data sharing. Test data platforms serve engineering teams. Open-core libraries serve data scientists. Design-driven generators create data from rules without touching production. Platforms for AI generate text, images and simulations for model training.
Why Synthetic Data Is a Separate Purchase
Privacy rules and security risk limit who can use production data. Masking removes direct identifiers but can leave records re-identifiable, and copying production into test environments widens the attack surface. Synthetic data offers another route, and public bodies recognize it. NIST says agencies should decide upon a data-sharing model, such as publishing de-identified data, publishing synthetic data based on identified data, providing a query interface that incorporates de-identification, or sharing data in non-public protected enclaves.
Privacy guarantees vary by product. MOSTLY AI describes generating high-fidelity synthetic data with built-in differential privacy using the TabularARGN model architecture, with support for complex tabular and textual datasets. DataCebo describes creating synthetic data with differential privacy guarantees. Ask each vendor which privacy measure it reports and what an attacker could still learn.
Open Source, Platform, or Test Data Suite
Teams choose among open-source libraries run by data scientists, commercial platforms with governance and privacy reporting, or test data suites that combine masking, subsetting and generation. Open source is flexible and cheap to start. Platforms add evaluation, access controls and support. Test data suites fit engineering workflows and database relationships.
Some products run where the data is. MOSTLY AI describes creating synthetic data locally in a Python environment, with the user in full control of the data. Test data products keep relational structure intact. Tonic.ai describes transforming sensitive production databases into high-fidelity, referentially intact test data. Syntho describes combining synthetic data masking, rule-based synthetic data and AI-generated synthetic data in one synthetic test data management platform.
| Model | What you get | What you take on |
|---|---|---|
| Open-source library | Models and code for generation | Evaluation, governance and support |
| Synthetic data platform | Generation, privacy reporting and sharing | A platform to run and license |
| Test data suite | Masking, subsetting and generation for testing | Integration with databases and pipelines |
| Design-driven generator | Data from rules without production access | Writing and maintaining the rules |
| AI data platform | Text, image or simulation data for models | Validating that models learn the right things |
How do you evaluate Synthetic Data Generation?
Evaluate these products on your own data. The capabilities that decide results are fidelity, privacy measurement, relational and multi-table support, unstructured data, rule and constraint control, deployment location, and integration with pipelines.
Unstructured and AI uses are growing. Tonic.ai describes detecting, redacting and synthesizing sensitive data in unstructured datasets to develop and fine-tune LLM and AI models. NVIDIA says NeMo Safe Synthesizer creates privacy-safe versions of sensitive data with default configurations designed to meet data privacy regulations such as HIPAA and GDPR. Rendered.ai describes simulation for any CV sensor type, including Synthetic Aperture Radar (SAR), infrared, multispectral, hyperspectral, X-ray, EO and RGB.
| Capability | What it does | Buyer translation |
|---|---|---|
| Fidelity | Matches statistics and relationships | Compare analyses on real and synthetic data. |
| Privacy measurement | Reports re-identification risk | Ask which measures and thresholds apply. |
| Multi-table support | Keeps keys and relationships | Essential for test databases. |
| Unstructured data | Text, documents and images | Match to your AI use cases. |
| Rules and constraints | Controls values and edge cases | Needed for test scenarios. |
| Deployment | Runs in your environment or the vendor's | Decides whether real data leaves. |
| Pipeline integration | Feeds CI, data platforms and ML | Ask about APIs and connectors. |
| Governance | Approvals, lineage and audit | Shows who released which dataset. |
Vendors in Synthetic Data Generation
Representative vendors include DataCebo, GenRocket, K2view, MOSTLY AI, NVIDIA and Tonic.ai. The list is alphabetical, is not a ranking, and is not for sale. How we choose vendors
The approaches below differ by use case and data type. Several vendors cover more than one, so each approach describes where a product is strongest.
This market has consolidated quickly. A request for gretel.ai is answered by nvidia.com. YData says it has been acquired by KPMG. MOSTLY AI's site describes MOSTLY AI as powered by Syntho. Ask any vendor that has changed hands about roadmap, pricing and support.
| Vendor | Approach | Where it fits |
|---|---|---|
| K2view | Data product platforms | Governed data products for testing and AI |
| GenRocket | Design-driven generation | Rule-designed synthetic test data |
| DataCebo | Open-core libraries | SDV open-core synthetic data |
| NVIDIA | Synthetic data for AI | Synthetic data for agentic and physical AI |
| MOSTLY AI | Tabular synthetic data | Synthetic tables with differential privacy |
| Tonic.ai | Test data platforms | Test data and unstructured de-identification |
One vendor was chosen for each approach after the approaches were written, and a row here is not for sale. About twenty vendors sell synthetic or test data products. Vendors, named here or not, can present their own case in the Spotlight below.
How much should you budget for Synthetic Data Generation?
Synthetic data is priced by usage, data volume, users or platform subscription, and test data suites by environments and databases. Tonic.ai says its Free tier includes $5 in monthly usage credits, with no credit card required. Tonic.ai says Fabricate usage is measured in tokens. Ask each vendor for the pricing unit and how it grows with tables, rows and environments.
The value shows up in faster test cycles, data shared that could not be shared before, models trained on data that was previously off-limits, and fewer copies of production data. Model it on one use case before expanding.
| Meter | You are charged for | Grows with | Where it goes wrong |
|---|---|---|---|
| Usage or tokens | Data generated | Volume | Repeated generation of the same data |
| Data volume | Rows, tables or databases | Scope | Paying for unused tables |
| Users | Data scientists and engineers | Team size | Seats for occasional users |
| Environments | Test environments served | Pipelines | Each environment priced separately |
| Deployment | Self-hosted or vendor cloud | Security needs | Self-hosting in top tiers only |
How long does implementation take for Synthetic Data Generation?
Synthetic data programs depend on agreed measures of privacy and usefulness. Set them with privacy and data owners before generating anything.
Pick one dataset and one use. Agree privacy and fidelity measures with data owners and privacy staff.
Generate data, run the agreed tests, and record results and approvals.
Connect to the pipeline or environment that will use the data. Retire the production copy it replaces.
Add datasets and uses. Re-evaluate when source data or models change.
What should you ask vendors about Synthetic Data Generation?
Ask each vendor to answer on your data and use cases.
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Is the main use software testing?Yes Shortlist test data platforms and design-driven generators.No Continue.
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Is the main use analytics or data sharing?Yes Shortlist tabular synthetic data platforms.No Continue.
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Is the main use training AI models?Yes Shortlist synthetic data for AI.No An open-core library may be enough to start.