Synthetic data software
Generate realistic, privacy-safe synthetic data for testing, development, and analytics — choose from masking, rule-based, or AI generation methods.
| What is it | Generate realistic, privacy-safe synthetic data for testing, development, and analytics — choose from masking, rule-based, or AI generation methods. |
|---|---|
| Pricing | Contact for Pricing |
| Free tier | No |
| Platform | Web Application |
| API | Yes |
| Best for | Generating realistic test data for software QA and staging environments, Creating privacy-protected datasets for analytics and AI model training |
| Domain registered | 2019 |
Data updated Aug. 1, 2026
What does Synthetic data software do?
Synthetic data software is a platform that generates realistic, privacy-preserving synthetic data for testing, development, and analytics. It replaces the need for production data in non-production environments by creating artificial data that looks and behaves like the real thing. The tool offers three generation methods: synthetic data masking (scrambles or removes personally identifiable information), rule-based generation (creates data from predefined patterns and formulas), and AI-generated synthetic data (uses machine learning models to replicate statistical patterns). You can switch between methods or combine them in a single run depending on your use case.
The platform is designed to be deployed in your own environment — on-premises, in a VPC, or in your cloud account — so the raw data never leaves your control. After deployment, you connect to source and target databases using built-in connectors. The user interface lets you select a generation method, configure parameters, and run a job. You can also automate recurring data refreshes using the API. One standout feature is the ability to generate edge cases and hypothetical future scenarios, which is useful for testing rare conditions or building more robust models. The quality assurance report provides metrics on how well the synthetic data preserves statistical properties.
This tool is aimed at teams that need safe, fast access to realistic data. Software developers and QA engineers use it to create test data that catches bugs before production. Data scientists use it to build sandbox environments for model validation without exposing sensitive records. Organizations that need to share data with external partners can generate synthetic copies that preserve analytical value while eliminating privacy risks. If your team regularly hits bottlenecks waiting for data access approvals or spends too much time writing custom data generation scripts, this platform offers a straightforward alternative that hand...
Key features
What makes it stand outWho is Synthetic data software for?
Who benefits most from this toolPricing
Basic
- Syntho Engine self-hosted / on-premise
- 200+ Mockers
- Rule-Based Synthetic Data
- PII Column Scanner
- Consistent Mapping
- Subsetting
- AI-Generated Synthetic Data
- Upsampling
- Deployment
- Support
- Database Connections: 1-5
- Database Connector Types: 1-2
- Implementation Deployment Packages: Small
Standard
- Syntho Engine self-hosted / on-premise
- 200+ Mockers
- Rule-Based Synthetic Data
- PII Column Scanner
- Consistent Mapping
- Subsetting
- AI-Generated Synthetic Data
- Upsampling
- Deployment
- Support
- Includes all current features
- UI Languages
- Time-Series
- QA Report
- Database Connections: 6-10
- Database Connector Types: 1-5
- Implementation Deployment Packages: Medium
Ultimate
- Syntho Engine self-hosted / on-premise
- 200+ Mockers
- Rule-Based Synthetic Data
- PII Column Scanner
- Consistent Mapping
- Subsetting
- AI-Generated Synthetic Data
- Upsampling
- Deployment
- Support
- Includes all current features
- UI Languages
- Time-Series
- QA Report
- Includes all future features
- PII Open Text Scanner
- More Database Connections On Request
- More Database Connector Types
- Database Connections: 11-15
- Database Connector Types: 1-8
- Implementation Deployment Packages: Large
Trust & presence
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