Synthetic data · Synthesized Ltd.
Synthesized
Synthetic and test-data platform for generating privacy-preserving, statistically representative data to develop and validate ML models.
Synthesized generates synthetic datasets that mirror the statistical properties of production data — distributions, correlations and rare-event patterns — so teams can develop, test and validate machine-learning models and applications without exposing real records. It positions itself around data privacy compliance: the claim is that generated records do not map back to any real individual, letting the resulting data move across teams or outside a compliance boundary that production data cannot cross. Beyond generation it offers data profiling and augmentation tooling for balancing under-represented classes in training data. Synthesized is a much smaller company than platform vendors like Tonic.ai or MOSTLY AI, and pricing is not published; buyers should confirm current company scale and support capacity before a purchase decision.
At a glance
| Vendor | Synthesized Ltd. |
|---|---|
| Pricing model | Quote only |
| Free tier | — |
| Deployment | Cloud, Self-hosted |
| Open source | No |
| Best for | ML teams needing representative training or test data without exposing production records. |
Pricing
No public pricing found; quoted per deployment.
Pricing has not been verified yet — see the vendor's site.
Features
- Synthetic data generation preserving statistical properties of source data
- Data profiling and automated data-quality reporting
- Class-balancing/augmentation for underrepresented training data
- Privacy-preserving generation aimed at removing re-identification risk
- On-prem/self-hosted deployment for regulated data
- SDK and API access for pipeline integration
- Support for tabular and time-series data
Integrations
Profile last reviewed September 21, 2026