Syntho
Synthetic data software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is Syntho
Syntho is a synthetic data software platform used to generate privacy-preserving synthetic versions of sensitive datasets for analytics, testing, and data sharing. It targets data teams and privacy, risk, and compliance stakeholders that need to use production-like data without exposing personal or confidential information. The product focuses on synthesizing tabular data and providing controls to assess privacy risk and data utility, with deployment options that support regulated environments.
Privacy-focused data generation
Syntho is designed to create synthetic datasets that reduce exposure of personal and confidential information compared with using raw production data. It supports workflows aimed at enabling data use while addressing privacy and compliance requirements. This positioning aligns with common synthetic data adoption drivers such as safer analytics and controlled data sharing.
Utility and risk evaluation
The platform includes mechanisms to evaluate how well synthetic data matches statistical properties of the source data and to assess privacy-related risks. These checks help teams decide whether a dataset is suitable for specific downstream uses such as model development or BI. Having evaluation in the same tool reduces reliance on ad hoc scripts for basic validation.
Enterprise deployment options
Syntho supports deployment patterns that can fit enterprise and regulated settings, where data cannot leave controlled environments. This is relevant for organizations that require on-premises or private-cloud operation and tighter governance. It can simplify adoption compared with approaches that assume fully managed, external processing.
Best fit for tabular data
Syntho is primarily oriented toward structured/tabular datasets and common enterprise data sources. Organizations needing high-fidelity synthetic image, video, audio, or complex unstructured text generation may require additional tools. This can limit standardization on a single platform for all synthetic data needs.
Validation still requires expertise
Synthetic data quality and privacy risk depend on configuration choices, source data characteristics, and intended use. Teams often still need statistical and domain expertise to interpret utility metrics, select appropriate generation methods, and set acceptable risk thresholds. Misconfiguration can lead to synthetic data that is either too dissimilar to be useful or insufficiently privacy-protective.
Integration and governance effort
Operationalizing synthetic data typically requires integration with data catalogs, access controls, CI/CD, and downstream analytics or ML pipelines. The amount of built-in connectors and governance automation varies by environment and may require custom implementation. This can extend time-to-value for organizations with complex data estates.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Basic | Get a quote (contact sales) | Feature-based license; Syntho Engine; 1 database connector type; Deployment package: Small; Support included; No consumption-based fees; Unlimited workspaces; Typically starts with a 1-year license agreement (evaluation timeline may be included). |
| Standard | Get a quote (contact sales) | All Basic features plus 1–5 database connector types; Adds more feature-set (consistent mapping shown for Standard); Deployment package: Medium; Support included; No consumption-based fees. |
| Ultimate | Get a quote (contact sales) | Full feature set: Syntho Engine (self-hosted/on‑premise option noted); 1–8 database connector types; Advanced features shown (time series, upsampling, consistent mapping, PII scanner & open text support, 200+ mockers); Deployment package: Large; Support included; No consumption-based fees. |
Seller details
Syntho B.V.
Amsterdam, Netherlands
2020
Private
https://www.syntho.ai/
https://x.com/synthoai
https://www.linkedin.com/company/syntho/
