Synthetic data · Syntho B.V.

Syntho

Synthetic test-data platform combining AI-generated, rule-based and masked data, deployed inside a customer's own environment.

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Syntho generates synthetic test and demo data using a mix of AI-based synthesis, rule/pattern-based generation and traditional data masking, aimed at teams that need realistic but non-sensitive data for testing, demos and AI/ML development. Its privacy claim centers on on-premises deployment: the platform runs inside the customer's own environment so Syntho itself has no access to the underlying sensitive data being processed. It includes a sensitivity scanner to locate PII before generation and a quality-assurance/upsampling step to check that synthetic output remains statistically usable. It is aimed at both technical users (via API/automation) and non-technical teams (via a UI), and is used mainly in regulated sectors like healthcare and financial services where data cannot leave internal infrastructure.

At a glance

Vendor Syntho B.V.
Pricing model Quote only
Free tier —
Deployment Self-hosted
Open source No
Best for Regulated organizations that need synthetic test data generated fully inside their own infrastructure.

Pricing

No public pricing found; deployed and quoted per organization.

Pricing has not been verified yet — see the vendor's site.

Features

  • Sensitivity scanner to locate PII before generation
  • AI-based, rule-based and masking generation methods combined
  • On-premises deployment so the vendor never accesses source data
  • Quality-assurance reporting and data upsampling
  • UI for non-technical users and REST API for automation
  • Test-data management for QA and demo environments
  • Used across healthcare, financial services and other regulated sectors

Integrations

Profile last reviewed September 21, 2026

Alternatives

Tonic.ai Suite of synthetic/de-identified test-data tools (Structural, Textual, Fabricate) for databases, unstructured text and mock data. MOSTLY AI Synthetic data platform that generates statistically representative tabular/relational data with built-in differential privacy. Synthesized Synthetic and test-data platform for generating privacy-preserving, statistically representative data to develop and validate ML models.

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Terms to know

Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. Data masking Replacing sensitive values with realistic but fake substitutes so data can be used for testing or analysis without exposure.

Related guides

How to choose a synthetic data tool Decide what kind of data you need synthesized, where generation must happen, and whether an open-source library or a vendor platform fits your compliance bar.