Syntho | Synthetic Test Data Management Platform
Syntho helps software vendors and regulated organizations create realistic test data without using sensitive PII. It is designed for teams that need to maintain referential integrity across complex databases while meeting privacy requirements.
At a glance
- Category
- Software Development
- Best for
- Software vendors, HealthTech companies, Financial institutions, Public organizations, Technical teams managing large tabular databases
- Pricing
- Syntho uses a feature-based licensing model with Basic, Standard, and Ultimate plans. Pricing is not consumption-based, but specific costs depend on the chosen tier and number of database connectors. Buyers should confirm current pricing on the vendor website.
- Key use cases
- Test Data Management, Privacy-Preserving Analytics, Safe Data Sharing, Tailored Product Demos
- Integrations
- PostgreSQL, SQL Server, Oracle, MySQL, Databricks
- Official website
- www.syntho.ai/
Syntho is a data management platform designed to generate synthetic versions of real-world data for use in testing, development, and analytics. It allows users to combine different generation methods—such as AI-driven synthesis, rule-based logic, and data masking—within a single engine to match specific project needs.
The software is intended for technical teams in industries with strict data privacy requirements, such as Finance, HealthTech, and public organizations. It helps these teams provide developers and analysts with production-like data without exposing actual personally identifiable information (PII).
Buyers should note that the tool is designed for structured, tabular data. It supports deployment via Docker-Compose or Kubernetes, which helps keep the data within the customer's own secure environment.
Before choosing this tool, buyers should confirm their specific database types and the volume of connectors required, as feature access and connector limits vary by pricing plan.
Key Features
Identifies personally identifiable information within datasets.
Uses artificial intelligence to mimic the statistical patterns of original data.
Supports the preservation of referential integrity across relational data ecosystems.
Generates data based on predefined rules, constraints, or specific formulas.
Supports the creation of smaller, manageable subsets of data for specific testing needs.
Provides a way to assess generated synthetic data for accuracy and privacy.
Use Cases
Creating realistic, non-production datasets to support software testing and development.
Generating synthetic twins of sensitive data for use in advanced analytics and AI modeling.
Sharing representative data with external stakeholders or partners without exposing PII.
Producing synthetic datasets to populate product demonstrations for potential clients.
Best For
Integrations
Pricing
Syntho uses a feature-based licensing model with Basic, Standard, and Ultimate plans. Pricing is not consumption-based, but specific costs depend on the chosen tier and number of database connectors. Buyers should confirm current pricing on the vendor website.
FAQ
Syntho uses feature-based pricing with Basic, Standard, and Ultimate plans. There are no consumption-based charges for data generation or database usage.
Syntho is designed to be deployed in the customer's own environment via Docker-Compose or Kubernetes, which helps ensure that sensitive data remains within the customer's environment.
The platform is designed for structured, tabular data, including categorical and numerical data, time series, and multi-table databases with referential integrity.
Source category: Software Development
Source subcategory: Test Data Generation
