AI TOOL PROFILE
Syntho | Synthetic Test Data Management Platform
- Software Development
- Test Data Generation
- 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.
At a glance
- Best for
- Software vendors, HealthTech companies, Financial institutions, Public organizations, Technical teams managing large tabular databases
- Key use cases
- Test Data Management, Privacy-Preserving Analytics, Safe Data Sharing, Tailored Product Demos
- Integrations
- PostgreSQL, SQL Server, Oracle, MySQL, Databricks
- Official website
- Visit Syntho official website
How AI is used
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
PII Scanner
Identifies personally identifiable information within datasets.
AI-Generated Synthetic Data
Uses artificial intelligence to mimic the statistical patterns of original data.
Consistent Mapping
Supports the preservation of referential integrity across relational data ecosystems.
Rule-Based and Formula-Based Synthesis
Generates data based on predefined rules, constraints, or specific formulas.
Subsetting
Supports the creation of smaller, manageable subsets of data for specific testing needs.
Quality Assurance Report
Provides a way to assess generated synthetic data for accuracy and privacy.
Use Cases
Test Data Management
Creating realistic, non-production datasets to support software testing and development.
Privacy-Preserving Analytics
Generating synthetic twins of sensitive data for use in advanced analytics and AI modeling.
Safe Data Sharing
Sharing representative data with external stakeholders or partners without exposing PII.
Tailored Product Demos
Producing synthetic datasets to populate product demonstrations for potential clients.
Integrations
- PostgreSQL
- SQL Server
- Oracle
- MySQL
- Databricks
- IBM DB2
- MariaDB
- Azure Data Lake
- Amazon S3
FAQ
How is Syntho priced?
- Syntho uses feature-based pricing with Basic, Standard, and Ultimate plans. There are no consumption-based charges for data generation or database usage.
Where is the software deployed?
- 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.
What types of data does Syntho support?
- 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
More tools in Software Development
Other published listings in the Software Development category.
More tools in the Test Data Generation software type
Related listings that share the same software type for comparison and shortlisting.
