Generate realistic, private test data for faster development and AI
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16 reviews trackedThe Bottom Line
Entry price
Paid plans only
Biggest pro
Combines all major synthetic data generation methods in one platform.
Biggest con
Requires a quote for all pricing plans, lacking immediate public pricing details.
TL;DR - Syntho
- Generates realistic, privacy-preserving synthetic data using multiple methods.
- Accelerates software development, testing, and AI/analytics with safe data.
- Deploys securely within your environment with transparent, feature-based pricing.
Pricing: Paid only
Best for: Enterprises & pros
4.6/5 across review platforms
What is Syntho?
Syntho is a comprehensive Synthetic Test Data Management Platform designed to address the challenges of using real data for testing, development, and analytics. It allows organizations to generate data that accurately mimics the statistical patterns and characteristics of original data while ensuring privacy and security. The platform integrates multiple synthetic data generation methods, including Synthetic Data Masking, Rule-Based Synthetic Data, and AI-Generated Synthetic Data, enabling users to choose or combine approaches optimized for various use cases.
Syntho is ideal for teams requiring fast, safe, and realistic data access. It helps create production-like test data to reduce bugs and accelerate time to market, facilitates faster product and feature development by generating data from scratch or edge cases, and enables tailored product demos for higher conversion rates. The platform also accelerates data access for stakeholders, simplifies secure data sharing, and provides realistic, representative data for AI modeling and analytics, all while allowing deployment within a user's own trusted environment for full data control.
Pros & Cons
Pros
- Combines all major synthetic data generation methods in one platform.
- Ensures data privacy by allowing deployment in the user's own environment.
- Offers transparent, feature-based pricing without consumption limits or hidden fees.
- Accelerates development cycles and improves data access for various teams.
- Preserves referential integrity across complex relational data ecosystems.
Cons
- Requires a quote for all pricing plans, lacking immediate public pricing details.
- The platform's full capabilities might require technical expertise for optimal setup and configuration.
- Specific integration details for all possible database types are not explicitly listed.
Ratings Across the Web
4.6(16 reviews)
Syntho holds an aggregate rating of 4.6 out of 5 from 16 reviews across G2, last checked June 5, 2026.
Ratings aggregated from independent review platforms. Learn more
Preview
Key Features
Synthetic Data Masking (PII Scanner, Synthetic Mock Data, Consistent Mapping)Rule-Based Synthetic Data (Formula-Based, Pattern-Based, Subsetting)AI-Generated Synthetic Data (Quality Assurance Report, Time Series, Upsampling)Self-hosted / On-premise deploymentAutomated synthetic data generation workflowsOut-of-the-box database connectorsUser-friendly UI and API for data generationUnlimited database usage and synthetic data generation
Pricing Plans
Pricing checked Sep 3, 2026
Basic
Contact us
- 200+ Mockers
- Rule-Based Synthetic Data
- PII Column Scanner
- Consistent Mapping
- Subsetting
- AI-Generated Synthetic Data
- Upsampling
- UI Languages
Standard
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- 200+ Mockers
- Rule-Based Synthetic Data
- PII Column Scanner
- Consistent Mapping
- Subsetting
- AI-Generated Synthetic Data
- Upsampling
- UI Languages
Ultimate
Contact us
- 200+ Mockers
- Rule-Based Synthetic Data
- PII Column Scanner
- Consistent Mapping
- Subsetting
- AI-Generated Synthetic Data
- Upsampling
- UI Languages
Syntho Engine
Contact us
- Self-hosted / on-premise
- Includes all current features
- Includes all future features
- More Database Connections On Request
- More Database Connector Types
- Deployment Support
- Implementation
Hidden Costs & Gotchas
Minimum database connections/types
Reviews
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4.6/5
Across 16 verified user reviews on G2
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Syntho FAQ
How does Syntho ensure data privacy when generating synthetic data?
Syntho ensures data privacy by allowing deployment directly within your own trusted environment, meaning your original data never leaves your infrastructure. Additionally, it uses Synthetic Data Masking to remove or modify Personally Identifiable Information (PII) and generates new data that mimics statistical patterns without containing actual sensitive information.
Can Syntho generate synthetic data for complex relational databases while maintaining data consistency?
Yes, Syntho can generate synthetic data for complex relational databases. Its Consistent Mapping feature is specifically designed to preserve referential integrity across an entire relational data ecosystem, ensuring that relationships between tables are maintained in the synthetic dataset.
What types of synthetic data generation methods does Syntho offer, and how can they be combined?
Syntho offers Synthetic Data Masking, Rule-Based Synthetic Data (including formula-based and pattern-based), and AI-Generated Synthetic Data. These methods can be easily switched or combined within a single run via the user interface or API to match specific use cases and achieve optimal accuracy and privacy.
How does Syntho support the generation of synthetic data for time-series datasets?
Syntho includes a dedicated Time Series Synthetic Data feature that accurately synthesizes time-series data. This allows users to generate realistic time-dependent datasets while preserving their statistical properties and patterns.
What is the deployment model for Syntho, and what are the implications for data control?
Syntho is designed for self-hosted or on-premise deployment. This model ensures that you retain full control over your data, as the Syntho Engine operates within your own environment, and Syntho itself does not have access to your data.
Beyond testing, what are the key use cases where Syntho's synthetic data provides significant value?
Beyond testing, Syntho's synthetic data provides significant value in product and feature development (building and validating new features faster), tailored product demos (higher conversion rates), accelerating data access for stakeholders, secure data sharing and protection, and creating realistic, representative data for Analytics & AI modeling.
Does Syntho offer capabilities to generate specific edge cases or hypothetical scenarios for testing?
Yes, Syntho can generate edge cases and hypothetical future scenarios. This capability is particularly useful for product and feature development, allowing teams to test new functionalities against a wider range of data possibilities that might not exist in real production data.
How does Syntho's pricing model differ from consumption-based alternatives?
Syntho operates on a transparent, feature-based pricing model with no consumption charges. This means there are no extra fees per generation or database usage, no hidden costs, and no unpredictable usage costs, providing full control and transparency over expenses.
Source: syntho.ai