Syntho
Syntho is a self-service platform specializing in the generation of synthetic data to accelerate data-driven solutions. Synthetic data mimics the statistical pa...
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What is Syntho?
Syntho is an Amsterdam-based synthetic data platform that enables organizations to generate privacy-preserving, production-like data at scale. It combines all major synthetic data generation methods—Synthetic Data Masking, Rule-Based Synthetic Data, and AI-Generated Synthetic Data—into a single engine, allowing users to configure exact methods or combinations for any use case. The platform deploys in your own environment, ensuring data never leaves your control, and supports easy connection to source databases via out-of-the-box connectors.
Key features include PII Scanner and consistent mapping for masking sensitive information, formula-based and pattern-based generation for rule-based data, and AI-powered synthesis that mimics statistical patterns, relationships, and time series data. Users benefit from Quality Assurance Reports assessing accuracy, privacy metrics like Identical Match Ratio and Distance to Closest Record, upsampling, subsetting, and automation of recurring workflows through UI or API. It's designed for fast, secure data generation for testing, development, demos, analytics, and sharing.
Syntho targets data teams, developers, analysts, and stakeholders in industries like healthcare, finance, pharma, government, and manufacturing who need realistic data without privacy risks. It accelerates innovation by reducing data access bottlenecks, enabling safe collaboration across silos, and providing tailored data for prototypes, ML models, and product demos. Non-technical users appreciate the intuitive interface, while enterprises value self-hosted deployment and transparent operations.
Syntho pricing
Pricing model: Free
Pricing details not explicitly listed on the website; emphasizes transparent pricing with no usage-based fees or hidden costs. Contact sales for custom enterprise plans based on deployment and needs. Offers product demos and expert consultations for tailored quotes.
Syntho pros
- Combines masking, rule-based, and AI generation in one platform
- Deploys in your own environment for full data control
- PII Scanner automatically detects sensitive information
- Consistent mapping preserves referential integrity
- AI mimics statistical patterns and relationships accurately
- Quality Assurance Reports for every generation
- Supports time series and upsampling for complex data
- Formula-based synthesis for custom scenarios
- Pattern-based data mimicking real-world distributions
- Subsetting for focused datasets
- Out-of-the-box database connectors
- Automates recurring data generation jobs
- User-friendly UI for non-technical teams
- API for programmatic control
- Privacy metrics like IMR, DCR, NNDR
- No usage-based fees or hidden costs
- Faster prototyping and reduced bugs in production
Syntho cons
- Self-deployment requires infrastructure setup
- No cloud-hosted option mentioned
- Limited to supported database connectors
- Quality reports may overwhelm simple users
- AI generation might need tuning for niche data
- Enterprise-focused, less for individuals
- No free tier explicitly advertised
- Demo data preparation still manual in some cases
- Scalability depends on your environment
Frequently asked questions about Syntho
What is Synthetic Data Masking in Syntho?
Synthetic Data Masking protects sensitive information by removing or modifying PII using features like PII Scanner, Synthetic Mock Data, and Consistent Mapping to ensure the same real value always maps to the same synthetic output across datasets.
How does Rule-Based Synthetic Data work?
Rule-Based Synthetic Data generates new data mimicking real-world or targeted scenarios with predefined rules, including Formula-Based Synthetic Data, Pattern-based synthetic data, and Subsetting for creating focused subsets.
What makes AI-Generated Synthetic Data special?
AI-Generated Synthetic Data uses artificial intelligence to mimic statistical patterns, relationships, and characteristics of original data, supporting Time Series Synthetic Data, Upsampling, and producing production-like outputs for advanced analytics.
What is included in the Quality Assurance Report?
The Quality Assurance Report assesses synthetic data on accuracy via statistical comparisons like distributions and correlations, privacy with metrics such as Identical Match Ratio (IMR), Distance to Closest Record (DCR), and Nearest Neighbour Distance Ratio (NNDR), and generation speed.
Can Syntho be deployed on-premises?
Yes, Syntho deploys securely in your own environment to ensure source data never leaves your trusted perimeter, with easy connections to source and target databases via out-of-the-box connectors.
Who is Syntho designed for?
Syntho serves teams in healthcare, pharma, finance, government, and manufacturing needing production-like test data, feature development, product demos, analytics, AI modeling, and safe data sharing across internal and external stakeholders.
How does Syntho ensure privacy?
Syntho ensures privacy through on-premises deployment, synthetic masking of PII, rule-based anonymization, AI generation that avoids real data leakage, and validated privacy metrics in QA reports confirmed by external experts like SAS.
What are the steps to generate synthetic data?
Deploy in your environment, connect to source database, select synthesis methods via UI or API, generate data (combine methods if needed), review job summary and QA report, then automate for recurring use and utilize the output.
Does Syntho support automation?
Yes, automate synthetic data generation jobs via User Interface or API, ideal for recurring needs like CI/CD pipelines, regular test data refreshes, or scheduled analytics sandboxes.
Why combine synthetic data methods in Syntho?
Combining methods like masking for PII, rules for scenarios, and AI for patterns delivers the most accurate, privacy-preserving data tailored to use cases, outperforming single-method approaches.