Syntho AI
PaidGenerate synthetic tabular data that preserves privacy and matches real dataset properties
What is Syntho AI?
Syntho is a no-code synthetic data generation platform that creates privacy-safe artificial datasets mirroring the statistical properties and relationships of real data. For enterprises handling sensitive customer information, financial services, healthcare, retail, using real production data for ML training, software testing, or analytics creates regulatory risk under GDPR, HIPAA, and similar laws. Syntho solves this by generating synthetic datasets that are statistically indistinguishable from real data but contain zero actual customer information. Unlike simple anonymization (which can often be reversed), Syntho's generative models create entirely new rows that preserve column correlations, distributions, and referential integrity across tables. This makes the synthetic data safe to share with offshore dev teams, use in staging environments, or train ML models without privacy concerns. Syntho's pricing is feature-based rather than consumption-based, you pay for capabilities, not data volume, which is unusual in the synthetic data space and makes costs predictable. The platform is no-code with a visual workflow builder for non-engineers, and it integrates with major data warehouses and ETL tools. In 2026 Syntho introduced a new platform version with faster generation, smarter quality metrics, and expanded support for unstructured data. On 9 June 2026 Syntho also acquired the MOSTLY AI brand and related trademark assets; MOSTLY AI now runs under the name MOSTLY AI, powered by Syntho. Syntho itself remains live and independent, and now presents itself as a synthetic test data management platform.
Syntho AI demo video
Watch Syntho's official demo to see Syntho AI in action before reading our full review.
Official video by Syntho via YouTube, embedded for reference. ToolChase does not host or claim this video.
⚡ Quick Verdict
Enterprise data teams needing privacy-safe test data for ML training and software development
Small teams without strict data privacy requirements
Custom enterprise pricing, feature-based, no consumption charges
No, demo required
High-fidelity synthetic data generation with no per-row consumption charges
Enterprise pricing: three named tiers, Basic, Standard, and Ultimate, but no published prices
Bottom line: Syntho AI scores 4.3/5, The leading platform for generating privacy-safe synthetic tabular data.
Pricing
Syntho uses feature-based pricing with no consumption or volume-based charges, unusual in the synthetic data space. This means you pay for the capabilities you need rather than per row or per dataset. As of August 2026 the Syntho pricing page lists three named tiers, Basic, Standard, and Ultimate, and each one shows a "Get a quote" button instead of a figure. The tiers separate on capability rather than usage: Basic covers the 200+ mockers, rule-based synthetic data, the PII column scanner, consistent mapping, subsetting, AI-generated synthetic data, and upsampling; Standard adds UI languages, time-series synthetic data, and the quality assurance report; Ultimate adds the PII open text scanner. Database connections scale from 1 to 5 on Basic, 6 to 10 on Standard, and 11 to 15 on Ultimate, with more available on request, and connector types scale alongside them, 1 to 2 on Basic, 1 to 5 on Standard, and 1 to 8 on Ultimate. Ultimate is also the only tier listed as including all future features, where Basic covers current features only. Deployment packages are sized small, medium, or large. Every tier includes deployment support, unlimited workspaces, unlimited deployments across environments, and no extra fee per generation or database. Exact tier pricing isn't published publicly; enterprise plans are custom-priced based on features, deployment model (cloud or self-hosted), and support level, typically starting in the low-to-mid five figures per year. Request a quote directly through the Syntho pricing page for current details.
Key Features
- High-fidelity synthetic tabular data generation
- Preserves statistical properties, distributions, and column correlations
- Maintains referential integrity across multi-table datasets
- No-code visual workflow builder
- Feature-based pricing (no consumption charges)
- Integrations with major data warehouses (Snowflake, BigQuery, Redshift)
- Quality metrics for evaluating synthetic data fidelity
- Cloud and on-premise deployment options
Pros & Cons
Pros
- Feature-based pricing eliminates surprise costs at high data volumes
- No-code workflow makes it accessible to non-engineers
- Strong privacy guarantees make it safe for GDPR/HIPAA compliance
- Preserves multi-table relationships for complex datasets
Cons
- Enterprise pricing excludes smaller teams and individual developers
- Custom quote process; the Basic, Standard, and Ultimate tiers carry no published prices
- Primarily tabular, less mature for unstructured data
FAQ
How much does Syntho cost?
Syntho uses feature-based pricing, you pay for capabilities, not data volume. The pricing page lists three tiers, Basic, Standard, and Ultimate, but every one of them shows a quote request rather than a price. Enterprise plans are custom-priced based on features, deployment, and support, typically starting in the low-to-mid five figures per year. Request a quote from syntho.ai/pricing.
Is synthetic data really indistinguishable from real data?
Syntho's generative models produce data that's statistically indistinguishable, column distributions, correlations, and referential integrity match real data, but contain zero actual customer rows. For ML training and analytics, the models produce similar results to training on real data. Quality metrics in the platform let you quantify how close the synthetic matches reality.
Syntho vs Mostly AI vs Tonic?
This comparison changed in 2026. On 9 June 2026 Syntho announced it had acquired the MOSTLY AI brand and related trademark assets, and MOSTLY AI now continues under the name MOSTLY AI, powered by Syntho, so the two are no longer independent competitors. That leaves Tonic as the main separate alternative; Tonic targets software dev teams needing test data, while Syntho is the no-code option with feature-based pricing and strong support for multi-table relational data. Choice depends on team structure and primary use case.
Can Syntho replace production data in ML training?
Yes, for most use cases. Syntho-generated data preserves the statistical properties needed to train accurate ML models while eliminating privacy risk. Real-world tests show models trained on synthetic data perform within 1-3% of models trained on real production data.
Is Syntho compliant with GDPR and HIPAA?
Yes. Because synthetic data contains no actual customer rows, it's not considered personal data under GDPR or PHI under HIPAA. This lets enterprise teams share synthetic datasets with offshore dev teams, use them in staging environments, and train ML models without the regulatory burden of real production data.
How long does it take to generate synthetic data with Syntho?
Generation time depends on source dataset size and complexity. For typical business datasets (millions of rows, dozens of tables), initial model training takes hours and subsequent generation takes minutes. The no-code workflow handles the complexity behind the scenes.
📋 Good to know
Request a demo at syntho.ai. Work with the Syntho team for initial model training on your source data; typical setup is 1-2 weeks.
SOC 2 Type II compliant. GDPR compliant. On-premise deployment available for air-gapped environments.
Not applicable, Syntho is enterprise-only with annual contracts.
Low for end users (no-code). Moderate for data engineers configuring source data pipelines and quality metrics.
