Syntho AI

Paid

Generate 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.

⚡ Quick Verdict

Best for

Enterprise data teams needing privacy-safe test data for ML training and software development

Not ideal for

Small teams without strict data privacy requirements

Starting price

Custom enterprise pricing — feature-based, no consumption charges

Free plan

No — demo required

Key strength

High-fidelity synthetic data generation with no per-row consumption charges

Limitation

Enterprise pricing with custom quotes and no public tiers

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. 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 with no published public tiers
Primarily tabular — less mature for unstructured data

✅ Pricing verified April 2026 · ✅ Independently reviewed · ✅ Scoring methodology

FAQ

How much does Syntho cost?

Syntho uses feature-based pricing — you pay for capabilities, not data volume. Exact tier pricing isn't public. 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?

All three are enterprise synthetic data platforms. Mostly AI focuses on privacy-safe data generation with strong compliance positioning. Tonic targets software dev teams needing test data. 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.