Synthetic data · MOSTLY AI GmbH

MOSTLY AI

Synthetic data platform that generates statistically representative tabular/relational data with built-in differential privacy.

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MOSTLY AI generates synthetic tabular and relational data by training generative models on production data and sampling new records that preserve the original's statistical structure — column distributions, correlations and cross-table relationships — without containing any real individual's record. It is bought mainly to let teams share or test with data that cannot leave a compliance boundary: the platform claims built-in differential privacy and states production data never has to leave the customer's own environment when deployed on-prem or in their cloud. Beyond generation it also offers "mock" data for staging and "simulated" data for modeling edge cases. It is available as a managed cloud service, self-hosted deployment on Kubernetes/OpenShift, or through an open-source Python SDK for the core synthetic-data engine.

At a glance

Vendor MOSTLY AI GmbH
Pricing model Quote only
Free tier —
Deployment Cloud, Self-hosted
Open source No
Best for Enterprises needing to share or test with representative data that cannot leave a regulatory or contractual boundary.

Pricing

No public pricing page found; the open-source SDK is free, the managed platform is quoted per deployment.

Pricing has not been verified yet — see the vendor's site.

Features

  • Generative models for tabular and multi-table relational data
  • Built-in differential privacy controls
  • Mock data generation for staging and demo environments
  • Simulated/edge-case data for stress-testing models
  • Self-hosted deployment on Kubernetes or OpenShift
  • Open-source synthetic-data SDK (Apache-2.0) for the core engine
  • Fidelity and privacy metrics reporting per generation job

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Tonic.ai Suite of synthetic/de-identified test-data tools (Structural, Textual, Fabricate) for databases, unstructured text and mock data. Synthesized Synthetic and test-data platform for generating privacy-preserving, statistically representative data to develop and validate ML models. Synthetic Data Vault Open-source Python library for generating synthetic tabular, relational and time-series data from real datasets you run yourself. Syntho Synthetic test-data platform combining AI-generated, rule-based and masked data, deployed inside a customer's own environment.

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Terms to know

Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. Differential privacy A mathematical technique for publishing aggregate statistics while guaranteeing no individual record can be inferred from the results.

Related guides

How to choose a synthetic data tool Decide what kind of data you need synthesized, where generation must happen, and whether an open-source library or a vendor platform fits your compliance bar.