Synthetic Data Vault alternatives

3 tools to consider instead of Synthetic Data Vault, shown against it.

Synthetic Data Vault MOSTLY AI Tonic.ai Synthesized
Vendor DataCebo, Inc. (open-source project, originated at MIT) MOSTLY AI GmbH Tonic AI, Inc. Synthesized Ltd.
Pricing model Open source + paid options Quote only Free tier + paid plans Quote only
Free tier Yes — Yes —
Deployment Self-hosted Cloud, Self-hosted Cloud, Self-hosted Cloud, Self-hosted
Open source Yes (BSL-1.1) No No No
Best for Data scientists who want a self-hosted, code-first synthetic data library rather than a managed platform. Enterprises needing to share or test with representative data that cannot leave a regulatory or contractual boundary. Engineering teams needing privacy-safe test data or PII-scrubbed text for lower environments and LLM pipelines. ML teams needing representative training or test data without exposing production records.
Pricing

The core library is free and open source under a source-available license; DataCebo sells a separate paid enterprise product.

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

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.

Fabricate has a free tier plus a low-cost Plus plan with monthly credits and pay-as-you-go overage; Structural and Textual are quoted per deployment size (data volume, users).

Fabricate Free $0/month
Fabricate Plus $29/month
Structural Professional custom
Enterprise (all products) custom

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

No public pricing found; quoted per deployment.

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

Features
  • Single-table, multi-table relational and time-series synthesizers
  • Gaussian copula and deep-learning (CTGAN) generation models
  • SDMetrics companion library for fidelity and privacy scoring
  • Constraint definitions to preserve business rules in generated data
  • Runs entirely in the user's own environment
  • Python API for pipeline integration
  • Active open-source community and research lineage from MIT
  • 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
  • Structural: database de-identification and synthesis with referential integrity preserved
  • Textual: NLP-based PII detection and redaction/synthesis in unstructured text
  • Fabricate: fully synthetic mock data generation from a schema
  • Self-hosted/on-prem deployment for regulated environments
  • Data subsetting for provisioning smaller realistic test databases
  • Consistent data masking across linked tables and systems
  • LLM-pipeline PII scrubbing via Textual
  • Synthetic data generation preserving statistical properties of source data
  • Data profiling and automated data-quality reporting
  • Class-balancing/augmentation for underrepresented training data
  • Privacy-preserving generation aimed at removing re-identification risk
  • On-prem/self-hosted deployment for regulated data
  • SDK and API access for pipeline integration
  • Support for tabular and time-series data

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