Syntho alternatives

3 tools to consider instead of Syntho, shown against it.

Syntho Tonic.ai MOSTLY AI Synthesized
Vendor Syntho B.V. Tonic AI, Inc. MOSTLY AI GmbH Synthesized Ltd.
Pricing model Quote only Free tier + paid plans Quote only Quote only
Free tier — Yes — —
Deployment Self-hosted Cloud, Self-hosted Cloud, Self-hosted Cloud, Self-hosted
Open source No No No No
Best for Regulated organizations that need synthetic test data generated fully inside their own infrastructure. Engineering teams needing privacy-safe test data or PII-scrubbed text for lower environments and LLM pipelines. Enterprises needing to share or test with representative data that cannot leave a regulatory or contractual boundary. ML teams needing representative training or test data without exposing production records.
Pricing

No public pricing found; deployed and quoted per organization.

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 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.

No public pricing found; quoted per deployment.

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

Features
  • Sensitivity scanner to locate PII before generation
  • AI-based, rule-based and masking generation methods combined
  • On-premises deployment so the vendor never accesses source data
  • Quality-assurance reporting and data upsampling
  • UI for non-technical users and REST API for automation
  • Test-data management for QA and demo environments
  • Used across healthcare, financial services and other regulated sectors
  • 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
  • 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
  • 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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