Synthetic data · Tonic AI, Inc.

Tonic.ai

Suite of synthetic/de-identified test-data tools (Structural, Textual, Fabricate) for databases, unstructured text and mock data.

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Tonic.ai sells a family of products rather than one engine: Tonic Structural de-identifies and synthesizes relational databases so realistic, referentially-intact test data can be provisioned to lower environments; Tonic Textual detects and redacts or synthesizes sensitive entities (names, IDs, PII) inside unstructured text and documents before it reaches an LLM or data pipeline; Tonic Fabricate generates fully synthetic mock data from scratch, including for schemas that do not exist yet. The common thread is letting engineering and data-science teams work with realistic data without exposing production PII, which is bought mostly to satisfy privacy and compliance requirements around lower environments and LLM pipelines. Structural and Textual support both cloud and self-hosted/on-prem deployment for regulated customers; Fabricate is cloud-only with a pay-as-you-go credit model.

At a glance

Vendor Tonic AI, Inc.
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source No
Best for Engineering teams needing privacy-safe test data or PII-scrubbed text for lower environments and LLM pipelines.

Pricing

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).

Plan Price Notes
Fabricate Free $0/month $5 in monthly credits
Fabricate Plus $29/month $25 in monthly credits plus pay-as-you-go overage
Structural Professional custom up to 10 TB source data, up to 10 users
Enterprise (all products) custom unlimited data/users, self-hosted option, contact sales

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

Features

  • 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

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

MOSTLY AI Synthetic data platform that generates statistically representative tabular/relational data with built-in differential privacy. Synthesized Synthetic and test-data platform for generating privacy-preserving, statistically representative data to develop and validate ML models. 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. Data masking Replacing sensitive values with realistic but fake substitutes so data can be used for testing or analysis without exposure. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way.

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.