Answer in brief · 10 November 2026
Synthetic data platforms, by the data you need
Generating a new dataset and masking an existing database are different jobs. Tonic Fabricate starts with $5 of monthly credits on its free tier; MOSTLY AI offers local and client SDK modes; DataMasque focuses on production-like customer data and masking workflows. Decide whether you need test fixtures, statistical synthesis or protected source data first.
- 3 products and providers compared
- 3 dated sources
- Checked 10 November 2026
The call
Runner-up
MOSTLY AI when a Python workflow and locally operated training/generation are important. DataMasque belongs in a separate evaluation for masked production-like data.
What buyers get wrong
Assuming synthetic means private, representative or useful. Those properties require tests appropriate to the underlying data and intended use.
Products and providers, side by side
| Name and our take | Work it covers | Access / deployment | Published price or buying route | Scope or limit to check |
|---|---|---|---|---|
| TonicSynthetic generation and test-data products Choose the specific Tonic product: generation, structured masking and text de-identification have different pricing. Visit Tonic → | Fabricate generates relational and unstructured data; Structural manages protected test data1 | Fabricate cloud; enterprise/self-hosted options vary by product1 | Fabricate Free: $5 monthly credits; Plus $29/month with $25 credits1 | Extra usage is metered; do not apply Fabricate's tariff to Structural1 |
| MOSTLY AISynthetic-data platform and Python SDK Useful when the evaluation should include local compute and programmatic generation. Visit MOSTLY AI → | Train generators and create tabular or language synthetic datasets2 | SDK LOCAL mode or CLIENT platform mode2 | Open-source SDK; platform and compute costs require separate scope2 | Local generation still needs your own compute and data-quality validation2 |
| DataMasqueData masking and production-like data A different starting point when you already have source data to protect rather than invent from scratch. Visit DataMasque → | Data masking, non-production data and AI/ML data workflows3 | Enterprise product and demo route3 | Consult the vendor's pricing for the required deployment3 | Confirm the method preserves useful relationships and meets your privacy requirements3 |
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How to read the table
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Questions buyers ask
- How were these products selected?
- They cover the buying category or a clearly labelled part of its workflow. The table distinguishes different product types instead of treating every vendor as interchangeable.
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Use this data
Synthetic Data Platforms. “Synthetic data platforms, by the data you need.” Superprompt, 10 November 2026. https://syntheticdataplatforms.com/
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