Local learned datasets: model fit, utility and license limits. Choose around your required workflow, not a universal winner.
SDV learns patterns from existing tables and samples synthetic data through Python. Community includes single-table, multi-table and sequential workflows. Enterprise capabilities and license terms are separate.
Consider SDV when you can train on representative tables and measure the patterns your tests need. It offers a local workflow for statistical data generation and evaluation.
MOSTLY AI’s Python SDK trains on tabular or language data and generates samples locally or through a remote SDK endpoint. Syntho acquired the brand assets on 9 June 2026. Confirm former commercial-platform availability separately.
Consider the SDK when representative training data is available and you can evaluate distributions, relationships and downstream utility. Local experimentation is documented.
Train locally on the same synthetic-only input and hold back evaluation rows. Measure rare cases and source overlap separately from runtime. Review SDV’s BSL terms and MOSTLY SDK’s Apache license. Saved models and generator exports do not establish cross-engine portability. The run record separates successful execution from blocked attempts.
Use the same target schema and business acceptance criteria for both products. Check source-data dependencies, operating model, exact edition and current product status. Document setup effort and failure recovery separately from row-generation speed.
| Decision criteria | SDV Community / Enterprise | MOSTLY AI Synthetic Data SDK |
|---|---|---|
| Required inputs | Representative source tables plus metadata and verified constraints. | Representative tabular/language training data; optional conditioning rows. |
| What you get | Sampled dataframes/datasets, not prescribed transaction cases by default. | Learned synthetic datasets; evaluate statistical utility and rare cases. |
| Who operates it? | Community runs locally in Python on CPU. | Python LOCAL mode on your infrastructure, or CLIENT mode using a remote SDK endpoint. |
| Repeatability to verify | reset_sampling on fitted model | random_state; not cross-runtime byte proof |
| Export and reuse | Saved Python model reusable in a compatible SDV stack; not a vendor-neutral executable model. | SDK generator export/import documented; cross-engine reuse and former-platform continuity unverified. |
| License / edition | Community is source-available under BSL 1.1, not blanket open source. Check the additional-use restriction and terms of the installed version. Enterprise requires a separate license. | The public SDK is Apache-2.0. Commercial platform services and support terms are separate; confirm the current supplier and entitlement. |
| Synthetic generation | Learn from one or more related tables | Locally trained tabular/language models |
| De-identification / masking | UnclearThe cited sources do not establish this capability for the assessed product and edition. | UnclearThe cited sources do not establish this capability for the assessed product and edition. |
| Subsetting / subset planning | UnclearThe cited sources do not establish this capability for the assessed product and edition. | UnclearThe cited sources do not establish this capability for the assessed product and edition. |
| Data virtualization | UnclearThe cited sources do not establish this capability for the assessed product and edition. | UnclearThe cited sources do not establish this capability for the assessed product and edition. |
| Seeded replay, scoped | Repeat sampling from the same trained model | Seed setting for sampling; exact file replay unverified |
Vendor documentation is not a measured head-to-head benchmark. Unresolved requirements need evidence; documented capabilities link to their sources.
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