How to choose a synthetic data tool

A synthetic data tool generates new records that preserve the statistical shape of a real dataset — distributions, correlations, relationships between tables — without any record mapping back to a real person or transaction. Teams buy one to unblock work that production data cannot legally or contractually cross: giving a lower environment realistic test data, letting an offshore or third-party team develop against something real-shaped, or training a model on rare cases that are underrepresented in production.

It is not a substitute for data masking, which alters real records in place and is often faster to set up for simple cases. Synthetic data generation is worth the extra step when masking alone still leaves records too close to identifiable, or when you need more volume or more edge cases than the real dataset contains.

What kind of data do you actually need to synthesize

This splits the category more than any vendor's marketing does.

• Relational or tabular data — rows, columns, foreign keys between tables. MOSTLY AI, Synthetic Data Vault and Synthesized are all built around this case: learning column distributions and cross-table relationships, then sampling new rows that preserve them.
• Data that must be generated fully inside your own infrastructure, with the vendor never touching source records. Syntho is built specifically around this deployment model, running on-premises with a built-in sensitivity scanner to locate PII before generation even starts.
• Unstructured text and documents — not database rows — that need PII detected and redacted or synthesized before they reach an LLM pipeline or a data lake. Tonic.ai's Textual product is the only one of these five built for that; the others assume structured, tabular input.
• Mock data for a schema that does not have real data yet, such as a new feature in early development. Tonic.ai's Fabricate product and MOSTLY AI's mock-data mode both cover this, generating plausible data from a schema alone.

Generative model vs rule-based vs masking, or a blend

MOSTLY AI, SDV and Synthesized lean on generative machine-learning models — Gaussian copulas, CTGAN and similar architectures that learn a dataset's structure and sample new rows from it. Syntho and Tonic.ai combine techniques: AI-based synthesis alongside rule-based generation and traditional masking, chosen per field depending on what best preserves usability while removing identifiability. Neither approach is universally better — a purely generative model can better preserve subtle correlations across a wide table; a rule-based or masking approach can be easier to validate for a specific compliance requirement, because the transformation is explainable rather than learned.

Where generation is allowed to happen

This is often the deciding constraint before any feature comparison. SDV is a Python library, not a hosted service — it runs entirely inside your own environment by definition, since there is no vendor infrastructure involved at all. Syntho is built around on-premises deployment for the same reason many regulated buyers choose it: the vendor states it never has access to the sensitive source data. MOSTLY AI and Tonic.ai both offer self-hosted or on-prem options for their database-facing products alongside a managed cloud service, so the choice of deployment is yours rather than fixed by the vendor. Synthesized also supports both, though it is a considerably smaller company than Tonic.ai or MOSTLY AI — worth confirming current scale and support capacity directly before a purchase decision, since that is not something a comparison page can verify for you.

Privacy validation is not automatic

"Statistically representative" and "private" are two different claims, and not every product proves both the same way. MOSTLY AI states built-in differential privacy controls, a formal mathematical guarantee rather than just a resemblance test. SDV pairs its generation library with a companion library, SDMetrics, that scores how well synthetic output preserves statistical properties without directly matching real records — useful, but it is a fidelity/leakage score, not a formal privacy guarantee by itself. Syntho's sensitivity scanner finds PII before generation rather than proving privacy after. Ask any vendor exactly what is measured, on what metric, and whether an independent audit exists — do not accept "synthetic" alone as a privacy claim.

Open source and buy-vs-build

SDV is the one genuinely open-source option here, maintained by DataCebo under a source-available license (free for the core library; DataCebo separately sells a paid enterprise product built on top). Choosing it means your team owns integration, scaling and validation — a real cost, even with no license fee. The other four are commercial platforms with UI, automation, scanning and support built in, priced by quote rather than published tiers, except Tonic.ai's Fabricate product, which has a published free tier and a low-cost paid tier alongside its quoted enterprise products.

A shortlist by situation

• You want a free, code-first library you run yourself, and your team can own integration and validation: Synthetic Data Vault.
• Enterprise relational data with a formal differential-privacy guarantee and flexible deployment: MOSTLY AI.
• Synthetic data specifically to balance underrepresented classes for ML training, alongside profiling tools: Synthesized.
• A fully on-prem requirement in a regulated sector, with PII scanning built into the workflow: Syntho.
• You need both database synthesis and PII scrubbing from unstructured text feeding an LLM pipeline, from one vendor: Tonic.ai.

Questions to ask before you commit

1. Does the tool generate structured/tabular data, unstructured text, or both — and does that match what you actually need to protect?
2. What privacy claim is being made, and what metric or audit backs it up?
3. Can generation run entirely inside your own environment if that is a hard requirement, or only in the vendor's cloud?
4. Does it preserve referential integrity across related tables, or only single-table statistics?
5. How is pricing structured — per deployment, per data volume, per seat — and what does that look like at your actual scale, not a pilot?

Common mistakes

• Treating "synthetic" as automatically compliant without checking what privacy metric, if any, the vendor actually measures.
• Choosing a tabular-data tool for a problem that is really about unstructured text or documents.
• Underestimating the engineering time to integrate and validate an open-source library, and comparing its "free" cost directly against a commercial platform's price without counting that time.
• Skipping a proof of concept on your own schema — generative models that look great on a vendor's demo dataset can behave differently on a wide table with many rare categories.

See MOSTLY AI vs Tonic.ai and MOSTLY AI vs Synthetic Data Vault, or browse every tool in this category.