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MOSTLY AI vs Synthetic Data Vault

A buy-vs-build choice — MOSTLY AI is a managed platform with a formal privacy guarantee and support; SDV is a free library your team runs and validates itself.

Side by side

	MOSTLY AI	Synthetic Data Vault
Vendor	MOSTLY AI GmbH	DataCebo, Inc. (open-source project, originated at MIT)
Pricing model	Quote only	Open source + paid options
Free tier	—	Yes
Deployment	Cloud, Self-hosted	Self-hosted
Open source	No	Yes (BSL-1.1)
Best for	Enterprises needing to share or test with representative data that cannot leave a regulatory or contractual boundary.	Data scientists who want a self-hosted, code-first synthetic data library rather than a managed platform.
Pricing	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.	The core library is free and open source under a source-available license; DataCebo sells a separate paid enterprise product. Pricing has not been verified yet — see the vendor's site.
Features	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	Single-table, multi-table relational and time-series synthesizers Gaussian copula and deep-learning (CTGAN) generation models SDMetrics companion library for fidelity and privacy scoring Constraint definitions to preserve business rules in generated data Runs entirely in the user's own environment Python API for pipeline integration Active open-source community and research lineage from MIT

Verdict

This comparison is really buy versus build. MOSTLY AI is a commercial platform: a managed or self-hosted product with built-in differential privacy controls, a UI, and vendor support, priced by quote. Synthetic Data Vault (SDV) is a free, open-source Python library that provides the generative models — Gaussian copulas, CTGAN and others — but nothing around them; your team writes the integration, decides how to validate output, and owns it going forward.

Both cover the same core case: single-table, multi-table relational and time-series synthetic data generation that preserves a real dataset's statistical structure. The difference is what you get without engineering effort. MOSTLY AI's privacy claim is backed by built-in differential privacy; SDV's privacy story rests on the fact that generation runs entirely inside your own environment plus its companion library, SDMetrics, which scores fidelity and privacy risk after the fact rather than guaranteeing it in advance.

Choose MOSTLY AI if

You need a formal, built-in differential-privacy guarantee without building the validation yourself.
You want vendor support and a managed deployment option rather than an internal engineering commitment.
You also need mock or simulated data modes beyond core synthetic generation.

Choose Synthetic Data Vault if

You want to run generation entirely inside your own environment with zero license cost, and have the engineering capacity to integrate and validate it.
Your use case fits SDV's supported patterns — single-table, multi-table relational, or time-series data.
You are comfortable using SDMetrics to score fidelity and privacy risk yourselves rather than relying on a vendor's built-in guarantee.

The honest caveat

"Free" undercounts SDV's real cost — someone still has to integrate it, decide what to generate, and validate that the output is actually safe to share, which is exactly the work MOSTLY AI's platform is built to shorten. If your team has that capacity and your case fits SDV's supported data shapes, the savings are real. If it does not, budget the engineering time honestly before treating SDV as the cheaper option.

Last reviewed September 22, 2026

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