SDV vs. Gretel vs. MOSTLY AI: Compare Synthetic Data Tools
There is no established universal winner among SDV, Gretel, and MOSTLY AI. The best fit depends on your data structure, where processing can happen, the integrations and workflow you need, and how you will test privacy and usefulness. Compare them on the same representative workload before choosing.
How the three tools differ
The clearest distinctions are their documented scope and operating models—not proof that one produces better synthetic data. SDV emphasizes Python-based work with structured and relational data; Gretel emphasizes configurable workflows and runners that can operate in cloud or customer environments; MOSTLY AI offers an SDK with local and remote-platform modes.
| Decision area | SDV | Gretel | MOSTLY AI |
|---|---|---|---|
| Documented data and workflow scope | Community documentation covers single-table, sequential, and multi-table data. Enterprise highlights large, interconnected datasets. | Vendor materials describe tabular, text, and time-series synthesis, plus configurable workflows. | SDK documentation describes tabular and language data, with generator training, generation, probing, and connectors. |
| Execution options | Community and Enterprise are Python SDKs for on-premises use; Enterprise also describes enterprise deployment and integrations. | Vendor materials describe cloud runners and runners operating in a customer’s environment. Confirm the planned service’s architecture and residency. | Local mode uses local CPU or GPU resources. Client mode connects to a remote platform and uses its compute. |
| Documented evaluation and privacy features | Community describes quality measurement and visualization. Optional Enterprise bundles include differential privacy. | Vendor materials advertise quality and privacy scores and configurable Safe Synthetics workflows. | Project documentation lists automated quality metrics and privacy evaluation. |
| Integration and scaling | Enterprise describes scalable synthesizers and optional direct database connectors. | Workflows describe connectors, scheduling, and composable transformations and models. | SDK documentation describes connectors and local or remote operation. |
| Commercial information established here | Community is documented under the Business Source License. Enterprise is licensed; bundle documentation directs buyers to contact the vendor about pricing and plans. | Comparable current pricing: not stated (Gretel product and developer documentation). | Comparable current pricing: not stated (MOSTLY AI SDK documentation). |
These are vendor-documented capabilities, not independent evidence of comparative quality. In particular, the listed evaluation features do not establish that different vendors’ scores are equivalent or sufficient for your risk model.
Which tool is the strongest starting point for your setup?
SDV: Python workflows for structured and relational data
SDV Community is a Python library and SDK for tabular synthetic data. Its documentation covers single-table, sequential, and multi-table workflows, alongside data-quality measurement and visualization. It also describes customization through constraints and preprocessing. Community is distributed under the Business Source License, so review that license against your intended use.
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SDV Enterprise is a separate licensed offering. Its documented scope includes larger numbers of complex, interconnected tables, scalable synthesizers, advanced preprocessing and customization, and enterprise-wide integrations. Optional bundles cover AI and database connectors, Constraint Augmented Generation, differential privacy, targeted sampling, and enhanced synthesizers. Do not assume those Enterprise features are included in Community.
SDV is a sensible candidate when your main requirement is a Python and on-premises approach to structured data, particularly if a specific Enterprise scale, connector, or privacy capability addresses a concrete need.
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Gretel: scheduled, connected synthesis workflows
Gretel describes a platform for synthetic data, with workflows that can be scheduled, connect to sources and destinations, and chain models with transformations. Its developer materials distinguish Safe Synthetics, which starts with an existing dataset, from Data Designer, which creates data from scratch. Product materials describe synthesis for tabular, text, and time-series data, as well as reports for quality and privacy.
Gretel describes both cloud runners and runners operating in a customer’s environment. That does not by itself settle where data is processed or stored in the service configuration you would use; confirm the exact deployment, residency, and operating requirements with the vendor. Treat advertised quality and privacy scores as features to validate, not as a substitute for your own acceptance criteria.
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NVIDIA’s author biography says Alex Watson joined NVIDIA in 2025 with the acquisition of Gretel. That establishes the acquisition context stated in the biography, but does not establish product-roadmap changes, support continuity, or current contract terms.
Gretel is a candidate when scheduled workflows, source and destination connectors, or a choice between cloud and in-environment runners matters to your pipeline.
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MOSTLY AI: one SDK for local or platform compute
MOSTLY AI’s SDK documentation describes two operating modes through the same API. Local mode runs on a local computer or supported Python environment. Client mode connects to a remote MOSTLY AI Platform and uses that platform’s compute. The documentation says platform deployment uses Kubernetes; Client mode requires a platform endpoint and API key.
The SDK documents generator training on tabular or language data, synthetic-record generation, generator probing, and connectors for organizational data sources. Some database and cloud or data-platform connectors require optional local dependencies. Check compatibility against the exact SDK version and infrastructure you plan to deploy, rather than assuming every connector or runtime is available in every setup.
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MOSTLY AI is worth evaluating when a consistent SDK across local and remote modes, or its documented generator, probe, and connect workflow, fits your operating model.
How to run a fair, useful pilot
A matched pilot is more informative than comparing feature lists or vendor scores. Set the workload and success criteria before asking vendors to demonstrate their tools.
- Choose representative data. Include the structures, relationships, rare categories, and difficult cases that matter in production. Specify whether the test begins with existing records or requires data designed from scratch.
- Define the intended use. Name the downstream task and decide what synthetic data must preserve for it to remain useful. A dataset suitable for one analysis may not suit another.
- Set deployment boundaries. State where data may be processed, who may access it, and the required data-residency and operating model. Ask each vendor to explain how the proposed configuration meets those requirements.
- Agree on acceptance criteria. Assess fidelity and downstream utility alongside privacy risk. Include rare-segment and relational behavior, failure modes, runtime, and operational effort. Use the same workload and criteria across vendors.
- Review the full operating and commercial picture. Compare integration work, monitoring, governance, licensing, and total cost using current written quotes and deployment-specific terms.
For sensitive or regulated uses, have the responsible privacy and legal teams assess the specific generation and release process. Synthetic data is not automatically anonymous or compliant simply because it is synthetic.
What the available comparison does—and does not—establish
No controlled, independent head-to-head benchmark or comparable current price sheet is established for these three options. The documented feature differences can help narrow a shortlist, but they do not support a cross-vendor quality ranking or numerical claim that one tool improves results more than another. Resolve the remaining questions with a representative pilot, current product-version details, and written vendor terms.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
