Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThere is no evidence-based universal winner among SDV, Gretel, and MOSTLY AI. The right fit depends on your data shape, where processing can happen, the workflow and integrations you need, and how you will test utility and privacy. Use the comparison below to shortlist tools, then run each candidate against the same representative workload and acceptance criteria.
At a glance: where each tool may fit
This comparison reflects capabilities described in the vendors’ official product and developer documentation, not an independent head-to-head test. Documented features do not prove that one tool will produce more useful or safer data for your workload.
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| Decision area | SDV | Gretel | MOSTLY AI |
|---|---|---|---|
| Data and workflow scope | SDV Community documents single-table, sequential, and multi-table synthetic-data workflows. Enterprise describes support for large, complex interconnected datasets. (SDV documentation and Enterprise materials) | Gretel describes workflows for tabular, text, and time-series synthesis, including scheduled, configurable pipelines. (Gretel product and developer documentation) | The SDK documents training generators on tabular or language data, generating records, and probing generators. (MOSTLY AI SDK documentation) |
| Where it runs | Community and Enterprise are Python SDKs for on-premises use; Enterprise also promotes enterprise integrations. (SDV documentation and Enterprise materials) | Gretel describes cloud runners and runners operating in a customer’s environment. Confirm the architecture and residency options for the specific service you plan to use. (Gretel product page) | Local mode uses local CPU/GPU resources; Client mode connects to a remote MOSTLY AI Platform and uses its compute. (MOSTLY AI SDK documentation) |
| Quality and privacy evaluation | SDV describes quality measurement and visualization. Optional Enterprise bundles include differential privacy. (SDV documentation and bundles page) | Gretel describes quality and privacy scores and configurable Safe Synthetics workflows. Assess these against your own risk model. (Gretel product and developer documentation) | MOSTLY AI project documentation lists automated quality metrics and privacy evaluation. Verify the methods in the SDK and platform versions you intend to use. (MOSTLY AI SDK documentation) |
| Connectors and customization | Enterprise materials describe integrations, scalable synthesizers, advanced preprocessing, and optional database connectors and other bundles. (SDV Enterprise materials) | Gretel describes source connectors, scheduled workflows, and chains of models and transformations. (Gretel Synthetics page) | The SDK describes connectors for organizational data and local or remote operation; connector dependencies vary by source. (MOSTLY AI SDK documentation) |
| Commercial terms | Community is distributed under the Business Source License; Enterprise is licensed. Enterprise bundle pricing is contact-led. (SDV documentation and bundles page) | Current comparable pricing is not stated in the official materials described here; request a current quote and deployment-specific terms. | Current comparable pricing is not stated in the official materials described here; request a current quote and deployment-specific terms. |
Choose by workload, not feature count
Consider SDV for Python-based tabular and relational work
SDV Community is a Python library and SDK for tabular synthetic data. Its documentation covers single-table, sequential, and multi-table workflows, along with quality measurement, visualization, constraints, and preprocessing. That makes it a natural candidate when your team wants to build and evaluate structured-data workflows in Python and keep execution on premises.
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Do not assume Community includes every Enterprise capability. SDV Enterprise is positioned for larger, more complex interconnected datasets and enterprise integration. Optional bundles include AI and database connectors, Constraint Augmented Generation, differential privacy, targeted sampling, and enhanced synthesizers. Check which specific features are included in the license you are considering; the Enterprise bundles page directs buyers to contact SDV about pricing and plans.
#1 Best Overall
Consider Gretel for scheduled, connected workflows
Gretel describes a platform for synthetic-data generation with configurable workflows, prebuilt source connectors, scheduling, and chained transformations and models. Its materials distinguish Safe Synthetics, which starts from existing data, from Data Designer, which is for creating data from scratch. That distinction matters: a workflow that synthesizes a real dataset has different inputs and validation needs from one that generates designed data without that starting dataset.
Gretel describes both cloud runners and runners operating in a customer’s environment. Treat those as deployment options to verify, not a blanket assurance about where every part of a service runs. Ask for the architecture, data flows, residency controls, and operational responsibilities for your planned configuration. Gretel’s reported quality and privacy scores are vendor-described evaluation features; decide whether their methods address your particular privacy threat model and downstream task.
Rank #2
Consider MOSTLY AI for a consistent local and remote SDK workflow
MOSTLY AI’s SDK documents two modes through the same API. Local mode runs on a local computer or supported Python environment and uses local compute. Client mode connects to a remote MOSTLY AI Platform and uses platform compute; the documentation says platform deployment uses Kubernetes and that Client mode requires a platform endpoint and API key.
The SDK describes training generators for tabular or language data, generating synthetic records, probing generators, and connecting to organizational data sources. Some database and cloud-platform connectors require optional local dependencies. Confirm compatibility with the exact SDK version, Python environment, source systems, and platform deployment you plan to operate.
Rank #3
How to compare candidates in a pilot
A useful evaluation holds the workload constant. Before inviting vendors to demonstrate, write down what the data must support and what risks the release must control. Then use the same inputs, conditions, and acceptance criteria for every candidate.
- Define the intended use. Name the downstream task—such as software testing, analytics, or model development—and specify what a successful synthetic dataset must enable. A quality score alone cannot establish usefulness for every task.
- Map the data structure. Identify tables and relationships, time ordering, text or language fields, important constraints, and rare categories or segments. Include representative edge cases rather than only the most common records.
- Set deployment and access boundaries. State where data may be processed, what must remain in your environment, who needs access, and what connectors or destinations the workflow requires. Ask vendors to show the actual architecture for the proposed configuration.
- Agree on evaluation criteria before the demo. Test fidelity and downstream utility alongside privacy risk. Include checks for relational consistency, rare-segment behavior, and failure modes. Have privacy and legal teams assess the generation and release process for sensitive or regulated use; synthetic data is not automatically anonymous or compliant.
- Measure operational effort. Compare setup, integration work, runtime, monitoring, governance, and ongoing operating cost on the same workload. Record where manual steps, infrastructure, or specialist skills are required.
- Resolve commercial and licensing details in writing. Confirm the applicable license, included features, deployment rights, support arrangements, and current total cost for your intended use. Do not infer price or terms from feature pages.
What is not established by a feature comparison
The documentation described here does not establish a controlled independent benchmark across all three tools, a comparable current price sheet, or a numerical quality winner. The vendors’ documented claims are useful for shortlisting, but they cannot answer how a specific dataset will perform under your requirements. A matched pilot and current written proposals are needed to make that decision.
There is also a corporate-context change relevant to Gretel buyers: NVIDIA’s biography for Alex Watson, a senior director of product at NVIDIA AI, says, “He joined the company in 2025 with the acquisition of Gretel.” That establishes the acquisition context as described by NVIDIA, but does not establish changes to product roadmaps, support continuity, contracts, or specific commercial arrangements. Confirm those points directly for any purchase.
Quick Recap
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