SDV Enterprise

Overview

SDV Enterprise is a self-hosted Python SDK for generating synthetic structured data, with tools for modeling relationships across database tables. It suits teams that need realistic test or machine-learning data while keeping customer data and generated models in their own environment. Its strength is the combination of configurable processing and database workflows; the trade-off is a paid, usage-billed route to the broader capabilities.

Key features

The SDK generates data with properties and formats like real data, covering database tables, CSV, Excel and JSON. It supports single-table, multi-table and sequential data, and can learn intertable patterns in complex schemas. That makes it a stronger fit for teams whose test data must preserve connections between tables than a workflow built around isolated records.

Users can choose from hundreds of processing options, anonymize sensitive PII and supply business logic to guide the AI model. AI Connectors detect database metadata, import training subsamples and export synthetic data in the source database format. Supported connectors are AlloyDB, BigQuery, MSSQL, Oracle, PostgreSQL and Spanner.

SDV Enterprise is a downloadable SDK for Linux, Windows and macOS, with specified 64-bit Python and pip requirements. It can run in a secure environment or on premises and does not require GPUs. DataCebo says customer data and generated models stay in the customer environment and are not hosted, stored or accessed by the company. The software may transmit usage and diagnostic telemetry, including performance data, error logs and crash reports, but excludes customer data and created models.

Pricing

SDV Community is free and includes five data types, five basic constraints, nine models, CSV and Excel support, and forum support. It is a sensible way to begin with the core workflow, but the narrower model and data-type selection and lack of listed database connectors make it less suited to teams needing the Enterprise connector workflow.

SDV Bundles costs $250 a month, billed per bundle per user plus usage. It includes CAG, XSynthesizers, Targeted Sampling, Differential Privacy and AI Connectors. It fits buyers who want those named capabilities as a bundle; per-user and usage charges mean the monthly amount is not the whole cost.

SDV Enterprise Base costs $500 a month, billed per user plus usage. It includes 12+ models, 10+ data types, an AI Connectors add-on and dedicated forum support. This is the more complete base for teams that need broader model and data-type coverage, though connectors are an add-on and usage is extra. Both paid plans use pay-as-you-go billing for synthetic data, with a spending cap users can set for monthly cost control. The Community plan is free; no free trial is offered.

Platforms

SDV Enterprise supports Linux, Windows and macOS, as well as self-hosted deployment. API access is available. Its self-hosted design suits organizations that want processing to remain in their environment, though telemetry can still carry usage and diagnostic information.

Who it's for

SDV Enterprise is best for data and engineering teams generating structured synthetic data for testing or machine-learning development, especially when schemas span related tables or deployment must stay on premises. Teams should weigh the per-user subscription and additional usage charges against that control and connector support. It is a weaker fit for someone seeking a fully free, broad-capability package or a hosted web-first workflow.

Pros and cons

• Pro: Multi-table modeling learns relationships across complex schemas, helping preserve table connections in generated data.
• Pro: Self-hosted deployment and the stated customer-data boundary support in-environment processing without GPU requirements.
• Pro: Database connectors cover six named systems and can import subsamples and export in the original database format.
• Con: Paid plans add per-user or per-bundle charges plus usage, so cost grows beyond the base monthly price.
• Con: AI Connectors are an add-on for Enterprise Base, while Community has only five data types and nine models.
• Con: Telemetry may include diagnostics, performance information and crash reports, which may matter to teams with strict monitoring policies.

Alternatives

Browse the Synthetic Data Generation Software directory to compare options. Synth Studio is worth considering for a free option with a stated limit of up to 1M rows per generation and an MIT License. YData SDK may suit teams wanting a free monthly credit, all features and 20+ connectors, with a pay-as-you-go plan also available at $1 per onc.

MOSTLY AI offers a free plan for readers comparing freemium choices. NVIDIA ShadowPlay is a free Windows option with a required supported GPU and GeForce 551.52 driver or later. Simudyne Pulse offers a free plan centered on cached simulations, with rolling daily group and run limits. SynthCity is a free open-source Python library for Linux users. SynthForge IO has a free web tier with stated row and storage limits. Tonic.ai offers a freemium option with Fabricate Plus at $29 a month and $25 of monthly credits.

Verdict

Choose SDV Enterprise if your team needs synthetic structured data that preserves multi-table patterns, database-connected workflows and self-hosted deployment. The main reason to pick it is that mix of schema-aware generation and in-environment operation. Look elsewhere if per-user and usage charges do not suit your budget, or if you need a free plan with broader capabilities.