SDV Enterprise

SDV Enterprise is a downloadable Python SDK for generating synthetic data that retains properties and formats of real data. It is intended for software testing, machine-learning development, and other uses. It works with structured information in database tables, CSV, Excel, and JSON, and can learn relationships across multi-table schemas. The SDK can be installed in a secure environment or on premises on Linux, Windows, or macOS, and does not require GPUs. DataCebo says customer data and models stay in the customer’s environment and are not hosted, stored, or accessed by the software. Users can select from hundreds of data-processing options, anonymize sensitive personal information, and provide business logic to the model. AI Connectors detect database metadata, bring in training-data samples, and export synthetic data in its original database format; listed connectors include AlloyDB, BigQuery, MSSQL, Oracle, PostgreSQL, and Spanner. SDV Community is available at no cost. SDV Enterprise Base is $500 per user per month plus usage, and SDV Bundles are $250 per bundle per user per month plus usage.

SDV Enterprise is designed for organizations that want programmable synthetic-data generation inside their own environment. The no-cost Community plan provides an entry point, while paid offerings add broader capabilities with usage charges.

It suits teams that need synthetic structured data for testing or machine-learning development and can install a Python SDK in their own environment. Its multi-table modeling and database connectors may fit users working with connected database schemas.

SDV Enterprise is a programmable Python toolkit for generating structured synthetic data while keeping processing in a company-controlled environment. It suits engineering, data-science and compliance-conscious teams working with connected database schemas. Choose it when on-premises deployment and deep workflow control matter more than a simple hosted interface; usage billing and a narrower free tier make it less attractive for casual experimentation.

Overview

The SDK creates artificial records that retain the properties and formats of operational data, supporting software testing, machine-learning development and related work. It handles rows-and-columns data from database tables, CSV files, Excel spreadsheets and JSON. Single-table, multi-table and sequential modalities are covered, with multi-table modeling designed to preserve relationships across complex schemas.

SDV Enterprise is downloadable and installable in secure or on-premises environments on 64-bit Linux, Windows or macOS with Python and pip. It does not require GPUs. DataCebo says customer data and generated models stay in the customer environment rather than being hosted, stored or accessed by the company. Telemetry can still include synthesizer usage, sampled-cell counts, accessed features, performance information, error logs and crash reports; customer data and created models are excluded.

Key features

Programmable generation

Hundreds of data-processing options let teams shape generation around their own rules. Sensitive PII can be anonymized, and business logic can be supplied to guide the model. That flexibility is valuable for regulated or highly customized pipelines, but it implies more engineering involvement than a preset generator.

Multi-table and sequential modeling

The software learns intertable patterns instead of treating each table as an isolated source. This is the central advantage for testing applications whose records depend on linked entities. Sequential data support broadens the fit beyond ordinary static tables.

AI Connectors

The connector bundle detects database metadata, imports training-data subsamples and exports synthetic results in the original database format. Connectors cover AlloyDB, BigQuery, MSSQL, Oracle, PostgreSQL and Spanner, reducing transfer work for teams already using those systems. API access and CSV, Excel, JSON and database outputs support downstream automation.

Pricing

SDV Community — $0, free. It includes five data types, five basic constraints, nine models, CSV and Excel support, and forum support. This is a useful evaluation tier, but its caps and file-format scope make it a poor fit for broad enterprise schemas.

SDV Bundles — $250 a month per bundle per user, plus usage. Bundles add CAG, XSynthesizers, Targeted Sampling, Differential Privacy and AI Connectors. They suit teams that need specialized generation and privacy tooling without immediately taking the full base subscription. Monthly synthetic-data charges remain separate.

SDV Enterprise Base — $500 a month per user, plus usage. It provides 12+ models, 10+ data types, an AI Connectors add-on and dedicated forum support. This is the practical starting point for production teams, though connector access is an add-on and pay-as-you-go generation can raise costs. Users can set a monthly spending cap. There is no free trial; the Community plan is the no-cost route for assessment.

Platforms

SDV Enterprise runs on Linux, Windows and macOS, and supports self-hosted deployment. Its Python SDK and API access fit scripted pipelines, while database output and connectors target existing data platforms. The on-premises option is a strong match for environments that cannot send source data to a vendor service.

Who it's for

Choose SDV Enterprise for organizations testing software with relational data, developing models from sensitive records, or enforcing internal data boundaries. It is especially well suited to teams that need linked-table fidelity, programmable business rules and database-native import and export. Small teams wanting a quick, hosted, low-commitment generator should look elsewhere, as the paid tiers combine per-user fees with usage billing and the free plan is deliberately limited.

Pros and cons

• Pros: Self-hosted operation keeps data and generated models in the customer environment; multi-table learning preserves schema relationships; extensive processing controls support custom rules and PII anonymization; GPU-free installation lowers infrastructure requirements; spending caps help control variable usage costs.
• Cons: Enterprise pricing starts at $500 per user per month before usage; Bundles and Base both add usage charges; AI Connectors are not fully included in the Base price; Community is limited to five data types, five constraints and nine models; telemetry still transmits diagnostic and usage information.

Alternatives

For a broad directory of comparable tools, see Synthetic Data Generation Software.

NVIDIA ShadowPlay is a free Windows utility rather than a synthetic-data platform, so pick it only for that different capture use case. SynthEngyne is the better starting point when monthly free credits and text, tabular, time-series or image generation are more important than self-hosting. Tonic.ai fits readers seeking a freemium API, web or self-hosted product with a $29 monthly entry plan and usage credits. Simudyne Pulse is for cached financial simulations through API or web access, not general synthetic database generation. Synth Studio suits users wanting a free, MIT-licensed tool with up to 1M rows per generation. Synthetic AI Data offers a freemium API, web and self-hosted alternative with a free starting plan. SynthForge IO is a freemium API/web option with a free row allowance and no trial. Tabularis.AI is a lower-cost API or self-hosted choice, with a free developer tier and a $19 starter plan.

Verdict

SDV Enterprise is the right choice for organizations that need programmable, relational synthetic data inside their own infrastructure, especially where PII controls and database connectivity are priorities. Its decisive drawback is cost structure: substantial per-user subscriptions plus metered generation. Pick it for governed, customized enterprise pipelines; look elsewhere for a hosted workflow, a generous free quota or simpler pricing.