Overview

SDV combines model choice with tools to evaluate and customize generated data. Teams can train synthesizers using different AI algorithms, generate data on demand, and compare synthetic output with the source data. That makes SDV more than a generator: it is a fit for teams that need to inspect quality and adjust workflows, not just produce sample rows.

The Community SDK can be installed and run on-prem, which suits teams that want a local Python workflow. Its five data types, five basic constraints, and nine models put meaningful boundaries around the free option, however. Teams that need broader coverage, database integrations, or differential privacy should budget for paid offerings.

Key features

Data shapes and generation

SDV supports single-table, sequential, and multi-table relational data. This breadth is useful when a project involves linked records or ordered data rather than a standalone table. Users can select among synthesizer algorithms and generate synthetic data on demand; API data generation is supported, and input formats include CSV, Excel, pandas DataFrame, and JSON metadata.

Evaluation and control

Quality measurement, diagnosis, and visual comparison with real data give teams ways to identify shortcomings in synthetic output. Constraints, preprocessing adjustments, and anonymization options provide further control over how synthesizers handle a dataset. These are useful tools for shaping a workflow, though the Community plan's caps on data types, constraints, and models may limit how far teams can take it without upgrading.

Connectors and privacy

The AI Connectors bundle can detect database schema metadata, subsample data for AI workflows, and export synthetic data in the original database format. Its listed connectors are AlloyDB, BigQuery, MSSQL, Oracle, PostgreSQL, and Spanner. These capabilities matter to teams moving between a database and downstream AI work, but they are paid add-ons rather than part of the free SDK.

The Differential Privacy bundle uses ε-differential privacy and lets users set a privacy-loss budget to manage the tradeoff between privacy and data quality. That makes privacy and utility an adjustable decision rather than a fixed setting, but teams should account for the bundle cost and evaluate the tradeoff for their own use case.

Pricing

SDV is freemium. The Community plan costs 0.00 USD per free, with no-cost billing. It includes five data types, five basic constraints, nine models, CSV and Excel support, local filetypes, forum support, and limited commercial use. It is the sensible entry point for evaluating the SDK or running a bounded local workflow; its caps and limited commercial-use terms make it less suitable as a default for broad production needs.

SDV Bundles cost 250.00 USD per month, billed at $250/mo per bundle plus usage per user. Add-ons include CAG, XSynthesizers, Targeted Sampling, Differential Privacy, and AI Connectors. This tier is for teams that need specific extensions such as database integration or privacy controls, but the bundle price and additional usage charge require budgeting beyond the headline monthly fee.

SDV Enterprise Base costs 500.00 USD per month, billed at $500/mo plus usage per user. It includes 12 or more models, 10 or more data types, and dedicated forum support; AI Connectors are available as an add-on bundle. It better fits organizations that need more breadth and prioritized help than Community provides, though connectors still add cost. Enterprise usage-based pricing charges for synthetic data created and allows a monthly spending cap.

Community and Enterprise users can seek help through the DataCebo Forum, with Enterprise support prioritized during busy periods. Community is free to evaluate; there is no separate paid-plan trial period stated. SDV's privacy policy says usage data such as synthesizers used and cells sampled may be transmitted for billing and service purposes, a consideration for teams with strict data-governance requirements.

Platforms

SDV is available as an API and self-hosted software. Community is a Python SDK for on-prem use, developed on Python 3.9–3.14. That makes it a natural fit for Python-centered stacks and teams seeking local deployment, rather than buyers looking for a web-first product.

Who it's for

SDV is best suited to data and engineering teams that need synthetic versions of structured, sequential, or relational data and want to evaluate and customize results in a Python workflow. It is less compelling for teams seeking a simple hosted interface, or those that need database connectors and privacy controls but cannot take on the paid bundles and per-user usage charges.

Pros and cons

Pro: Support for single-table, sequential, and relational data gives teams room to handle more than isolated tabular samples.
Pro: Quality diagnostics and comparisons help teams assess synthetic output rather than treating generation as a black box.
Pro: On-prem Python SDK deployment suits teams that want a local workflow.
Con: Community limits users to five data types, five basic constraints, and nine models, which can constrain larger or more varied projects.
Con: Database connectors and differential privacy are paid bundle capabilities, not included in the free plan.
Con: Paid plans add usage charges per user, and Enterprise usage billing is tied to data created, so the monthly base price is not the whole cost.

Alternatives

For teams choosing among test-data and synthetic-data tools, the Test Data Management Software, Synthetic Data Generation Software, Test Data Generation Tools, and AI Synthetic Data Generators directories provide broader categories to compare.

Mockaroo is a freemium API, self-hosted, and web option; its free plan allows 1,000 rows per file, unlimited browser downloads, and 200 API requests per day.
Tonic Fabricate offers a free Tonic Cloud plan with $5 monthly credits and basic data exports, plus a $29.00 USD per month Plus plan with $25 monthly credits and metered additional usage.
Windocks is worth considering when Windows or Linux support and container-based database images matter; its free plan limits images to 1 GB and simultaneous containers to one, with no support.
Databucket is a free, open-source API and self-hosted alternative.
MOSTLY AI has a free plan and supports API, Linux, self-hosted, and web deployments.
BlazeMeter is a freemium API and web option; its APIs Small plan costs 79.00 USD per month for 250K requests, five team members, 19 global locations, and standard email support.
Perforce Delphix Test Data Management is an enterprise test data management platform with custom pricing.
Curiosity Software Enterprise Test Data offers custom pricing charged by data type rather than volume.

Verdict

Choose SDV if your team works in Python and needs synthetic data across multiple data shapes, with evaluation and customization in the same workflow. The free Community SDK is a useful way to start, while paid bundles extend it with connectors and differential privacy. Look elsewhere if you need a web-first experience or want those advanced capabilities without bundle and usage costs.

	Syntellix	Synthetic Data Vault
Free plan	No	Yes
Free trial	No	No
Paid from	—	$250/mo
Open source	No	No
Platforms	Web	api, self-hosted
Unstructured data	Yes	—
Free plan	—	Yes
Relational data	—	Yes

Syntellix vs Synthetic Data Vault

Syntellix

Synthetic Data Vault