The Synthetic Data Vault (SDV) is a Python library from DataCebo that learns statistical patterns from a real dataset and samples a synthetic one shaped like it. When the database in front of you is empty, that training step is the whole problem, and it is the usual reason someone starts looking for an SDV alternative.
Who SDV fits
SDV mostly fits data science work. You hold a table of real records, you cannot share it, and you want a stand-in with the same distributions to train or evaluate a model on. SDV Community installs with pip install sdv, has been developed on Python 3.9 to 3.14, and ships under the Business Source License 1.1 rather than a conventional open-source licence.
Where schema-first seeding differs
Seedfast starts from the schema rather than a dataset. Give it a PostgreSQL connection string and a sentence describing what the data should represent, one of the shapes in scope examples, and it reads the tables and constraints as they stand before writing rows that satisfy them. Nothing has to be loaded afterwards either, which matters more than it sounds, because SDV's sample() returns pandas DataFrames and the insert is still yours to write.
SDV does have a from-scratch path, and it sits behind a licence. DayZSynthesizer "produces synthetic data from scratch using the metadata," and the same page states the feature "is only available for licensed, enterprise users."
| SDV Community | Seedfast | |
|---|---|---|
| What it needs to start | Real training data plus a metadata object | A connection string and a sentence |
| Runtime | Python 3.9 to 3.14, pip install sdv | A CLI, plus an MCP server for AI agents |
| Where the output lands | pandas DataFrames you insert yourself | Rows written into your database |
| Related tables | HMASynthesizer, "optimized for smaller datasets with around 5 tables and 1 level of depth" | One scope across every table it touches |
| Generating with no source data | DayZSynthesizer, an SDV Enterprise feature | The ordinary path |
| Licence | Business Source License 1.1, converting to MIT four years after release | Commercial; the CLI runs on your machine |
| Price | Free to install; Enterprise by quote | $0 with $5 of credits a month, $16 with $32, $69 with $180 |
Which SDV alternative fits your job
Stay with SDV when you have real records, work in Python, and care that the synthetic copy holds the statistical shape of the original. Seedfast is the answer to a different question, the one you get when the Postgres schema is empty and an application has to boot against it today. We would rather say that plainly than stage a rivalry between two tools aimed at separate problems.
Frequently asked questions
Is SDV open source and free?
SDV Community is free to install and its source is public, but the licence is not a conventional open-source one. The repository ships under Business Source License 1.1, with a grant that forbids offering the library's functionality as a synthetic data service, converting to MIT four years after each release. SDV Enterprise is priced by quote.
Can SDV generate data without any real data?
Yes, through DayZSynthesizer, which the docs describe as producing synthetic data from scratch with "no machine learning required." It is marked an SDV Enterprise feature for licensed users only. Everything in SDV Community fits on a real dataset first, so an empty schema needs either that Enterprise path or a schema-first tool.
Does SDV write rows into a PostgreSQL database?
The community edition does not, on its own. You call sample(), receive DataFrames, then handle the insert yourself in the right order. Connecting to a live database belongs to the Enterprise AI Connectors bundle. Seedfast writes into PostgreSQL over the connection string, which is what makes it one step in CI/CD database seeding.
Sources
Each of these was read on 10 September 2026:
- Welcome to the SDV
- SDV Community, for the licence and Python versions
- The SDV licence text
- HMASynthesizer, for the five-table guidance
- DayZSynthesizer, for the Enterprise notice
- Integration overview, for training data and the AI Connectors bundle
Verified: September 2026. SDV moves its page paths occasionally, so look up its current page before relying on a detail here.
Two neighbours are worth a look. Syntho alternative covers the self-hosted UI platform that comes up in the same conversation, and the test data tools comparison puts every option in one matrix. To try the schema-first route on your own schema, the free plan asks for no card and a first seed takes about two minutes.