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Metasyn

github.com

Free plan WindowsmacOSLinuxSelf-hosted
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Spec score 7.6 #15 of 25 in Test Data Management Software

What it is

Metasyn generates synthetic tabular data to help make sensitive datasets more accessible, reproducible, and reusable. Users fit a MetaFrame to a dataframe, save the model and settings as auditable JSON, then generate new rows. It handles varied data types, missing values, categories, unique key columns, and structured strings such as postcodes or identifiers. Metasyn supports pandas and Polars dataframes and uses Faker for localized names, emails, phone numbers, and other data. By default, its model excludes multivariate information to reduce disclosure risks. A disclosure-control plugin is available, but synthetic output still needs human review and is not guaranteed to be free of sensitive information. The plugin system allows users and organizations to add extensions. Generated dataframes can be exported to CSV, Parquet, Excel, and other formats. The free, MIT-licensed Python package is self-hosted and can be installed with pip from PyPI or directly from GitHub. The project also points users to versioned Docker containers and a command-line interface. Its intended users include public organizations, research groups, and individual researchers with sensitive datasets.

Spec sheet

Purpose
Metasyn generates synthetic tabular data to help make sensitive datasets more accessible, reproducible, and reusable.Read 4 Oct 2026 · github.com
Intended users
The documentation identifies public organizations, research groups, and individual researchers who own sensitive datasets as the intended audience.Read 4 Oct 2026 · metasyn.readthedocs.io
Deployment
The project points users to versioned Docker containers and a command-line interface for building programs with Metasyn.Read 4 Oct 2026 · github.com
Integrations
Metasyn supports Polars and pandas dataframes and uses Faker to generate localized names, emails, phone numbers, and other data.Read 4 Oct 2026 · github.com
Support
Users can report bugs or ask technical questions through GitHub issues and can contact Raoul Schram.Read 4 Oct 2026 · github.com
License
The software is distributed under the MIT License, which grants free use, modification, distribution, and sublicensing subject to its conditions.Read 4 Oct 2026 · github.com
Exports
Generated dataframes can be exported to CSV, Parquet, Excel, and other formats.Read 4 Oct 2026 · github.com
Extensibility
A plugin system lets users or organizations add extensions to adapt Metasyn to their needs.Read 4 Oct 2026 · github.com
Installation
The package can be installed with pip from PyPI or directly from GitHub for the development version.Read 4 Oct 2026 · github.com
Privacy approach
By default, Metasyn excludes multivariate information from its model to reduce risks such as identity, attribute, or group disclosure.Read 4 Oct 2026 · github.com
Privacy plugin
The maker offers a disclosure-control plugin, while warning that synthetic output still needs human review and is not guaranteed to be free of sensitive information.Read 4 Oct 2026 · github.com
Supported data
Metasyn handles multiple data types, missing values, categorical data, unique key columns, and structured strings such as postcodes or identifiers.Read 4 Oct 2026 · github.com
Workflow
Users fit a MetaFrame to a dataframe, save its model and settings as auditable JSON, and synthesize new rows.Read 4 Oct 2026 · github.com

Plans and prices

Metasyn Free Open-source Python package · MIT license Read 4 Oct 2026 · github.com

Every Metasyn plan and price →

Strong points and limits

Strong points

  • Handles missing values and structured strings.
  • Supports pandas and Polars dataframes.
  • Exports to CSV, Parquet, and Excel.
  • Plugin system supports extensions.
  • Free under the MIT License.

Limits

  • Synthetic output still needs human review.
  • Output is not guaranteed free of sensitive information.
  • Self-hosted package requires installation.

Who it suits

Metasyn suits public organizations, research groups, and individual researchers working with sensitive tabular data. It is designed for users who can review synthetic output and operate a self-hosted Python package.

Verdict

Metasyn offers a documented workflow for fitting and reusing tabular-data models, with multiple dataframe integrations and export formats. Its privacy approach reduces some disclosure risks but does not remove the need to review output.

Where Specifiction places it

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