No. 13 of 26 ·AI Synthetic Data Generators

twinify

Self-hosted · API

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Freedom report

One barScore 5.4

Free tierNo free tier on record
Open codeNo open-source code on record
Runs widely0 of 6 device platforms
DocumentedPlans, terms and facts published

twinify is ranked #13 of 26 in AI synthetic data generators on Freedom251. It runs on API, Self-hosted.

Compared on AI synthetic data generators

Deployment: self_hosted
Unstructured data: No
Privacy-risk metrics: Yes

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The record

Purpose: twinify is a software package for privacy-preserving generation of synthetic twins of sensitive tabular datasets.
Privacy method: It learns probabilistic models under differential privacy, with ε and δ parameters for setting the privacy level.
Inference methods: It implements NAPSU-MQ and differentially private variational inference (DPVI).
Modeling: DPVI supports automatic modeling and user-defined models written with NumPyro.
Ways to use it: The package can be used as a Python library or as a command-line tool that reads CSV datasets.
Data types: DPVI can handle categorical, continuous, or mixed data; NAPSU-MQ is currently suitable only for fully categorical data.
Missing values: Automatic modeling handles missing values by modeling their probability, assuming missingness is independent across features.
Integrations: The implementation relies on NumPyro for modeling and inference, JAX for CPU and GPU kernels, and d3p for differentially private training routines.
Installation: The README says a stable version can be installed from PyPI with pip, or installed from a cloned repository for the development version.
License: The code base is licensed under the Apache License 2.0.
Limitations: NAPSU-MQ may run for a long time on datasets with many feature dimensions, while DPVI approximates the true posterior and does not explicitly capture additional uncertainty due to differential privacy.
Intended audience: The package metadata classifies twinify for scientific research audiences.
Maintainer organization: The DPBayes GitHub organization describes itself as providing differential privacy software from the Finnish Center for Artificial Intelligence FCAI.
Methods: It implements NAPSU-MQ and differentially private variational inference (DPVI).

Where it ranks on Freedom251

Best AI Synthetic Data Generators in 2026#13 of 26

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Sources

github.com/DPBayes/twinify· checked 4 Oct 2026
github.com/DPBayes/twinify/blob/master/setup.py· checked 4 Oct 2026
github.com/DPBayes· checked 4 Oct 2026