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Gretel Releases Privacy Engineering Developer Stack

Company announces comprehensive privacy solution for creating safe, shareable synthetic data.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Gretel has released its privacy engineering APIs and services. Gretel’s comprehensive offering enables users to classify, transform, and generate high-quality synthetic data. Combined, these capabilities remove privacy bottlenecks that prevent data sharing and stifle innovation for a myriad of development and workflow processes. A free plan for developers is available to anyone who wants to get started, and usage-based options are available for larger projects and teams.

Gretel has tested its products in an open beta program for over a year and incorporated improvements to its toolkit based on feedback from more than 60 enterprise engagements, its community of thousands of users, and open source users who have downloaded their SDK over 70,000 times.

Gretel has been working with teams and organizations across industries including healthcare, life sciences, finance, and gaming. Some of their recent work includes creating synthetic genomic data and synthetic time-series banking data. The broad interest in Gretel’s privacy engineering tools is not surprising and is supported by analysts’ forecasts that by 2030, synthetic data will completely overshadow real data in AI models

Gretel is committed to fostering a culture of trust, transparency, and shared knowledge with the public and developer community. They continue to open source their core synthetic data technology and research, as well as offer free access to its tools through its Developer tier.

Advanced Privacy Engineering Made Accessible

With Gretel’s all-in-one privacy stack, developers can streamline workflows and access advanced privacy engineering tools to easily:

  • Create highly accurate, privacy-proven synthetic data
  • Seed preproduction systems with safe, statistically accurate data sets
  • Identify and remove sensitive data to reduce PII-related risks
  • Augment and de-bias data sets to train ML/AI models fairly
  • Anonymize sensitive data in real time, for data at scale

Gretel is also previewing an AWS S3 connector for its toolkit, and anyone interested in it can contact Gretel directly. For more information about Gretel’s toolkit, see their product overview or visit

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