Gretel Models

Reference docs for Gretel Models.

Gretel provides a number of different model types which may be utilized directly or combined via workflows. This page will outline the different categories of models that Gretel offers.

Model Configurations

Gretel configurations are declarative objects that specify how a model should be created. Configurations can be authored in YAML or JSON. Each of the below models will be declared and configured via a model configuration.

For more information, please refer to the Model Configurations documentation. For more information about each of the specific model types, refer to their individual sections.


Synthetic models are the core models you will use to generate privacy guaranteed synthetic data. Here are the Synthetic data models supported by Gretel APIs.

  • Gretel ACTGAN - Adversarial model for tabular, structured numerical, high column count data.

  • Gretel Tabular DP - Graph-based model for tabular data with differential privacy.

  • Gretel GPT - Generative pre-trained transformer for natural language text.

  • Gretel DGAN - Adversarial model for time-series data.

  • Gretel Amplify - Statistical model for high volume tabular data.

  • Gretel LSTM - Language model for tabular, time series, text data.

For more information, please refer to the Synthetics documentation.


Gretel’s Transform model combines data classification with data transformation to easily detect and anonymize or mutate sensitive data. Gretel’s data classification can detect a variety of Supported Entities such as PII, which can be used for defining transforms. Gretel Transform can be combined with Gretel Synthetics using workflows to transform data before training the synthetic model or after generating output synthetic data.

For more information, please refer to the Transform documentation.


The Classify model allows you to define a policy to discover and label sensitive data including personally identifiable information, credentials, and even custom regular expressions inside text, logs, and other structured data.

For more information, please refer to the Classify documentation.


With Gretel Relational, you can synthesize and transform multiple tables or entire SQL databases, while ensuring referential integrity, accuracy, and privacy.

For more information, please refer to the Relational documentation.

Navigator (previously Tabular LLM) is Gretel's first AI system designed to generate, edit, and augment tabular data using natural language or SQL prompts. It's a tool for working with and enhancing datasets in a more intuitive and interactive way.

For more information, please refer to the Navigator documentation.

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