Compare the Top Synthetic Data Generation Tools that integrate with GitHub as of July 2025

This a list of Synthetic Data Generation tools that integrate with GitHub. Use the filters on the left to add additional filters for products that have integrations with GitHub. View the products that work with GitHub in the table below.

What are Synthetic Data Generation Tools for GitHub?

Synthetic data generation tools are software programs used to produce artificial datasets for a variety of purposes. They use a range of algorithms and techniques to create data that is statistically similar to existing real-world data but does not contain any personal identifiable information. These tools can help organizations test their products and systems in various scenarios without compromising user privacy. The generated synthetic data can also be used for training machine learning models as an alternative to using real-life datasets. Compare and read user reviews of the best Synthetic Data Generation tools for GitHub currently available using the table below. This list is updated regularly.

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    AutonomIQ

    AutonomIQ

    AutonomIQ

    Our AI-driven, autonomous low-code automation platform is designed to help you achieve the highest quality outcome in the shortest amount of time possible. Generate automation scripts automatically in plain English with our Natural Language Processing (NLP) powered solution, and allow your coders to focus on innovation. Maintain quality throughout your application lifecycle with our autonomous discovery and up-to-date tracking of changes. Reduce risk in your dynamic development environment with our autonomous healing capability and deliver flawless updates by keeping automation current. Ensure compliance with all regulatory requirements and eliminate security risk using AI-generated synthetic data for all your automation needs. Run multiple tests in parallel, determine test frequency, keep pace with browser updates and executions across operating systems and platforms.
  • 2
    Gretel

    Gretel

    Gretel.ai

    Privacy engineering tools delivered to you as APIs. Synthesize and transform data in minutes. Build trust with your users and community. Gretel’s APIs grant immediate access to creating anonymized or synthetic datasets so you can work safely with data while preserving privacy. Keeping the pace with development velocity requires faster access to data. Gretel is accelerating access to data with data privacy tools that bypass blockers and fuel Machine Learning and AI applications. Keep your data contained by running Gretel containers in your own environment or scale out workloads to the cloud in seconds with Gretel Cloud runners. Using our cloud GPUs makes it radically more effortless for developers to train and generate synthetic data. Scale workloads automatically with no infrastructure to set up and manage. Invite team members to collaborate on cloud projects and share data across teams.
  • 3
    GenRocket

    GenRocket

    GenRocket

    Enterprise synthetic test data solutions. In order to generate test data that accurately reflects the structure of your application or database, it must be easy to model and maintain each test data project as changes to the data model occur throughout the lifecycle of the application. Maintain referential integrity of parent/child/sibling relationships across the data domains within an application database or across multiple databases used by multiple applications. Ensure the consistency and integrity of synthetic data attributes across applications, data sources and targets. For example, a customer name must always match the same customer ID across multiple transactions simulated by real-time synthetic data generation. Customers want to quickly and accurately create their data model as a test data project. GenRocket offers 10 methods for data model setup. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce.
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