Synthetic data generation tools create secure fake data that mirrors real data. The top 2025 tools are K2view, Gretel, MOSTLY AI, Syntho, YData, and Hazy.
Synthetic data generation is the process of creating artificial data that mirrors the features, structures, and statistical attributes of production data, while maintaining compliance with data privacy regulations.
Think of it as crafting a perfect digital twin of your data – retaining all valuable insights without any privacy risks.
Enterprises leverage synthetic data to test software under development at scale and to train AI models without exposing sensitive data. As organizations increasingly rely on AI data, having a robust synthetic data generation tool has become essential.
In this article, we’ll explore how synthetic data generation works, why it’s important, and the best synthetic data generation tools to consider in 2025.
Synthetic data examples include:
Structured, tabular data, such as customer records, financial transactions, healthcare data, and employee information, fits neatly into rows and columns, making it ideal for traditional database systems and analytical applications.
Unstructured data, such as images, videos, audio recordings, and IoT sensor data, lacks a defined format, making it more challenging to generate and use but equally valuable for applications like image recognition and natural language processing.
Types of synthetic data (Source: Gartner Peer Community)
According to Gartner Peer Community, synthetic text-based data – which can be structured or unstructured depending on its format – is leveraged the most, with 84% of organizations using it. Image-based (54%) and tabular (53%) synthetic data follow closely.
The best synthetic data generation tools enable greater accuracy, efficiency, and privacy in 2 key areas:
Synthetic data generation plays a critical role in software testing by creating representative datasets for testing application functionality, performance, and reliability in production environments. It provides testing teams complete control over their datasets while maintaining data privacy. Also, the ability to quickly generate large volumes of synthetic test data accelerates the software development cycle, making it easier to identify and fix issues prior to deployment.
For data scientists, synthetic data creation results in an unlimited supply of training data without the associated privacy concerns. It enhances AI model training by introducing controlled variations and edge cases, diversifying the data to combat bias, balancing underrepresented classes, and overcoming data scarcity. High-quality synthetic data, that mimics real-world patterns, enables organizations to build more robust, unbiased models while maintaining data privacy.
The ability to synthesize data makes the lives of data scientists and developers easier by unlocking the following valuable benefits:
Data availability
Generate large volumes of realistic test data on-demand, eliminating dependencies on production data or manual creation.
Privacy compliance
Meet regulatory requirements by using synthetic data that maintains the statistical properties of real data without exposing sensitive information.
Cost efficiency
Reduce expenses associated with data collection, storage, and maintenance, while enabling unlimited data generation.
Edge case testing
Create rare scenarios and edge cases that might be difficult or impossible to obtain from real-world data.
Scalability
Scale data volume up or down, to test system performance and train models under various conditions.
Controlled variations
Introduce specific data patterns, anomalies, or characteristics to test specific functionality or train models for specific scenarios.
Bias reduction
Generate balanced datasets that represent diverse scenarios and populations, helping to minimize algorithmic bias.
Time savings
Accelerate development cycles by eliminating the need to wait for real data provisioning or complex data preparation processes.
With the ability to use GenAI to create synthetic data, Gartner predicts the demand and volume of synthetic data for software testing and AI model training will rise significantly over the next 5 years:
Growth in synthetic structured data
Gartner projects that through 2030, synthetic structured data will grow at least 3 times as fast as real structured data for AI model training.
Dominance in image and video training
By 2030, synthetic data will constitute more than 95% of data used for training AI models in images and videos.
Reduction in privacy violations
By 2030, synthetic data will help companies avoid 70% of privacy violation sanctions by reducing the need for personal customer data collection.
Expansion in edge scenario training
Synthetic data usage for filling edge scenarios in training AI models is expected to grow from 5% today to over 90% by 2030.
The synthetic data generation market is rapidly expanding and evolving. Here’s an overview of the 6 best solutions available today:
K2view offers a comprehensive synthetic data management platform that combines multiple data generation approaches, including AI-powered generation, intelligent masking, rules-based generation, and data cloning. K2view empowers both technical and non-technical users to generate synthetic data on-demand while maintaining data compliance with regulations like GDPR, CPRA, and HIPAA.
Through its innovative entity-based approach and patented Micro-Database™ technology, K2view ensures referential integrity and enables complete lifecycle management – from source connection and PII identification to test environment deployment and CI/CD pipeline integration. The platform has proven successful in Fortune 500 companies, helping organizations accelerate software delivery, enhance test coverage, and reduce testing costs while maintaining data security and compliance.
Gretel provides a synthetic data platform for developers and AI engineers who use the platform's APIs to generate anonymized and safe synthetic data while preserving data privacy. Key features include fine-tuning capabilities to tailor synthetic data to specific domains, maintaining complex relationships within data, and providing quality metrics to assess the privacy and accuracy of the generated synthetic data.
MOSTLY AI's synthetic data platform transforms production data into privacy-safe synthetic versions through a streamlined six-step process. Users create a generator by uploading their data, configure relationships and model settings, and let the platform's Generative AI automatically train models. The resulting generators can be shared across teams to create customized synthetic datasets. The platform includes an AI Assistant for natural language data exploration and supports multiple use cases, including AI development and testing.
Syntho's AI-based engine creates artificial datasets that mirror the statistical patterns of original data while ensuring privacy. Their Syntho Engine platform features quality assurance reporting, time-series data support, and up-sampling capabilities, making it suitable for analytics, data sharing, and product demonstrations. The solution helps organizations unlock data value while maintaining privacy-by-design principles, with no one-to-one relationship to real data.
YData's Fabric platform combines automated data profiling with synthetic data generation to help organizations improve their training data quality. The platform offers both no-code and SDK options for data teams to profile, generate, and enhance datasets, enabling faster AI development while maintaining privacy compliance. Users can easily connect data sources, experiment with their data, and scale their workflows through an integrated environment.
Hazy provides a secure synthetic data platform that generates privacy-protected artificial data without moving sensitive information from its source environment. Their solution supports multiple data types, includes differential privacy mechanisms, and offers both UI and SDK access while maintaining regulatory compliance. Teams can safely generate and validate synthetic data through controlled permissions and built-in security features.
Here's a table summarizing the pros and cons of each tool:
| Tool | Pros | Cons |
| 1. K2view |
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| 2. Gretel |
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| 3. MOSTLY AI |
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| 4. Syntho |
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| 5. YData |
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| 6. Hazy |
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As organizations embrace generative AI (GenAI), the need for high-quality synthetic data has never been greater. The key to success lies in an entity-based approach. By generating synthetic data around business entities – such as customers, devices, or orders – organizations ensure their artificial data maintains referential integrity across all systems.
This approach serves as a blueprint, guaranteeing that generated data remains contextually accurate and consistent, regardless of the generation technique used.
As we move toward 2025, organizations that adopt robust synthetic data generation tools with entity-based architectures will gain significant competitive advantages. They'll be better positioned to accelerate development cycles, train AI models more effectively, and maintain compliance with evolving privacy regulations – all while keeping their sensitive data secure.
Discover K2view, the best synthetic data generation tool for 2005.