As of May 2026, there are 50 SaaS companies in Synthetic Data Software. They have combined revenues of $399.2M and employ 3K people. They have raised $157.2M and serve 10.3K customers combined.
Synthetic data software provides tools that generate artificial datasets which mimic real-world data. These datasets can be used for development, testing, and training machine learning models while ensuring privacy and compliance with data protection regulations. The primary use cases include software testing, model training, and data analysis where maintaining confidentiality is paramount. These tools typically offer features such as data generation, data masking, and customization options to create datasets that resemble original data patterns. Common buyer personas for synthetic data software include software developers, data scientists, compliance officers, and IT managers who require secure, scalable solutions to maintain data integrity without sacrificing privacy.
Sorting: Highest -> Lowest
Showing 10 of 50 companies ranked by annual revenue.
London, United Kingdom
Prolific helps AI developers, researchers, and organizations easily access the highest-quality human data. It is a technology company building the biggest pool of quality human data in the world and the ultimate platform to access it.
Stamford, Connecticut, United States
Unleash the Power of Secure Data We safeguard privacy, ensure data is protected everywhere, and fuel innovation with secure AI.
New York, New York, United States
DataForce delivers high-quality, multimodal training data and services to power the next generation of AI. From large language models to voice, image, and video generation, DataForce supports AI innovators in tech, life sciences, automotive, and beyond with scalable, secure solutions for development, testing, and safety. Backed by cutting-edge technology and over one million data contributors, DataForce helps ensure AI systems are accurate, adaptable, and ready for real-world deployment. DataForce is part of TransPerfect, the world’s largest provider of language and AI solutions for global business, with offices in more than 140 cities worldwide. Learn more at www.dataforce.ai. Contact: [email protected]
Ramat Gan, Israel
Foretellix is the leading provider of data-automation for AI-powered autonomy, offering a trusted development toolchain for training and validating automated driving systems.
Gurgaon, Haryana, India
Synaptic is an alternative data platform that provides actionable insights for private and public market investors.
San Mateo, California, United States
Explorium is a leading data company that uses GenAI technology to build the world’s largest and highest quality collection of premium external data, empowering businesses to make accurate go-to-market decisions. With our profound expertise in data science and years of building enterprise-grade external data, we can uncover a new world of business data attributes that were not possible before, allowing us to create tailor-made data sets to solve fundamental business questions for our customers.
San Francisco, CA, United States
Unlock insights. Inspire innovation. People Data Labs builds people and company data for developers, engineers, and data scientists. We handle the heavy-lifting of data collection, so you can build innovative and compliant data solutions at scale. Every day, our clients use our data to build person profiles, enrich person records, power predictive modeling, drive artificial intelligence, and build new tools to make their teams more efficient, productive, and successful. We’re proud to be the preferred data partner to the data science and engineering teams building the next generation of data-driven products and services.
San Francisco, CAlifornia, United States
Tonic mimics your production data to create safe, useful, de-identified data for QA, testing, and development.
Vienna, Vienna, Austria
Mostly AI develops a GPU-powered technology that simulates synthetic customer data at scale, enabling organizations to generate high-quality, privacy-safe synthetic data and seamlessly analyze and share data across teams.
- The software must generate synthetic datasets that replicate the structure and characteristics of real data. - It should provide capabilities for data masking and privacy preservation. - The platform should support various data types including structured data, images, and text. - Tools must allow customization to suit different development and testing requirements. - Solutions should integrate easily with existing development and data science workflows. - Not just a data augmentation tool; it must also create entirely synthetic datasets suitable for training and testing.
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