- Revenue
- $5M
- Customers
- -
- Year founded
- 2020
- Funding
- -
- Team size
- 5
- Growth
- -
Top 50 Synthetic Data Software SaaS Companies in May 2026
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.
Filters
Sorting: Highest -> Lowest
Top Synthetic Data Software Companies
Showing 10 of 16 companies ranked by annual revenue.
2
Data Safeguard Inc.
Santa Clara, California, United States
An Artificially Intelligent, humanly impossible, previously unsolvable, hyper-accurate approach to comply with data privacy compliance and prevent synthetic fraud losses.
- Revenue
- $4.7M
- Customers
- -
- Year founded
- 2021
- Funding
- -
- Team size
- 42
- Growth
- -
3
DemystData
New York, New York, United States
DemystData - Mobilizing the world's data to unlock financial services. Serve new segments of customers by harnessing a universe of data.
- Revenue
- $4.3M
- Customers
- -
- Year founded
- 2010
- Funding
- $31.5M
- Team size
- 84
- Growth
- 40.17%
4
Narrative I/O, Inc.
New York, NY, United States
At Narrative, we revolutionize data collaboration by providing an AI-driven, privacy-centric platform designed for seamless interoperability. Our innovative solutions empower businesses to easily design and execute collaborative data strategies, ensuring control over data governance and commercial terms. With advanced features like automated data standardization, robust security measures, and modular scalability, we simplify the complexities of data aggregation, filtering, and transaction automation. Join us in transforming how data is managed and utilized, enabling smarter decisions and driving growth. Discover the Narrative difference—where data collaboration meets unparalleled efficiency and security.
- Revenue
- $3.6M
- Customers
- -
- Year founded
- 2016
- Funding
- -
- Team size
- 33
- Growth
- -
- Revenue
- $3.1M
- Customers
- -
- Year founded
- 2017
- Funding
- -
- Team size
- 28
- Growth
- -
6
ActivePrime
Mountain View, California, United States
We help Salesforce users identify and resolve data quality issues using a comprehensive AI-enabled Data Quality Platform. We will start with with a complimentary Data Quality Assessment that uses AI to identify data quality issues across 7 key dimensions. Then, we will meet with you to for an in depth discussion regarding the data quality issues our AI-enabled platform discovered and then clean your Salesforce data with ActivePrime AI-enabled CleanData. ActivePrime’s AI-enabled CleanData automates and streamlines the process of data cleanup. There is even an ActivePrime Search Before Create function to catch data errors before they are entered. The data will continually be cleaned as it’s always on and running! Need to run simulations? ActivePrime uses AI to generate synthetic data that mimics your real data. Request a complimentary Data Quality Assessment today! Send us a message or visit our website!
- Revenue
- $2.9M
- Customers
- -
- Year founded
- 2001
- Funding
- -
- Team size
- 26
- Growth
- -
7
YData
Seattle, Washington, United States
Developer of data privacy automated software solution intended to provide privacy and synthetic data tools. The company offers flexible and easy to use Artificial Intelligent based system, generates a user-defined size dataset with preserving the original data statistics but ensuring users privacy and data generated is fully GDPR (General Data Protection Regulation) compliant as well as with other regulative frameworks, enabling clients to an acceleration of business insights extraction and unlocking the data sharing between organizations.
- Revenue
- $2.9M
- Customers
- -
- Year founded
- 2019
- Funding
- -
- Team size
- 33
- Growth
- 105.18%
8
Hazy
London, England, United Kingdom
Developer of a SaaS based data anonymization platform designed to share data securely across the Web or multiple devices. The company's platform combines artificial intelligence to share data securely and automatically and anonymizes personal information to identify and intelligently replace personally identifiable information in evolving datasets, enabling data-centric businesses to share valuable data while protecting privacy of personal information at the same time.
- Revenue
- $2.9M
- Customers
- 10K
- Year founded
- 2017
- Funding
- $6.8M
- Team size
- 33
- Growth
- 92.19%
9
Bitfount
United States
Bitfount is a federated privacy-preserving platform for AI and data collaboration. Use cases range from discovering and evaluating third-party datasets, to running data consortia, training advanced AI models, and much more.
- Revenue
- $2.8M
- Customers
- -
- Year founded
- 2020
- Funding
- -
- Team size
- 25
- Growth
- -
- Revenue
- $2.7M
- Customers
- -
- Year founded
- 2020
- Funding
- -
- Team size
- 18
- Growth
- -
Inclusion Criteria
- 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.