1
Protegrity Usa, Inc.
Stamford, Connecticut, United States
Unleash the Power of Secure Data We safeguard privacy, ensure data is protected everywhere, and fuel innovation with secure AI.
- Revenue
- $50.7M
- Year founded
- 1996
- Funding
- $6M
- Team size
- 378
Top 10 Synthetic Data Software Companies (November 2026)
As of November 2026, Latka tracks 43 synthetic data software companies. They have combined revenues of $253.7M and employ 1.9K people. They have raised $159.7M and serve 10K customers combined.
Every company below sells synthetic data software to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.
What Synthetic Data Software Companies do
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.
- Companies
- 43
- Revenue
- $253.7M
- Funding
- $159.7M
- Employees
- 1.9K
Filters
Sorting: Highest -> Lowest
Top Synthetic Data Software Companies by revenue
Showing 10 of 43 companies ranked by annual revenue.
2
DataForce
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]
- Revenue
- $46M
- Year founded
- 2020
- Team size
- 418
3
Foretellix
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.
- Revenue
- $26.2M
- Year founded
- 2017
- Team size
- 150
4
Explorium
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.
- Revenue
- $20.8M
- Team size
- 82
5
Tonic.ai
San Francisco, CAlifornia, United States
Tonic mimics your production data to create safe, useful, de-identified data for QA, testing, and development.
- Revenue
- $18.1M
- Customers
- 30
- Year founded
- 2018
- Funding
- $45M
- Team size
- 94
6
Mostly.ai
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.
- Revenue
- $16.7M
- Year founded
- 2017
- Team size
- 54
7
Sapien
San Francisco, California, United States
Sapien is a decentralized data foundry, turning collective human knowledge into enterprise-grade AI training data.
- Revenue
- $8.6M
- Year founded
- 2023
- Team size
- 58
8
zypl.ai
Dubai, United Arab Emirates
Zypl.ai is a technology company that develops artificial intelligence–based solutions for the financial sector. The company focuses on optimizing credit scoring for financial institutions using synthetic data and offers advanced technologies for data analysis and process automation.
- Revenue
- $6.1M
- Year founded
- 2021
- Team size
- 51
- Revenue
- $5.6M
- Year founded
- 2023
- Team size
- 37
10
Parallel Domain
San Francisco, California, United States
Simulation platform for testing, evaluating, analyzing, and training AI models at scale. Ensuring public safety while accelerating autonomy development. #syntheticdata #autonomy #AI #computervision #AV #ADAS #machinelearning #syntheticdatarealimpact
- Revenue
- $5.4M
- Year founded
- 2017
- Team size
- 49
Frequently asked questions about Synthetic Data Software Companies
How many synthetic data software companies are there?
Latka tracks 43 synthetic data software companies with reported revenue. Together they generate $253.7M in annual revenue and employ 1.9K people.
Which synthetic data software company is the largest?
Protegrity Usa, Inc. is the largest, with $50.7M in annual revenue, founded in 1996.
How much revenue does a typical synthetic data software company make?
The average synthetic data software company in this list makes $5.9M a year, across 43 companies with reported revenue. They serve 10K customers combined.
Who are the leading Synthetic Data software vendors?
Ranked by annual revenue, the leaders are Protegrity Usa, Inc., DataForce, Foretellix, Explorium and Tonic.ai.
How much funding have synthetic data software companies raised?
The 43 synthetic data software companies tracked here have raised $159.7M in disclosed funding between them.
Related Artificial Intelligence Software categories
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.