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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.

Companies
50
Revenue
$399.2M
Funding
$157.2M
Employees
3K

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Top Synthetic Data Software Companies

Showing 10 of 8 companies ranked by annual revenue.

1
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
Customers
-
Year founded
2023
Funding
-
Team size
58
Growth
-
2
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
Customers
-
Year founded
2021
Funding
-
Team size
51
Growth
-
3
Duality Technologies

Hoboken, New Jersey, United States

Duality's breakthrough innovative technologies eliminate the conflict between data protection and business growth and innovation. The Duality Data Analytics and AI platform is built upon advanced encryption methods, hardware technologies, and machine learning techniques that protect sensitive data while in use. Duality is the only multi-PET platform with the ability to combine various technologies to meet the unique needs of sensitive data operations. These guardrails streamline and enhance the data operations critical for data-driven insights and innovations by eliminating bulky, expensive, and limiting processes like data anonymization and tokenization. Traditional data protection methods prevent organizations from truly adopting and leveraging advanced models to their benefit, resulting in restrictive policies like "no sensitive data can be used for model training." With Duality, organizations can confidently customize 3rd party models on their own data without fear of data leaks. Model providers can scale model customization knowing that their proprietary model is never exposed to the customer, preventing competitive intelligence leaks. Financial institutions can turn their manual KYC requests into self-service operations, greatly enhancing the speed and success of these expensive requirements. The benefits of data protection guardrails span from efficiency gains, to unlocking previously inaccessible data, to slashing costs on high-security infrastructure.

Revenue
$5.6M
Customers
-
Year founded
2016
Funding
-
Team size
51
Growth
-
4
Carpe Data

Santa Barbara, California, United States

Developer of predictive scoring and data products for insurers designed to provide a holistic view into each risk. The company's products leverages the social web, online content, wearables, connected devices and other forms of next-generation data to assess risk at critical steps in the insurance policy lifecycle, aggregate and assess the social web as well as consolidate and functionalize the next generation of data, enabling insurers to more accurately predict risk and innovate with new products to meet changing customer habits.

Revenue
$5.6M
Customers
-
Year founded
2016
Funding
$26.7M
Team size
108
Growth
26.5%
5
Flower

, United States

Train AI on distributed data

Revenue
$5.6M
Customers
-
Year founded
2023
Funding
-
Team size
37
Growth
-
6
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
Customers
-
Year founded
2017
Funding
-
Team size
49
Growth
-
7
SafeGraph

Denver, Colorado, United States

SafeGraph is a data company. That's it - that's all we do. We predict the past. SafeGraph's mission is to democratize access to data. SafeGraph's five year goal is to be THE source for accurate data about every physical place in the world. SafeGraph builds truth sets for machine learning, deep learning, and AI. SafeGraph is unlocking the world's most powerful data so that machines and humans can answer society's toughest questions.

Revenue
$5.4M
Customers
-
Year founded
2016
Funding
-
Team size
49
Growth
-
8
SBX Robotics

Toronto, Canada

Synthetic data for better vision.

Revenue
$5M
Customers
-
Year founded
2020
Funding
-
Team size
5
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.