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Top 7 DataOps Platforms SaaS Companies in May 2026

As of May 2026, there are 7 SaaS companies in DataOps Platforms. They have combined revenues of $37.1M and employ 320 people. They have raised $33M and serve - customers combined.

DataOps Platforms are designed to streamline and enhance the management of data workflows, enabling organizations to deliver high-quality data products more efficiently. These platforms facilitate the integration of data from various sources, automate data preparation, and ensure that data is accessible for analytics and decision-making. By promoting collaboration across teams, DataOps Platforms help organizations maintain data integrity while accelerating the data lifecycle from conception to deployment. Common use cases for DataOps Platforms include data integration, data quality monitoring, and automating data pipeline processes. Typical features may include version control for data, automated testing and deployment, and dashboards for monitoring data flows. Various personas, such as data engineers, data analysts, and IT managers, engage with these platforms to optimize their data strategies and ensure their projects align with organizational goals.

Companies
7
Revenue
$37.1M
Funding
$33M
Employees
320

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Top DataOps Platforms Companies

Showing 10 of 1 companies ranked by annual revenue.

1
Rafay Systems

Sunnyvale, California, United States

The Rafay Platform is the infrastructure orchestration and workflow automation platform for enterprise AI and cloud-native use cases. The Rafay Platform delivers optimal developer productivity through self-service consumption of environments and experiences, making infrastructure truly composable.With Rafay, developers and data scientists experiment faster, ship new capabilities and features quicker, reduce infrastructure waste, increase utilization and drive better business outcomes.

Revenue
$25.1M
Customers
-
Year founded
2017
Funding
$33M
Team size
158
Growth
270.49%

Inclusion Criteria

- Must provide capabilities for automating data workflows - Should support integration of diverse data sources and formats - Must include features for monitoring data quality and access control - Should offer collaboration tools for cross-team communication - Not just focused on data storage; must also facilitate data processing and analytics - Must enable version control and traceability for data products