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Top 1 Data Fabric Software Companies With $10M–$100M Revenue (September 2026)

As of September 2026, Latka tracks 1 data fabric software companies with $10M–$100M in annual revenue. They have combined revenues of $23.5M and employ 53 people. They have raised $3M.

Every company below sells data fabric 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 Data Fabric Software Companies do

Data Fabric Software provides a cohesive data management framework that integrates disparate data sources across various platforms and environments. It enables organizations to efficiently access, manage, and analyze their data in real time, promoting a unified view of data assets regardless of where they reside. This approach streamlines data workflows and enhances data governance, making it easier for businesses to comply with regulations and maintain data quality. The primary use cases for Data Fabric Software include data integration, data governance, and data accessibility. It offers features such as data virtualization, data cataloging, and automated workflows, which help organizations improve decision-making processes and support analytics. Typical users of these solutions include IT teams, data analysts, and data engineers who require real-time data access and management capabilities for operational and strategic needs.

Companies
1
Revenue
$23.5M
Funding
$3M
Employees
53

Filters

Sorting: Highest -> Lowest

Filters
Revenue
All<$1M$1M-$5M$5M-$10M$10M-$100M$100M+
Funding (Locked)Growth (Locked)Team-Size (Locked)

Top Data Fabric Software Companies With $10M–$100M Revenue

Showing 1 of 1 companies ranked by annual revenue.

1Stae logo
Stae

Detroit, Michigan, United States

Provider of a digital infrastructure platform designed to offer open access to the world's civic data in one place, in real-time and with a uniform API. The company's digital infrastructure platform offers universal tool for data management and collaboration, integrate data from any source, map different data types and create real-time data feeds, enabling city managers to city workers to make better data-driven decisions, drive performance and manage vendors.

Revenue
$23.5M
Year founded
2016
Funding
$3M
Team size
53

Frequently asked questions about Data Fabric Software Companies With $10M–$100M Revenue

How many data fabric software companies with $10M–$100M in annual revenue are there?

Latka tracks 1 data fabric software companies with $10M–$100M in annual revenue. Together they generate $23.5M in annual revenue and employ 53 people.

Which data fabric software company with $10M–$100M in annual revenue is the largest?

Stae is the largest, with $23.5M in annual revenue, founded in 2016.

How much revenue does a typical data fabric software company with $10M–$100M in annual revenue make?

The average data fabric software company in this list makes $23.5M a year, across 1 companies with reported revenue.

Who are the leading Data Fabric software vendors with $10M–$100M in annual revenue?

Ranked by annual revenue, the leaders are Stae.

How much funding have data fabric software companies with $10M–$100M in annual revenue raised?

The 1 data fabric software companies with $10M–$100M in annual revenue tracked here have raised $3M in disclosed funding between them.

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All IT Infrastructure Software SaaS categoriesEvery category we track inside IT Infrastructure Software, each ranked by revenue.Browse every SaaS industryThe full A-Z of SaaS categories Latka tracks, from Data Fabric to everything else.

Inclusion Criteria

- Must provide seamless integration across multiple data sources and environments - Should enable real-time data access and analytics capabilities - Must include features for data governance and compliance - Should support data virtualization and automated workflows - Not just offer data storage solutions; must also facilitate data management and usability improvements - Must cater to both structured and unstructured data types - Should include tools for data quality assessment and monitoring