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Top 1 Machine Learning Data Catalog Software Companies Under $1M Revenue (September 2026)

As of September 2026, Latka tracks 1 machine learning data catalog software companies under $1M in annual revenue. They have combined revenues of $119.3K and employ 1 people.

Every company below sells machine learning data catalog 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 Machine Learning Data Catalog Software Companies do

Machine Learning Data Catalog Software serves as a specialized framework designed to enhance the management, discovery, and utilization of data specifically for machine learning projects. These solutions facilitate real-time data discovery by automating the cataloging of datasets, enabling organizations to effectively organize and manage their data assets. In doing so, they allow data scientists and machine learning engineers to locate relevant datasets quickly, thereby accelerating the development of machine learning models. Typical features of Machine Learning Data Catalog Software include automated metadata ingestion, lineage tracking, and advanced search capabilities powered by machine learning algorithms. This facilitates easier dataset evaluation and improves collaboration across teams, as stakeholders can access effective data documentation and understand the provenance of their data. Common buyer personas include data scientists, machine learning engineers, data governance professionals, and IT managers, all of whom seek efficient ways to manage and utilize large volumes of data for analytical and operational purposes.

Companies
1
Revenue
$119.3K
Funding
-
Employees
1

Filters

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Filters

Top Machine Learning Data Catalog Software Companies Under $1M Revenue

Showing 1 of 1 companies ranked by annual revenue.

1Deepstributed logo
Deepstributed

Wrocław, Poland

Deepstributed is an app. It makes unimaginably easy to cope with three ML processes that usually make life harder. 1. Finding GPU recources At first we help you to connect your GPUs to our app. Yes, you can manage your experiments from Deepstributed even on your own computing resources. And then, if you want to run more, you can reach for the community GPUs or GPU-as-a-Software resources. 2. Configuring them Then we let you specify which resources and preconfigured runtime frameworks you want to use for a certain experiment. PyTorch, Tensorflow, Caffe? Yes, we have them all. And many more. 3. Running experiment smoothly Finally, you upload your code, data and run the experiment on the cheapest resources possible and no configuration effort. Nice, huh?

Revenue
$119.3K
Year founded
2018
Team size
1

Frequently asked questions about Machine Learning Data Catalog Software Companies Under $1M Revenue

How many machine learning data catalog software companies under $1M in annual revenue are there?

Latka tracks 1 machine learning data catalog software companies under $1M in annual revenue. Together they generate $119.3K in annual revenue and employ 1 people.

Which machine learning data catalog software company under $1M in annual revenue is the largest?

Deepstributed is the largest, with $119.3K in annual revenue, founded in 2018.

How much revenue does a typical machine learning data catalog software company under $1M in annual revenue make?

The average machine learning data catalog software company in this list makes $119.3K a year, across 1 companies with reported revenue.

Who are the leading Machine Learning Data Catalog software vendors under $1M in annual revenue?

Ranked by annual revenue, the leaders are Deepstributed.

Related IT Infrastructure Software categories

Inclusion Criteria

- Must offer automated metadata management to simplify data organization - Should provide advanced search functionalities to enable quick data discovery - Must include lineage tracking to visualize data flow and relationships - Should facilitate collaboration among teams by offering clear documentation and accessibility - Must cater specifically to machine learning use cases, not just general data management - Should integrate seamlessly with existing data tools and platforms used by the organization - Not just a data storage solution; must actively support data discovery and utilization features