1
Databricks
San Francisco, California, United States
a cloud-based data engineering, data science, and machine learning platform
- Revenue
- $7B
- Customers
- 10K
- Year founded
- 2013
- Funding
- $13B
- Team size
- 12.3K
- Growth
- 45.83%
Top 10 AI & Machine Learning Operationalization (MLOps) Software Companies (November 2026)
As of November 2026, Latka tracks 2,854 AI & machine learning operationalization (MLOps) software companies. They have combined revenues of $22.9B and employ 114.8K people. They have raised $29.1B and serve 142.2M customers combined.
Every company below sells AI & machine learning operationalization (MLOps) 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 AI & Machine Learning Operationalization (MLOps) Software Companies do
AI & Machine Learning Operationalization (MLOps) software refers to a suite of tools and practices designed to facilitate the deployment, monitoring, and management of machine learning models in production environments. These solutions aim to streamline the entire machine learning lifecycle, enabling teams to transition from model development to operationalization more efficiently. MLOps software addresses critical stages such as model training, versioning, and continuous integration/continuous deployment (CI/CD), allowing organizations to rapidly iterate and improve their models over time. The primary use cases for MLOps software span various industries, including healthcare, finance, and retail, where organizations apply machine learning to enhance decision-making, forecasting, and operational efficiency. Typical features of MLOps platforms include automated workflows, data management tools, and model performance monitoring, which help ensure that deployed models operate optimally. Common buyer personas typically include data scientists, machine learning engineers, IT operations teams, and project managers focused on leveraging AI technologies to drive business outcomes.
- Companies
- 2,854
- Revenue
- $22.9B
- Funding
- $29.1B
- Employees
- 114.8K
Filters
Sorting: Highest -> Lowest
Top AI & Machine Learning Operationalization (MLOps) Software Companies by revenue
Showing 10 of 2,854 companies ranked by annual revenue.
- Revenue
- $1B
- Year founded
- 2021
- Funding
- $38.3M
- Team size
- 326
- Growth
- 900%
3
TELUS Digital AI Data Solutions
Las Vegas, Nevada, United States
TELUS Digital AI & Data Solutions partners with a diverse and vibrant community to help our customers enhance their AI and machine learning models. The work of our AI Community contributes to improving technology and the digital experiences of many people around the world. Our AI Community works in our proprietary AI ...
- Revenue
- $507.1M
- Year founded
- 2005
- Funding
- $1.6M
4
Lambda AI
San Francisco, California, United States
Lambda is an AI infrastructure company providing cloud services, infrastructure, and software training and inferencing of AI models. It serves as a trusted AI Infrastructure advisor to the world's top AI Labs, Enterprises, and Hyperscalers.
- Revenue
- $500M
- Year founded
- 2012
- Funding
- $320M
- Team size
- 669
5
Simulation AI
London, United Kingdom
Harnessing the power of artificial intelligence to create realistic, adaptable simulations for pilot training, aircraft design testing, air traffic management scenarios, and more. Enhance safety, efficiency, and decision-making in the aviation industry with AI -powered virtual environments.
- Revenue
- $459.4M
6
Invisible
San Francisco, California, United States
We are a tech-enabled service providing solutions to the world’s most complex business problems. Driven by our proprietary process orchestration platform, we seamlessly integrate advanced AI and automation with a global network of over 3,500 experts. This powerful combination delivers new capabilities and eliminates barriers to execution for our clients, unlocking unprecedented efficiency, scale, and growth opportunities.
- Revenue
- $409.9M
- Year founded
- 2015
- Team size
- 2.8K
7
Snorkel AI
Redwood City, California, United States
Snorkel AI is a software company that provides a platform for building and managing training data for machine learning models. Their platform allows developers and data scientists to label data programmatically and efficiently, using a combination of weak supervision and human-in-the-loop labeling. By automating the data labeling process, Snorkel AI enables companies to train machine learning models faster and with higher accuracy, while reducing the cost and time required for data labeling. The company was founded in 2019 by a group of researchers from Stanford University and is based in Palo Alto, California.
- Revenue
- $375M
- Year founded
- 2019
- Funding
- $235.3M
- Team size
- 776
- Growth
- 1775%
8
Dataiku
New York, New York, United States
Dataiku is an enterprise artificial intelligence and machine learning software company that helps businesses harness the power of data to gain insights and make informed decisions. The company was founded in 2013 and is based in New York City, with additional offices in Paris, London, Sydney, and Singapore. Dataiku's platform provides a collaborative environment for data scientists, analysts, and business users to build and deploy machine learning models, and to automate and scale data processes. The platform integrates with a wide range of data sources and tools, including cloud services, databases, and data warehouses, and supports a variety of programming languages. With Dataiku, businesses can gain insights from their data, automate tasks, and improve their operations. The company serves customers in industries such as healthcare, finance, retail, and more, and is focused on driving innovation and customer success.
- Revenue
- $350M
- Customers
- 750
- Year founded
- 2013
- Funding
- $846.9M
- Team size
- 1.6K
- Growth
- 16.67%
9
DataRobot
Boston, Massachusetts, United States
DataRobot provides an automated machine learning platform that helps organizations build and deploy predictive models quickly and efficiently.
- Revenue
- $285M
- Customers
- 850
- Year founded
- 2012
- Funding
- $1.3B
- Team size
- 869
10
KnowBox
Beijing, China
Knowbox is a China-based educational technology company offering artificial intelligence-assisted mobile applications for schools and consumers to help children manage their homework.
- Revenue
- $275M
- Year founded
- 2014
- Funding
- $250M
- Team size
- 2.5K
Frequently asked questions about AI & Machine Learning Operationalization (MLOps) Software Companies
How many AI & machine learning operationalization (MLOps) software companies are there?
Latka tracks 2,854 AI & machine learning operationalization (MLOps) software companies with reported revenue. Together they generate $22.9B in annual revenue and employ 114.8K people.
Which AI & machine learning operationalization (MLOps) software company is the largest?
Databricks is the largest, with $7B in annual revenue, founded in 2013.
How much revenue does a typical AI & machine learning operationalization (MLOps) software company make?
The average AI & machine learning operationalization (MLOps) software company in this list makes $8M a year, across 2,854 companies with reported revenue. They serve 142.2M customers combined.
Who are the leading AI & Machine Learning Operationalization (MLOps) software vendors?
Ranked by annual revenue, the leaders are Databricks, micro1, TELUS Digital AI Data Solutions, Lambda AI and Simulation AI.
How much funding have AI & machine learning operationalization (MLOps) software companies raised?
The 2,854 AI & machine learning operationalization (MLOps) software companies tracked here have raised $29.1B in disclosed funding between them.
Related Artificial Intelligence Software categories
Inclusion Criteria
- The software must provide capabilities for model deployment and management in production environments. - It should enable monitoring and maintenance of machine learning models post-deployment. - The solution must facilitate collaboration among data scientists and operations teams through streamlined workflows. - It should support version control and rollback features for models. - Not just a data analytics tool; must also offer integrated machine learning lifecycle management functionalities.