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
- $9B
- Team size
- 12.3K
- Growth
- 45.83%
Top 50 AI & Machine Learning Operationalization (MLOps) Software Companies $100M+ Revenue (October 2026)
As of October 2026, Latka tracks 63 AI & machine learning operationalization (MLOps) software companies with $100M+ in annual revenue. They have combined revenues of $20.3B and employ 80.2K people. They have raised $23.6B and serve 10M 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
- 63
- Revenue
- $20.3B
- Funding
- $23.6B
- Employees
- 80.2K
Filters
Sorting: Highest -> Lowest
Top AI & Machine Learning Operationalization (MLOps) Software Companies $100M+ Revenue
Showing 10 of 63 companies ranked by annual revenue.
2
MegazoneCloud
Seoul, South Korea
MegazoneCloud accelerates your journey to the AI era with comprehensive cloud, AI, and cybersecurity solutions.
- Revenue
- $1.2B
- Year founded
- 2018
- Funding
- $510M
- Team size
- 1K
3
Metropolis
Los Angeles, California, United States
Metropolis is a proprietary AI- and computer vision-based operating system designed to revolutionize parking management and support the advancement of mobility capabilities.
- Revenue
- $580M
- Year founded
- 2017
- Funding
- $1.8B
- Team size
- 603
4
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
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
DemandScience
Danvers, Massachusetts, United States
DemandScience is the premier AI-powered, B2B demand generation company accelerating global growth for our clients. The DemandScience intelligence platform empowers B2B organizations to swiftly identify the right accounts and target in-market buyers with precision. By combining groundbreaking technologies and AI innovation, the company ensures timely delivery of accurate data, intelligence, and insights, adding value to the end-to-end journey from initial engagement to conversion. Founded in 2012, DemandScience provides 1,500 global customers with superior marketing solutions, B2B data, and leads.
- Revenue
- $381.6M
- Customers
- 1.5K
- Year founded
- 2012
- Team size
- 1.1K
8
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
- 153.38%
9
Cartrack
United States
Cartrack is a leading global Software-as-a-Service (SaaS) platform provider for small, medium and large businesses, as well as consumers needing a software platform for data analytics to optimise fleets, driver behaviour, insurance risk, safety and asset recovery. Data analytics remain Cartrack’s primary offering, in addition to growing its artificial intelligence and value-added services to deliver a tangible return on investment to its subscribers. Cartrack is also renowned for its agility and speed in developing innovative, first-to-market solutions that are aimed at further enhancing the customer experience. Cartrack’s impressive organic growth since being launched in South Africa in 2004 has resulted in an extensive footprint in 23 countries across Africa, Europe, North America, Asia Pacific, and the Middle East. With an active subscriber base in excess of 1.5 million, the Group ranks among the largest of its peer companies globally. Cartrack is a vertically integrated service-centric organisation owning all its unique intellectual property and business processes, ranging from in-house design, device and software development, mobile-technical workshops and sales. Hence, Cartrack is in full control of delivering superior service while also protecting its industry-leading margins and clean balance sheet.
- Revenue
- $355.6M
- Year founded
- 2004
- Team size
- 3.2K
10
Fractal Analytics
New York, New York, United States
Fractal is a globally recognized Enterprise AI company with a vision to power every human decision in the enterprise. Fractal’s suite of businesses includes Asper.ai (enabling interconnected decisions for revenue growth) and Analytics Vidhya (one of the world’s largest data science communities). Fractal incubated Qure.ai, a global healthcare AI leader enhancing the rapid identification and management of tuberculosis, lung cancer, and stroke. Fractal’s dedicated AI research team is focused on foundational AI advancements, including knowledge-based foundational models, reasoning-based systems, and agentic systems. The team has launched successful products such as MarshallGoldsmith.ai, Vaidya.ai, Kalaido.ai, and the open-source reasoning model Fathom-R1-14B. Fractal currently has 5500+ employees across 18 global locations including The United States, Canada, UK, Netherlands, Ukraine, India, Singapore, South Africa, UAE, and Australia. Named Leader by Forrester Forrester Wave: Customer analytics service Q2 2025 Named Leader by Everest Group Everest Group Peak Matrix Assessment 2025 for AI and Analytics Services
- Revenue
- $330M
- Year founded
- 2000
- Funding
- $105.7M
- Team size
- 6.7K
- Growth
- 25.48%
Frequently asked questions about AI & Machine Learning Operationalization (MLOps) Software Companies $100M+ Revenue
How many AI & machine learning operationalization (MLOps) software companies with $100M+ in annual revenue are there?
Latka tracks 63 AI & machine learning operationalization (MLOps) software companies with $100M+ in annual revenue. Together they generate $20.3B in annual revenue and employ 80.2K people.
Which AI & machine learning operationalization (MLOps) software company with $100M+ in annual revenue 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 with $100M+ in annual revenue make?
The average AI & machine learning operationalization (MLOps) software company in this list makes $321.7M a year, across 63 companies with reported revenue. They serve 10M customers combined.
Who are the leading AI & Machine Learning Operationalization (MLOps) software vendors with $100M+ in annual revenue?
Ranked by annual revenue, the leaders are Databricks, MegazoneCloud, Metropolis, TELUS Digital AI Data Solutions and Simulation AI.
How much funding have AI & machine learning operationalization (MLOps) software companies with $100M+ in annual revenue raised?
The 63 AI & machine learning operationalization (MLOps) software companies with $100M+ in annual revenue tracked here have raised $23.6B in disclosed funding between them.
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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.