As of July 2026, there are 3,995 SaaS companies in AI & Machine Learning Operationalization (MLOps) Software. They have combined revenues of $48.1B and employ 272K people. They have raised $51.2B and serve 3.1B customers combined.
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.
Sorting: Highest -> Lowest
Showing 10 of 1,432 companies ranked by annual revenue.
Sao Paulo, Sao Paulo, Brazil
Developer of an analytical platform intended for the corporate legal department. The company's platform uses artificial intelligence to detect the trends of the judiciary in-depth, enabling clients to reduce costs, risks and increasing the level of predictability of legal liabilities.
San Francisco, CA, United States
Powerful, flexible, and data-driven, Gyaan AI makes it easy to consistently execute deal-winning plays to drive growth and boost your pipeline. GTM teams that use Gyaan drive adoption of their sales process and win bigger deals. It takes a team to win a champion. So get out there and play hard. See us in action at gyaan.ai.
San Francisco, California, United States
Alan de Souza built Reachbox.ai, an AI-powered cold email outreach platform offering email warmups, campaign automation, and lead enrichment. He scaled from zero revenue to $6 million ARR in just 18 months, serving 4,500 paying customers at $110 average revenue per user while growing 25% month-over-month with only 7.5% churn rate. His 27-person team generates $230,000 in revenue per employee, with 280 active affiliates driving 35% of all customers through a 20% lifetime commission program that pays out over $50,000 monthly. In this episode, Alan reveals how he added $5 million ARR in just 6 months using systematic customer feedback from 1,500+ conversations, why he still does 10 customer calls every day, and the exact affiliate strategy that turned his customers into a revenue-generating army earning lifetime commissions.
Washington, District of Columbia, United States
Vityl’s platform brings together culture coaching, recognition, and performance management features to promote an environment where all people belong and thrive. Vityl includes AI features that make advancing culture faster and better than ever.
Toronto, Ontario, Canada
Younet is a cutting-edge platform powered by AI that provides a platform to create personalized models on your data knowledge, communicate effectively, and deploy them wherever necessary. It's ready to assist in various fields such as Sales, Marketing, HR,etc.
Sydney, New South Wales, Australia
AI that builds fully-configured automated business processes
- 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.
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