Top 3,992 AI & Machine Learning Operationalization (MLOps) Software SaaS Companies in May 2026
As of May 2026, there are 3,992 SaaS companies in AI & Machine Learning Operationalization (MLOps) Software. They have combined revenues of $45.7B and employ 280.3K people. They have raised $41.7B 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.
Clay is a New York-based AI-powered sales intelligence and data enrichment platform designed to help growth and revenue operations (RevOps) teams automate personalized outreach and build comprehensive customer profiles. Founded in 2017 by Kareem Amin and Nicolae Rusan, the company has raised $102 million in funding, achieving a valuation of $1.3 billion as of early 2025 .
SambaNova is the leading Enterprise AI company that delivers a full-stack infrastructure from silicon to software, specializing in machine learning and big data analytics platforms.
Degreed is an AI-driven learning platform that helps identify skill gaps, provides personalized learning, and automates learning experiences for upskilling and certification.
Provider of a Saas recruitment management system designed to make the organizational recruitment process more effective. The company's platform uses artificial intelligence enabled screening program that automatically filters candidates and makes recommendations for companies, enabling recruiters to post job listings across multiple platforms with one click, saving them from the hassle of hopping between portals and achieve better recruitment results by employing suitable candidates.
DevRev is designed for AI to work alongside people by converging business infrastructure, which we call AgentOS. AgentOS unlocks the power of AI Agents,
We can replace legacy systems with an interconnected platform to automate labor-intensive tasks, and enhance the customer experience with self-service and preemptive engagement. Despite its breadth, AgentOS begins to deliver value in hours rather than months through our powerful data replication, sync capabilities, and analytics AI agents.
Patsnap Eureka uses AI that goes beyond simple keyword matching to deliver search results.
Validate your ideas and identify potential roadblocks before you invest time and resources.
Regardless of your expertise, our collaborative tool makes it easy for teams to work together seamlessly.
VusionGroup is the global leader in the digitalization solutions for commerce, serving over 350 large retailer groups around the world in Europe, Asia and North America. The Group develops technologies that create a positive impact on society by enabling sustainable and human-centered commerce.
technology company specializing in software solutions
Revenue
$97M
Customers
-
Year founded
2017
Funding
$209.7M
Team size
443
Growth
-
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.
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