MLOps Market (2025 - 2030) Size, Share & Trends Analysis Report By Component (Platform, Service), By Deployment (Cloud, On-premises), By Organization Size, By Vertical (BFSI, Retail & E-commerce), By Region, And Segment Forecasts

MLOps Market Summary

The global MLOps market size was estimated at USD 2,191.8 million in 2024 and is projected to reach USD 16,613.4 million by 2030, growing at a CAGR of 40.5% from 2025 to 2030. The MLOps market has seen significant growth in the past few years and is expected to demonstrate further substantial growth due to the outbreak of the COVID-19 pandemic.

The surge in digital and internet penetration worldwide positively impacts the market's growth. Furthermore, the healthcare industry increasingly uses AI and ML to improve patient outcomes and streamline processes. AI-powered tools are used for image analysis, predictive modeling, and personalized treatment recommendations. MLOps solutions also help healthcare organizations build and deploy these tools at scale while ensuring compliance with data privacy regulations. Such increasing usage in the healthcare sector is expected to provide lucrative growth opportunities for the market during the forecast period. An increase in the adoption of MLOps technology across industries to enhance productivity & operation also strengthens the market's growth for the future. Moreover, MLOps help to lower costs over the entire ML lifecycle and produce numerous possibilities for market growth in the upcoming years.

Component Insights

The platform segment led the market in 2024, accounting for the highest share of the global revenue owing to its feature of optimizing the management and operation of ML models. It helps organizations build, manage, train, and deploy models in a machine learning environment. It accelerates business experiments with purpose-built tools, including data preparation, classification, monitoring, training & tuning, and other activities. For instance, in October 2022, Weights & Biases, a provider of a developer-first MLOps platform, revealed significant enhancements to its developer-first MLOps platform. The latest enhancements will boost ML activities for practitioners and enterprises, delivering an end-to-end, seamless MLOps experience.

The service segment is estimated to grow significantly over the forecast period. Services in the MLOps market study include MLOps development, consulting, monitoring services, and license & maintenance services. MLOps services help organizations drive growth by enabling them to develop, deploy, and manage machine learning models efficiently. By leveraging these services, organizations can accelerate time-to-market, improve model accuracy, enhance operational efficiency, expand use cases, and facilitate collaboration. For instance, in December 2021, NxtGen Datacenter & Cloud Technologies Private Limited., a data center and cloud services provider, in partnership with Katonic Pty Ltd., an AI startup, launched MLOps as a Service offering to strengthen, simplify and accelerate mainstream adoption of AI by digital native startups, enterprises, educational institutions, research institutes, and government entities.

Deployment Insights

The On-premises segment held the largest revenue share in 2024. This is attributed to the numerous benefits offered by the On-premises deployment, such as high data safety and security. Enterprises prefer the On-premises model, as keeping data and models within a company's data center ensures they are secure and protected from external threats. In addition, with On-premises infrastructure, enterprises have greater control over their machine-learning pipeline, which can improve efficiency and reduce costs. For instance, in June 2022, Domino Data Lab, Inc. announced the launch of its nexus hybrid enterprise MLOps architecture. The architecture will let enterprises rapidly control, scale, and stage data science work across various compute clusters, On-premises, in different regions, and across clouds.

The cloud segment is predicted to foresee significant growth in the forecast period. Disaster management via cloud-based backup systems, automatic software upgrades, enhanced efficiency, and flexibility are key benefits of increasing the implementation of cloud-based delivery models for deep-learning software services and solutions. By leveraging cloud infrastructure, organizations can avoid the upfront costs of building and maintaining their data centers. It provides flexibility to experiment with different machine-learning models and algorithms without investing in expensive hardware or software.

Organization Size Insights

The large enterprises segment led the market in 2024, accounting for significant share of the global revenue owing to the growing implementation of Al technology and data science across large enterprises to present quantitative insights into their operations. Large enterprises can benefit from MLOps by improving the scalability and reliability of their machine learning models, reducing the time and resources required to deploy and manage these models, and enabling faster decision-making and innovation. Furthermore, MLOps can help large enterprises scale their ML models by automating tasks like model training, testing, and deployment. This can help organizations handle large volumes of data and rapidly changing business requirements.

The SMEs segment is estimated to grow significantly over the forecast period. SMEs are increasingly recognizing the value of adopting MLOps to drive growth and gain a competitive edge in their respective industries. The adoption of MLOps can further enhance the growth potential of SMEs as it enables them to leverage the power of machine learning to gain insights into customer behavior, optimize marketing campaigns, improve product development, and enhance operational efficiency. With the help of MLOps, SMEs can access powerful predictive models that can drive growth and help them stay competitive in the marketplace. For instance, in June 2022, OmniML, a startup artificial intelligence (AI) company, announced the launch of Omnimizer. The new platform simplifies and boosts machine learning operations (MLOps) by bridging the gap between edge hardware and ML models.

Vertical Insights

The BFSI segment held the largest revenue share in 2024. The adoption of machine learning by the financial sector has been on the rise, primarily for solving specific challenges such as yield management, predictive maintenance of legacy systems, and fraud detection. Financial institutions can automate integrating ML/AI models into applications using MLOps without a flaw at digital channels or touchpoints where clients interact with banks to enhance their overall experience. Furthermore, it allows financial institutions to train the models with their statistics and information. As a result, they are no longer required to outsource their stored figures and facts to third-party sellers, who cannot frequently deliver enterprise-grade data on a big scale.

The government segment is predicted to foresee significant growth in the forecast period. Governments worldwide are increasingly adopting MLOps to streamline AI deployment in public services, defense, and healthcare. Investments are focused on building scalable, secure, and explainable AI infrastructures. Agencies prioritize compliance, model governance, and ethical AI, driving demand for robust MLOps frameworks. Public-private collaborations are also rising to accelerate innovation and skill development in this domain.

Regional Insights

North America dominated the market in 2024, accounting for over 40.8% share of the global revenue owing to Al's intense research and development competencies in the developed economies, research institutes, and various leading Al companies based in this region. The increasing investment in advanced technologies to enhance customer experience and business operations is anticipated to provide lucrative growth opportunities in North America. Furthermore, the region has had strong R&D capabilities in Al and invested heavily in Al-related technologies over the past few years. Also, they have enforced policies to support the development of the field.

U.S. MLOps Market Trends

MLOps market in U.S. is significantly growing as adoption in the U.S. is rapidly increasing as enterprises scale AI initiatives from experimentation to production. There's strong demand for tools that automate model deployment, monitoring, and governance, particularly in regulated sectors such as finance and healthcare. Cloud providers and startups are expanding their MLOps offerings to meet enterprise needs. This growth reflects a broader shift toward operationalizing AI for real-world business impact.

Europe MLOps Market Trends

MLOps is experiencing strong momentum across Europe, driven by regulatory, technological, and industry-specific factors. The EU’s emphasis on data privacy and ethical AI—through frameworks such as GDPR and the AI Act—has accelerated demand for MLOps tools that ensure compliance and transparency in machine learning workflows. Countries such as Germany, the UK, and France are leading adoption, particularly in manufacturing, finance, and healthcare sectors. Cloud-based MLOps platforms are gaining traction as enterprises seek scalable, automated solutions for model deployment and monitoring. However, challenges such as talent shortages and legacy IT systems are slowing adoption in some areas.

Asia Pacific MLOps Market trends

Asia Pacific is anticipated to register the fastest CAGR over the forecast period. MLOps is experiencing significant growth across the Asia Pacific region, driven by rapid digital transformation and increased adoption of AI and machine learning technologies. Countries such as China, India, Japan, and South Korea are leading this surge, investing heavily in AI infrastructure and innovation. The region's expanding cloud infrastructure and the need for scalable, automated machine learning workflows are further propelling MLOps adoption. However, challenges such as data privacy concerns and a shortage of skilled professionals may impact the pace of implementation in certain areas.

Key MLOps Market Company Insights

Prominent firms have used product launches and developments, followed by expansions, mergers and acquisitions, contracts, agreements, partnerships, and collaborations as their primary business strategy to increase their market share. The companies have used various techniques to enhance market penetration and boost their position in the competitive industry.

• IBM is a global technology leader operating in over 170 countries, dedicated to driving innovation and positive impact through responsible technology, business ethics, and environmental commitment. The company offers a comprehensive suite of AI and hybrid cloud solutions designed to modernize, predict, automate, and secure business operations. In the realm of MLOps, IBM provides end-to-end solutions that help organizations design, deploy, and manage machine learning models efficiently, ensuring seamless integration with existing applications and infrastructure. Their MLOps offerings enable businesses to increase productivity, reduce costs, and improve outcomes by automating model lifecycle management and operationalizing AI at scale. With a strong focus on sustainability and upskilling, IBM continues to empower businesses and communities to thrive in the AI-driven future.

• Amazon Web Services (AWS), launched in 2006, is a global leader in cloud technologies, empowering organizations of all sizes to innovate and transform their businesses. AWS offers a comprehensive suite of cloud infrastructure and services, enabling customers to build, deploy, and scale solutions across industries. In the field of MLOps, AWS provides robust tools and platforms-such as Amazon SageMaker-for automating and streamlining the entire machine learning lifecycle, from data preparation and model training to deployment and monitoring. This allows organizations to efficiently operationalize machine learning workflows at scale. Trusted by startups, enterprises, non-profits, and governments alike, AWS is committed to customer-centric innovation and continuous reinvention.

Recent Developments

• In July 2024, Microsoft unveiled the MLOps v2 architectural framework for Azure, delivering robust, end-to-end machine learning operations across classical ML, computer vision, and natural language processing workloads. This new framework incorporates industry’s best practices, enabling organizations to deploy, monitor, and retrain AI models with greater efficiency, security, and scalability. With modular components for data management, administration, model development, and deployment, MLOps v2 ensures repeatable, maintainable, and production-ready AI solutions. The architectures are now available with sample deployment templates, empowering enterprises to accelerate their AI journey on Azure.

• In March 2025, Hewlett Packard Enterprise (HPE) and NVIDIA have announced new enterprise AI solutions designed to accelerate the deployment of generative, agentic, and physical AI. The collaboration introduces enhanced performance, security, and power efficiency through the NVIDIA AI Computing by HPE portfolio, including turnkey private cloud AI offerings. HPE Private Cloud AI now integrates the NVIDIA AI Data Platform, enabling rapid AI development and deployment for enterprises of all sizes. New HPE AI servers powered by NVIDIA Blackwell architecture and advanced observability tools further streamline AI operations, delivering faster time-to-value and greater innovation for businesses.

MLOps Market Report Scope

Global MLOps Market Report Segmentation

This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2017 to 2030. For this study, Grand View Research has segmented the global MLOps market report based on component, deployment, organization size, vertical, and region.

• Component Outlook (Revenue, USD Billion, 2017 - 2030)

  • Platform

  • Service

• Deployment Outlook (Revenue, USD Billion, 2017 - 2030)

  • Cloud

  • On-premises

• Organization Size Outlook (Revenue, USD Billion, 2017 - 2030)

  • SMEs

  • Large Enterprises

• Vertical Outlook (Revenue, USD Billion, 2017 - 2030)

  • BFSI

  • Healthcare & Life Sciences

  • Retail & E-Commerce

  • IT & Telecom

  • Energy & Utilities

  • Government & Public Sector

  • Media & Entertainment

  • Others

• Regional Outlook (Revenue, USD Billion, 2017 - 2030)

  • North America

    • U.S.

    • Canada

  • Europe

    • Germany

    • UK

  • Asia Pacific

    • China

    • Japan

    • India

    • South Korea

    • Australia

  • Latin America

    • Brazil

    • Mexico

  • Middle East and Africa (MEA)

    • Kingdom of Saudi Arabia

    • UAE

    • South Africa