ModelOps Market Size, Share & Trends Analysis Report By Offering (Platforms, Services), By Application (Continuous Integration/ Continuous Deployment, Batch Scoring, Governance, Risk and Compliance, Parallelization & Distributed Computing, Monitoring & Alerting, Dashboard & Reporting, Model Lifecycle Management, Others), By Model (ML Models, Graph-based Models, Rule & Heuristic Models, Linguistic Models, Agent-based Models, Others), By Vertical (BFSI, Retail & E-commerce, Healthcare & Life sciences, IT & Telecommunications, Energy & Utilities, Manufacturing, Transportation & Logistics, Others) and By Region(North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2025-2033

ModelOps Market Size

The global modelOps market size was worth USD 5.2 billion in 2024 and is estimated to reach an expected value of USD 7.24 billion in 2025 to USD 102.04 billion by 2033, growing at a CAGR of 39.2% during the forecast period (2025-2033).

ModelOps (Model Operations) is the practice of managing and operationalizing machine learning (ML) and artificial intelligence (AI) models throughout their entire lifecycle, from development and deployment to monitoring and continuous improvement. ModelOps focuses on the governance, automation, and scalability of AI/ML models in production environments, ensuring they deliver consistent business value while remaining compliant with regulations and business objectives. ModelOps helps organizations manage large-scale AI deployments efficiently, ensuring models remain accurate, reliable, and relevant over time.

The global modelOps industry, focusing on operationalizing and managing machine learning (ML) and artificial intelligence (AI) models, is witnessing a boom as business houses in finance, healthcare, and retail look toward AI/ML solutions. There is a need for robust ModelOps frameworks to enable efficient deployment, continuous monitoring, and maintenance of models while increasing demand for automation in ML pipelines, model governance, and real-time model monitoring.

Additionally, as organizations transition to hybrid or multi-cloud environments, ModelOps solutions become indispensable for smooth deployment and integration across various platforms. With the market projected to grow rapidly, driven by the increasing use of AI across sectors, the ModelOps landscape presents opportunities to improve model performance, scalability, and compliance.

The below depicts the varying levels of digital transformation adoption across different industries, with Telecom & Communication service providers leading at 65% and the Public Sector at the lowest with 34%. It highlights that industries such as Banking, Oil and Gas, and Retail are also significantly engaged in digital transformation, while sectors like Public Sector and Travel show relatively lower engagement.

Source: eLuminous Technologies, Straits Research

Latest Market Trends

Growing integration of AI

Artificial intelligence is quickly becoming an essential technology to enhance the energy efficiency, optimize grid management, and aid in the shift to cleaner and renewable energy sources. Industries and governments focused on achieving sustainability goals while fighting climate change find innovative AI models to improve energy systems, cut waste, and monitor and manage energy resources more efficiently.

• For instance, In April 2024, The U.S. Department of Energy (DOE) announced actions supporting the Executive Order on AI’s safe, secure development. DOE released two key reports: AI and Energy: Opportunities for a Modern Grid and Advanced Research Directions in AI For Energy, highlighting AI’s potential in clean energy.

Rising demand for energy-efficient cloud solution

Increasing demand for energy-efficient cloud services is one factor in the ModelOps market due to rapid development in digital technologies. As more business operations start using cloud computing, organizations need solutions that deliver scalability and flexibility and reduce energy consumption. The integration of AI and Edge Computing has significantly contributed to responding to this call. AI-powered algorithms enable far more intelligent management of resources with dynamic scaling of resources across multiple cloud infrastructures.

With AI and Edge Computing, AI-driven algorithms provide intelligent resource management, predictive analytics, and adaptive workloads that optimize energy usage and dynamic scaling across cloud infrastructures.

Global ModelOps Market Growth Factors

Growing adoption of AI by small businesses

Small businesses' growing adoption of Artificial Intelligence is a driving growth driver for the global ModelOps market. As AI technologies become increasingly adopted by small businesses, they unlock enormous benefits that improve performance across different facets of business operations, including marketing, customer communication, and decision-making processes. Indeed, AI allows these businesses to streamline operations, improve the customer experience, and increase overall efficiency, which later translates into business growth.

• For instance, according to the U.S. Chamber of Commerce, nearly one in four small businesses have implemented Artificial Intelligence, which has increased performance in marketing and communications. AI users are 12 points more likely to experience profit growth than non-users.

Increasing dependence on technology platforms

Widespread adoption of technology platforms is a prominent growth driver for the ModelOps market. Smaller businesses today depend more heavily on advanced technologies to improve their operations, making AI-powered solution requirements, especially ModelOps, surge. Technology platforms allow small businesses to streamline various processes, improve efficiency, and achieve higher sales, profitability, and workforce expansion. Technology, such as cloud services, AI tools, and automation systems, has allowed small businesses to scale more effectively and compete in a fast-evolving digital landscape.

• For instance, according to the U.S. Chamber of Commerce, 95% of the small businesses in the United States use at least one technology platform. The report further emphasizes that high-tech usage is significantly related to the concerned businesses' sales growth, employment, and profit growth.

Market Restraint

Data quality and accuracy

Data quality and accuracy are the major factors in deploying and achieving operational success with AI models in the ModelOps market. AI models are highly dependent on large volumes of data for training and prediction purposes. However, if the underlying data is not accurate, incomplete, or inconsistent, the resulting AI model outputs can be grossly erroneous. This may affect decision-making, lead to inefficiencies in operations, and compromise an organization's ability to trust the insights generated by its AI systems.

• For instance, according to SDXCentral, LLC, enterprises in the United States lose hundreds of millions of dollars due to AI data-quality issues. Data inaccuracies and hallucinations were reported by 50% of enterprises using LLMs, resulting in poor decisions, damaged trust, and wasted employee time.

Market Opportunity

Expansion of AI model usage across industries

The widespread adoption of AI across various industries presents a significant growth opportunity for the global ModelOps market. As artificial intelligence evolves, its applications are no longer confined to traditional finance, healthcare, and retail sectors. AI is rapidly penetrating industries like manufacturing, logistics, telecommunications, energy, and government services, enabling businesses to optimize operations, automate processes, and drive innovation. This growing demand for AI models across diverse sectors is boosting the need for efficient model management and operationalization, positioning ModelOps as a critical component in AI deployment.

• For instance, the United States remains a global leader in AI innovation. According to the Brookings Institution, U.S. investment in AI accounts for over 40% of global spending, transforming industries rapidly—from financial services and healthcare to logistics and advanced manufacturing.

Broader adoption of AI in emerging markets

Emerging markets are witnessing significant growth in AI adoption due to increased digital transformation initiatives. Countries in Asia-Pacific, Latin America, and the Middle East heavily invest in AI technologies, creating a robust demand for ModelOps solutions to ensure scalable and efficient model deployment.

China and India are leading the way in AI research and implementation in smart cities, e-commerce, and healthcare diagnostics. These markets offer a massive opportunity for ModelOps vendors as organizations seek platforms to handle the complexity of managing AI models at scale.

Regional Insights

North America: Dominant region with a significant market share

North America is the dominant region in the global ModelOps market because of its advanced technological landscape, high adoption of AI and machine learning technologies, and a vast array of leading tech companies. The U.S. is critical in this regard, as businesses adopt AI-driven solutions across many finance and healthcare applications. Leading ModelOps platform providers are present in North America, which has supported the growth in this region.

• For instance,more than 50% of US companies with more than 5,000 employees currently use AI. The number increases to 60% for companies with more than 10,000 employees. In general, large-scale organizations dominate the majority of companies using AI. Today, 42% of enterprise businesses with over 1,000 employees use AI.

Asia Pacific: Rapidly growing region

Asia Pacific is the rapidly growing region in the global ModelOps market due to the increasing pace of digital transformation across countries such as China, India, Japan, and South Korea. Each of these nations is heavily investing in AI and cloud technologies. Industries like health care, finance, and manufacturing are increasingly adapting AI models to implement automation and make data-driven decisions. The region is also experiencing increased growth in AI research and development efforts, where governments promote innovation through support for economic growth and productivity growth.

• For instance, the Asian Development Bank is optimistic, saying that surging global investments in artificial intelligence and easing inflation will push sustained economic growth to a higher trajectory through 2024 and 2025.

Countries Insights

• United States: The U.S. is at the forefront in terms of the adoption of AI and ModelOps solutions, primarily because of a robust presence of technology companies, substantial investments in AI research, and high demand in industries such as healthcare, finance, and retail. Early adoption and high scalability are needed for AI models to support the market.
• China: China is fast accelerating its AI and ModelOps investments, and these are also facilitated by the "AI 2.0" policy initiated by the government. The manufacturing, healthcare, and finance sectors are all highly dependent on AI models in this country, hence the growing importance of ModelOps platforms.
• United Kingdom: The UK primarily uses AI in healthcare, finance, and retail sectors and has supported ModelOps solutions to manage AI lifecycle operations. Models like the "AI Strategy" are promoted by the government to enhance the development of economic growth through AI models.
• Germany: Germany, the largest EU economy, heavily invests in AI. The sectors involved are manufacturing, automotive, and engineering. The country utilizes ModelOps to manage the deployment, monitoring, and optimization of AI models in production environments.
• India: India is rapidly adapting to AI and incorporating it to shape its business and technology horizons. It mainly focuses on ModelOps solution adaptation in BFSI, primarily optimizing financial models, fraud detection, and customer insights.
• Japan: Japan adopts AI for its manufacturing, robotics, and automobile sectors. This need for ModelOps platforms rises since companies incorporate AI models for more streamlined lines, automated process creation, and greater precision on their production lines.
• Canada: Canada has emerged as a hub for AI research and innovation, especially in healthcare, finance, and energy. Canadian enterprises are increasingly deploying AI models for automation and analytics, pushing the demand for ModelOps platforms to manage model lifecycles.

Segmentation Analysis

By Offerings

The platform segment dominates the market due to its capacity to offer end-to-end solutions for managing the whole lifecycle of AI models; the platform segment is driving the ModelOps market. It enables businesses to deploy, monitor, and optimize models in real time with dimensions of scalability and flexibility. With AI and machine learning gaining ground across industries, platform solutions represent comprehensive tools for streamlining model deployment and management.

By Application

The Continuous Integration/ Continuous Deployment segment dominates the market, as it plays a central role in automating AI model deployment and integration processes. As businesses strive to reduce time-to-market and maintain model reliability, CI/CD provides an automated pipeline for seamless updates and deployment. This application is crucial for organizations looking to rapidly test, deploy, and iterate on models with high availability and performance in production environments.

By Model

The ML Models segment dominates the market due to their broad applicability in industries, including predictive analytics, automation, and decision-making. Their flexibility also renders them for application in classification, clustering, and regression functions. With the growing adoption of AI and ML in businesses for data-driven insights, the need for these models is on the rise.

By Vertical

The BFSI segment dominates the market as the sector relies heavily on AI and machine learning to enhance operational efficiency, manage risks, improve customer experience, and detect fraud. Machine learning models are valuable in automating operations such as credit scoring, fraud detection, and financial forecasting, which are demanding and require immense accuracy and data-driven decision-making.

Company Market Share

Key market players are investing in advanced ModelOps technologies and pursuing strategies such as collaborations, acquisitions, and partnerships to enhance their products and expand their market presence.

Veritone, Inc: An Emerging Player in the ModelOps Market

Veritone, Inc. is an emerging player in the ModelOps market because it offers human-centric AI solutions designed to help the organization effectively manage and optimize AI models at scale. Veritone provides advanced AI model development and deployment tools combined with monitoring functionalities that enable a seamless integration of AI into its operations, maximizing efficiency and achieving better business outcomes. The company's AI platform is highly flexible. It supports numerous verticals such as media, entertainment, sports, and talent acquisition, making it an attractive choice for organizations looking to leverage AI at an enterprise scale.

Recent Developments:

• In Aug 2024, Veritone, Inc. announced that it entered a three-year Strategic Collaboration Agreement with Amazon Web Services (AWS). This agreement will help accelerate cloud-native and generative AI innovation to benefit customers in the media, entertainment, sports, talent acquisition, and public sectors.

Recent Developments

• May 2024- Microsoft is excited to present GPT-4o as the newest flag model for OpenAI on the Azure AI system. It holds text, visual, and even audio capabilities, a new benchmark to generative AI and conversational AI now, available in preview on Azure OpenAI Service.

Analyst Opinion

As per our analyst, the global ModelOps market is growing rapidly and plays a pivotal role in the broader AI ecosystem. As businesses increasingly rely on AI for competitive advantage, managing AI models efficiently, at scale, and with automation is becoming a top priority. ModelOps platforms address this need by streamlining the entire model lifecycle—from deployment and monitoring to optimization and retirement—ensuring that AI models remain aligned with business objectives and continue delivering value.

Moreover, the market is poised for exponential growth as organizations across sectors look for solutions that simplify AI model management, reduce operational complexities, and improve time-to-market for AI innovations. As AI adoption accelerates, the need for end-to-end model lifecycle management will drive further expansion of the global ModelOps market.