Model operations refer to the processes for handling AI models after development. ModelOps helps businesses maintain the reliability of models after deployment and facilitates oversight as models are used in production. According to Polaris Market Research, the ModelOps market was valued at USD 5.49 billion in 2025, is estimated at USD 7.68 billion in 2026, and is projected to reach USD 123.92 billion by 2034, exhibiting a CAGR of 41.39% from 2026 to 2034. The report attributes growth to rising adoption of AI and machine learning and to increasing AI governance and compliance requirements.

How ModelOps Works

The report outlines a sequence that begins with planning the steps needed to move models from development into business practice. Models are validated against performance criteria before production, released in the appropriate context, and monitored in use to detect performance changes. Model lifecycle management applies when models need to be retrained or replaced, and governance and retirement keep documentation and control for the duration of a model's operation.

ModelOps and MLOps Compared

Polaris states that MLOps and ModelOps are related but not identical. MLOps is more specific to the productionization of machine learning models, while ModelOps is broader and addresses more types of models with a focus on using them inside an organization. The report's comparison notes that ModelOps covers all decision models, including ML, rules, graphs, agents, and linguistic models, with extensive governance depth built for enterprise risk and compliance. The primary buyers are risk, compliance, and platform teams, compared with data science and ML engineering teams for MLOps.

AI Governance and Compliance Requirements

AI use across industries makes it imperative for organizations to have better governance over deployed models. An enterprise AI governance framework offers a systematic way to meet this need, and regulatory mandates stress accountability, documentation, risk assessment, and monitoring. In April 2026, India's Ministry of Electronics and Information Technology formed the AI Governance and Economic Group to oversee national policy implementation related to AI governance.

Explainable AI is also relevant when business teams need to explain model outputs, and ModelOps keeps that information tied to the particular model and its operational process. The report also notes that governance of models created by multiple teams no longer needs to be handled project by project. It mentions the NIST AI Risk Management Framework for AI risk management and the EU AI Act for organizations operating in the EU region. In financial institutions, guidelines such as SR 11-7 already govern model development, validation, governance, and controls, and ModelOps can incorporate them into the model lifecycle. The governance, risk, and compliance application segment is projected to grow at a CAGR of 45.16%, as platforms bring governance closer to everyday model operations through approval workflows and policy checks.

𝐁𝐫𝐨𝐰𝐬𝐞 𝐌𝐨𝐫𝐞 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬:

https://www.polarismarketresearch.com/industry-analysis/modelops-market 

Platforms, Deployment, and Release Practices

By offering, the platforms segment held the largest share at 67.46% in 2025. Increased model complexity has created the need for robust ModelOps platforms, which provide an integrated way to track models that are created, deployed, or modified and improve coordination between data science, IT, and business teams. They also help with documentation of model versioning and operational changes. The services segment is projected to grow at a CAGR of 43.16%, as organizations seek external help implementing ModelOps procedures.

By application, continuous integration/continuous deployment led with a 28.63% share in 2025. These practices manage the flow of changes through a structured development and release cycle for AI models, allow teams to test changes before release, and use automated testing and version control. By deployment mode, cloud led with a 61.72% share in 2025, while on-premises is projected to grow at a CAGR of 43.28% because it gives organizations more control over the platform. By model type, ML models dominated with a 36.84% share, while graph-based models are projected to grow at 44.61%.

IT Services and Regional Demand

The expansion of global IT and ITeS services is a driver, as providers help enterprises deploy AI applications into production. In March 2026, TCS introduced its Rapid Outcome AI platform with NVIDIA to help enterprises accelerate AI deployment. North America led the market with a 37.83% share in 2025. Asia Pacific is projected to grow fastest, at a CAGR of 45.37%, and Europe held a 26.47% share.

Key Players and Recent Developments

Key players include Arthur AI, AWS, Comet ML, DataRobot, Evidently AI, Google Cloud, H2O.ai, Microsoft Azure, ModelOp, Oracle, Teradata, and Weights & Biases. In September 2026, ModelOp partnered with Carahsoft to make its AI Delivery and Governance Platform available to public-sector organizations through government contract vehicles. In July 2026, DataRobot expanded its AI governance offering to on-premises, edge, air-gapped, and sovereign environments.

Conclusion

Governance, lifecycle control, and release discipline are the core of the ModelOps market. With USD 5.49 billion in 2025 and a projected USD 123.92 billion by 2034 at a 41.39% CAGR, vendors that help enterprises manage models responsibly are well positioned. For full segment, regional, and competitive coverage, readers can consult the Polaris Market Research report.

More Trending Latest Reports By Polaris Market Research:

Valve Positioner Market

Glass Interposers Market

Maritime Patrol Aircraft Market

Rare Earth Metals Market

Quantum Photonics Market

Sunglasses Market

Sulfuric Acid Market

Clear Aligners Market

Feed Mycotoxin Detoxifiers Market