The global ModelOps Market is projected to expand from USD 10.7 billion in 2026 to USD 339.4 billion by 2036, advancing at a 41.3% CAGR during the forecast period, according to Fact.MR. The market was valued at USD 7.6 billion in 2025.

The market is expected to create an absolute opportunity of USD 328.7 billion through 2036. Rising production AI inventories, stronger lifecycle governance requirements, and the spread of hybrid and multi-cloud deployments are increasing demand for systems that can track AI assets beyond individual development platforms.

ModelOps platforms help enterprise teams maintain a common view of predictive models, generative systems, and AI agents across development and production environments. They also connect approval records, policy controls, monitoring data, and ownership information throughout the AI lifecycle.

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Enterprise AI Portfolios Create Demand for Common Control Layers

AI platform teams increasingly need a shared inventory because models and agents can reach production through different development and deployment pipelines. Without a common control record, ownership, approval status, monitoring results, and operational changes can become difficult to reconcile.

Model risk teams also require traceable evidence that follows an AI asset from validation through deployment and monitoring. This is particularly relevant in regulated industries where approval conditions and remediation records must remain available after production release.

Distributed AI estates add another layer of complexity. Runtime drift, performance changes, agent behavior, and infrastructure costs can span multiple cloud and internal environments. ModelOps systems address this by connecting lifecycle records with ongoing operational monitoring.

Shambhu Nath Jha, Senior Analyst at Fact.MR, states, “ModelOps is becoming the control record for AI estates that no single development platform owns. Adoption is expected to favor systems that inventory every AI asset and connect approval evidence to runtime behavior. Providers should combine open integration and policy automation with clear operating cost visibility throughout production AI portfolios.”

Software and Cloud Lead the Market Structure

Software is projected to account for 34.5% of the ModelOps Market in 2026. Demand is supported by the need for shared AI inventories, automated policy controls, and lifecycle evidence across diverse production assets.

Services remain important where enterprises require implementation support, operating-model design, and integration with existing technology environments. API Tools support programmatic lifecycle operations, while API Connectors link ModelOps records with cloud platforms and other enterprise systems.

Cloud deployment is forecast to hold 41.8% share in 2026. Centralized access and elastic infrastructure make cloud environments suitable for distributed AI teams operating across multiple locations and workloads.

On-premise deployments continue to serve organizations with strict data and network controls. Hybrid environments provide a route for enterprises that need cloud-based development and monitoring while retaining sensitive workloads internally.

Eurostat reported in February 2026 that 53% of EU enterprises used paid cloud services during 2025, indicating an established cloud infrastructure base into which ModelOps platforms can integrate.

SMEs are expected to account for 47.8% share in 2026. Managed deployment options and lower infrastructure barriers are making production monitoring more accessible to smaller organizations. Australia’s government reported in June 2025 that 41% of small and medium enterprises were adopting AI, creating a broader potential user base for repeatable AI deployment and monitoring practices.

Workflow Automation represents 31% share in 2026, supported by agent-based workflows that combine AI models with external tools. BFSI leads end-use demand with a projected 30.4% share, reflecting formal model-risk programs and evidence requirements surrounding financial decision-making.

Production AI and Governance Requirements Support Expansion

Fact.MR identifies rising production AI inventories as a key market driver. Enterprises increasingly operate predictive models alongside generative AI systems and agents, creating demand for a common inventory and consistent operating records.

Regulatory evidence and lifecycle control requirements are also supporting adoption, particularly across Europe and North America. Hybrid and multi-cloud deployment adds another demand factor because organizations need comparable governance controls across different operating environments.

Agentic workflows provide a longer-term growth opportunity. As AI agents interact with external tools and perform multi-step tasks, enterprises require monitoring and policy controls that extend beyond conventional model endpoints.

Shared compute access is also widening opportunities in markets such as India and Australia. Government-supported infrastructure can allow smaller teams to develop AI applications without maintaining dedicated computing resources, increasing the potential need for managed ModelOps capabilities as projects move toward production.

At the same time, integration debt remains a barrier. ModelOps platforms must connect development tools, registries, runtime environments, service-management systems, and governance workflows that may use different metadata structures.

Skills shortages add further friction. A UK government employer survey published in January 2026 found that 25% of employers said a lack of AI specialists affected business goals. Enterprises may therefore prioritize platforms that reduce the operational effort required to establish monitoring and governance processes.

India and China Record the Fastest Country-Level Growth

India is projected to register a 43.1% CAGR from 2026 to 2036. Shared compute access and expanding enterprise AI delivery are key factors supporting the outlook. The Press Information Bureau reported in February 2026 that the IndiaAI Compute Portal provided access to more than 38,000 GPUs.

China follows with a projected 42.4% CAGR, supported by industrial AI deployment and expanding application programs. The country's State Council reported in March 2026 that China had more than 6,200 AI companies during 2025.

Australia is forecast to grow at 41.1% CAGR, while the United Kingdom is projected to advance at 40.8% CAGR. The United States is expected to record a 40.6% CAGR through 2036.

Other country-level forecasts include:

• Germany: 40.3% CAGR
• Japan: 40.0% CAGR

Competitive Landscape Centers on Enterprise AI Control

The competitive environment includes ModelOp, IBM, SAS, DataRobot, Domino Data Lab, H2O.ai, and Fiddler AI.

ModelOp focuses on enterprise AI inventory and governance across machine learning models, generative applications, and agent workflows. IBM combines AI lifecycle governance with hybrid enterprise operations capabilities.

DataRobot has expanded governance coverage across cloud, on-premise, edge, isolated, and sovereign environments. Domino Data Lab combines enterprise AI delivery with lifecycle controls, while H2O.ai and Fiddler AI address monitoring, evaluation, and runtime governance requirements.

SAS introduced AI Navigator in April 2026 to centralize visibility across models, agents, and enterprise AI use cases. Competition is increasingly centered on cross-platform inventory, evidence automation, integration depth, and the ability to connect policy controls with production behavior.

Read the full Fact.MR ModelOps Market analysis and forecast: https://www.factmr.com/report/modelops-market

Report Scope

The ModelOps Market covers software and services used to manage AI inventory, deployment controls, monitoring, and governance throughout production lifecycles. The analysis includes Software, Services, API Tools, and API Connectors across Cloud, On-premise, and Hybrid deployments.

The report examines SMEs, Large Enterprises, and Public Sector Buyers across Workflow Automation, Analytics, Governance, and Integration applications. End-use analysis covers BFSI, Retail, Manufacturing, IT, and Government across North America, Europe, East Asia, South Asia & Pacific, and the Middle East & Africa.

The study uses a hybrid top-down and bottom-up approach incorporating production AI project counts, software adoption, deployment mix, enterprise spending, governance intensity, connector requirements, managed service penetration, provider checks, and enterprise interviews. The analysis draws on 140+ sources, 45+ company portfolios, 30+ countries, and 24+ interviews.

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About Fact.MR

Fact.MR is a global market research and consulting firm providing market intelligence, syndicated research, custom studies, and strategic analysis across technology, industrial, healthcare, chemicals, food, and other major business sectors. Its research combines primary interviews, secondary research, market modelling, and company-level analysis to support business planning and investment decisions.