ModelOp Center
ModelOp Center is an enterprise platform for managing AI lifecycles and governance across machine learning, generative AI, agentic AI, and third-party systems.
What ModelOp Center is
ModelOp Center is an enterprise platform for managing AI lifecycles and governance across machine learning, generative AI, agentic AI, and third-party systems. It keeps a searchable inventory of AI solutions with ownership, metadata, and lifecycle details. Role-based workflows coordinate intake, risk assessment, reviews, approvals, monitoring, and retirement. Risk-based controls can enforce policies at delivery and through AI gateways handling live traffic. The platform monitors bias, drift, performance, data quality, and costs, including token use and throughput. It integrates with services such as AWS, Azure, Google Cloud, OpenAI, MLflow, ServiceNow, Jira, Databricks, Snowflake, Power BI, and Tableau. Deployment is within an organization’s environment, on-premises, in private or public cloud, or in a hybrid setup; it is not a public SaaS product. ModelOp says it references data in place without copying or transferring it. The product is paid, with pricing available on request, and has no free plan or trial.
Good
- Tracks ownership and lifecycle details in a searchable inventory.
- Automates risk reviews, approvals, monitoring, and retirement workflows.
- Monitors bias, drift, performance, data quality, and costs.
- Supports on-premises, cloud, and hybrid deployment.
Watch out
- No free plan or trial.
- Pricing is available on request.
- Not offered as a public SaaS solution.
Who it suits
ModelOp Center is intended for enterprise AI, technology, governance, risk, legal, and compliance teams. It suits organizations seeking lifecycle oversight and policy controls for AI systems in their own environments.
Verdict
ModelOp Center combines AI inventory, governance workflows, policy enforcement, and monitoring for enterprise environments. Its deployment is organization-hosted, and buyers must request pricing details.
Plans and prices
From ModelOp Center's own pricing page, checked 4 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| ModelOp Center | Priced on request | Enterprise product; request a demo for details |
The facts
Each one read from ModelOp Center's own pages, with the date it was checked.
The company
- Company history
- ModelOp says it was founded in 2018 to help large enterprises operationalize different types of models and enforce auditable governance.checked 4 Oct 2026
- Headquarters
- Chicago, Illinois, United Stateschecked 28 Sep 2026
What it does
- Data handling
- The company says ModelOp references data in place without copying, extracting, or transferring it.checked 4 Oct 2026
- Deployment
- ModelOp installs in an organization's environment on-premises, in private or public cloud, or in a hybrid setup; the Command Center page says it is not a public SaaS solution.checked 4 Oct 2026
- Extensibility
- ModelOp provides a REST-compliant microservices architecture and APIs for extending and embedding it into enterprise workflows.checked 4 Oct 2026
- Frameworks
- The Command Center page lists support for policies mapped to the EU AI Act, NIST AI RMF, OCC SR 11-7, and ISO 42001.checked 4 Oct 2026
- Identity and access
- ModelOp supports enterprise identity providers using OAuth2, SAML, and other authentication standards for role-based access.checked 4 Oct 2026
- Integrations
- Listed integrations include AWS, Azure, Google Cloud, OpenAI, MLflow, ServiceNow, Jira, Databricks, Snowflake, Power BI, and Tableau.checked 4 Oct 2026
- Intended users
- The company describes the product for enterprise AI, technology, governance, risk, legal, and compliance teams.checked 4 Oct 2026
- Policy controls
- ModelOp applies risk-based controls and can enforce policy at delivery time and through AI gateways on live traffic.checked 4 Oct 2026
- Purpose
- ModelOp Center provides enterprise AI lifecycle management and governance for ML, GenAI, agentic, and third-party AI.checked 4 Oct 2026
- System of record
- The platform maintains a searchable inventory of AI solutions, including ownership, metadata, and lifecycle information.checked 4 Oct 2026
- Version and documentation
- The integrations page links to ModelOp Center 3.3 documentation and release notes.checked 4 Oct 2026
- Workflow automation
- Role-based workflows coordinate AI intake, risk assessment, reviews, approvals, monitoring, and retirement.checked 4 Oct 2026
Money
- Monitoring and costs
- The platform monitors AI systems for bias, drift, performance, and cost, with visibility into token use and throughput.checked 4 Oct 2026
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