IBM has announced two new
managed services on IBM Cloud: Red Hat AI Inference on IBM Cloud and Red Hat OpenShift Virtualization Service on IBM Cloud. The services are focused at enterprises that want to move AI into real production and rethink how they manage virtual machines across hybrid cloud environments.
IBM Targets Production AI Workloads
Red Hat AI Inference on IBM Cloud is designed to help organizations run real-time AI inference without managing the underlying GPUs, infrastructure, or platform operations themselves. The service uses Red Hat AI’s inference engine and IBM Cloud infrastructure to support production AI workloads, including applications and agents that need consistent performance. IBM said the service includes governance controls, audit logging, privacy controls, IBM Cloud IAM integration, and SLA-backed reliability.
As AI usage moves across more teams and applications, inference can become difficult to track and manage. IBM says the service is designed to give customers more visibility into how models are being used, while standardizing model serving, access controls, audit logging, and workload orchestration.
Briana Frank, VP, IBM Cloud PaaS and Platform Product Management and Design, told ChannelE2E, “As enterprises scale AI across teams and applications, inference cost becomes one of the biggest operational challenges. Our approach is to give clients a fully managed inference platform with predictable performance, built-in governance, and clear visibility into how models are being used. By standardizing model serving, access controls, audit logging, and workload orchestration, organizations can understand and manage consumption before it becomes unpredictable.”
Frank said IBM also optimizes the underlying inference engine for high throughput and low latency, which can help reduce cost per request as usage grows. The service also uses autoscaling and a shared models-as-a-service architecture, giving enterprises a way to run production AI without each team building and managing its own inference setup.
Why the Red Hat AI Add-On Matters
IBM is positioning the service as a fully managed Red Hat AI add-on that gives customers a supported environment for model serving, governance, observability, and performance optimization. The company said IBM Cloud is the only cloud that provides a fully managed Red Hat AI add-on with access to the full capabilities of Red Hat AI.
“Enterprises want an AI platform that is open, consistent, and production-ready across hybrid environments,” Frank said. “IBM Cloud is the only cloud delivering the full Red Hat AI stack as a fully managed add-on, which means customers get a complete, supported environment for model serving, governance, observability, and performance optimization - a fully managed cloud service on IBM Cloud. That level of integration simply isn’t available elsewhere.”
The service also supports models as API-accessible resources. This gives internal teams a more consistent way to access AI models rather than creating fragmented setups across business units, developer teams, or cloud environments. Frank said this gives organizations “a unified AI foundation that works the same way on-prem, in their data centers, and in the cloud,” while reducing fragmentation and simplifying operations.
IBM Adds a Managed Path for Virtual Machines
IBM is also adding Red Hat OpenShift Virtualization Service on IBM Cloud for organizations that want a managed path to migrate and run VM-based workloads on OpenShift. The service runs on IBM Cloud VPC Bare Metal and includes lifecycle management, upgrades, patching, automated recovery, and worker-node remediation. It also includes migration tooling, including the Migration Toolkit for Virtualization, to help customers move workloads from legacy environments with less disruption.
Partners will be an important part of that virtualization opportunity, especially for larger migrations. IBM said the service is designed to be self-service, but many enterprise customers still rely on IBM Consulting, Red Hat Services, and global systems integrators for complex hybrid cloud transformation work.
“While the service is designed to be intuitive and self-service, many customers include a partner for large-scale migrations,” Frank said. “For example, hybrid cloud transformations can be driven by IBM Consulting, Red Hat Services, and global system integrators who specialize in complex enterprise environments. They’re essential to helping clients move from legacy virtualization platforms to a more secure, predictable, Kubernetes-based foundation.”
Where MSPs and Partners Fit
For MSPs and managed cloud providers, the opportunity extends beyond the first migration. Once workloads are running, partners can help manage VM lifecycle work, monitoring, patching, compliance and cost optimization. Those are recurring services customers continue to need after the initial move is complete.
IBM is also framing the go-to-market motion around longer-term partner involvement. Partners can help customers migrate workloads, manage ongoing operations, support containerization plans, and guide AI adoption over time.
“We’re bringing these services to market with a partner-first strategy that aligns with how enterprises modernize today,” Frank said. “Partners can lead migrations, manage ongoing operations, and guide clients through containerization and AI adoption. This creates a multi-year engagement model rather than a one-time project.”
Frank said partners can also build recurring revenue through managed operations, security and compliance services, performance tuning, modernization roadmaps, and AI lifecycle management. That matters for MSPs and managed cloud providers because it moves the conversation beyond infrastructure migration and into ongoing service ownership.
The Enterprise Takeaway
IBM is trying to make IBM Cloud more useful for two big enterprise needs: running AI in production and modernizing virtual machines. Many companies want to use AI more widely, but they also need to control security, cost, and governance. At the same time, they are reviewing their virtualization strategies and looking for more predictable ways to run workloads, with a clearer path to containers and modern applications. For customers, if these services make infrastructure easier to run, improve governance, and simplify migration planning, they could help enterprises move faster without adding more pressure on already stretched IT teams.
