OpenShift for Hybrid AI Workloads
Is OpenShift the answer for your AI platform challenges?
Most enterprise AI projects don’t start in Kubernetes—but that’s often where they end up when it’s time to scale, govern, and operate across environments.
If your organization is committed to hybrid cloud or has deep Red Hat investment, OpenShift is a logical candidate to standardize infrastructure for AI workloads across data centers, public cloud, and edge. Red Hat markets OpenShift AI (formerly RHODS) as an “AI developer platform”—but let’s unpack what that really means.
Not Quite an AI Platform (Yet)
Red Hat avoids the PaaS label, likely to distance itself from the rigidity and lock-in associated with older-generation platforms. Instead, it positions OpenShift AI as a modular, infrastructure-first offering that lets you bring your own tools and pipelines. But for many enterprise platform teams, that modularity feels like a burden—not a feature.
On paper, OpenShift AI includes:
Jupyter notebooks and curated data science images
Pipelines via Kubeflow and Tekton
GPU support via NVIDIA GPU Operator
Model serving through KServe
Optional integration with S3-compatible storage via OpenShift Data Foundation
But OpenShift only really delivers on point #1—consistent infra abstraction—and even that comes with a heavy lift.
What makes it heavy?
GPU scheduling isn’t turnkey—you’ll need to install and configure the NVIDIA operator, tune workloads for GPU affinity, and validate driver compatibility across clusters.
Pipelines are fragmented—deciding between Tekton and Kubeflow requires deep knowledge of both, and neither integrates seamlessly with OpenShift’s developer experience.
Serving models in production requires plumbing—configuring KServe across clusters, securing endpoints, and exposing APIs to upstream apps takes real effort.
This is the kind of effort that delays projects or leaves platform teams fielding tickets from confused data scientists.
What Developers Actually Want
Red Hat pitches OpenShift AI as a developer platform—but it’s still very infra-centric. What developers increasingly expect from an AI platform includes:
A cohesive end-to-end experience from experimentation to deployment
Easy onboarding, not a YAML marathon
Model performance monitoring and drift detection baked in
CI/CD workflows integrated with secrets, access controls, and internal portals
And yes, AI Code Assistants and boilerplate generation—tools that actively help them build
OpenShift doesn’t deliver that today. It delivers the scaffolding for it—if your team is willing to invest time, training, and custom integration.
Why Red Hat Took This Path
To be fair, Red Hat is playing to its strengths. Its core differentiator has always been enterprise-grade Kubernetes and hybrid infrastructure. OpenShift AI is a natural extension of that DNA: secure, policy-driven, and flexible.
But the PaaS hesitation—strategic or not—is starting to show. As enterprise devs grow accustomed to platforms like Azure ML, Vertex AI, or even Hugging Face Spaces, the expectation isn’t just infrastructure. It’s enablement.
What Needs to Happen Next
If you’re running OpenShift today, you’ve got a foundation. But delivering an actual AI developer experience means building a layer above it:
Templates for model training and serving
Integration with GitOps, secrets, and internal developer portals
Pre-built GPU-ready environments and code scaffolding tools
Eventually, AI code assistants fine-tuned for your enterprise context
If you’re not running OpenShift, and you’re comparing it to cloud-native AI platforms, understand this: OpenShift is flexible and portable—but you’re on the hook to make it usable.
Future Outlook
There’s still time for Red Hat to close the gap. They’ve proven they can partner deeply (e.g., with NVIDIA) and integrate open-source tooling into an enterprise platform. With the right acquisitions, stronger developer workflows, and clearer product packaging, OpenShift AI could evolve into a full-stack AI PaaS—whether or not they use the label.
Bottom Line
OpenShift offers a consistent hybrid substrate for AI workloads. But developers don’t want a substrate—they want a springboard.
The challenge for Red Hat is to move beyond enabling infrastructure and start enabling builders. And for enterprise platform teams, the challenge is whether you're ready to build that layer yourself—or want it ready-made.