Currently, companies running AI workloads are spending millions on one of their most expensive infrastructure investments: GPUs. However, much of that capacity sits idle. In fact, industry reports have found GPU utilization can be as low as 5%. Organizations continue investing in additional hardware, while much of what they already own goes unused.
We built Kubermatic AI to solve this.
What is Kubermatic AI?
Kubermatic AI is a Kubernetes-native platform that helps organizations run AI infrastructure more efficiently. It’s a platform layer that sits on top of the GPUs and clusters you already run.
Instead of every team building and managing its own AI environment, Kubermatic AI creates a shared platform. Infrastructure teams manage GPUs, models, security, and policies centrally. Developers consume AI through self-service, and it connects to the agentic workflows they already use through built-in MCP support, so there’s no new interface to learn.
The idea is simple: deploy a model once, and share it securely across every team. Instead of five teams each reserving their own GPUs for the same model, one deployment serves everyone, and each team gets its own key, quota, and policy. That’s the difference between GPU capacity that mostly sits idle and GPU capacity that’s actually used.
That means fewer duplicate deployments, better GPU utilization, and a much simpler way to scale AI across an organization.
The problem we see everywhere
Right now, many organizations solve AI access by creating separate model deployments for every team or business unit.
It works… but it’s expensive.
Every new deployment reserves GPU memory and compute, even when the model isn’t actively serving requests. At the same time, another team may be waiting for GPU capacity because those resources are already reserved elsewhere.
The result is a frustrating paradox.
Some teams can’t get GPUs when they need them, while other GPUs spend most of their time idle.
“We’re watching companies turn GPUs into the most expensive idle inventory in tech.”.
— Sebastian Scheele, Co-founder and CEO
How Kubermatic AI solves it
Kubermatic AI takes a different approach than traditional AI platforms.
Instead of deploying multiple copies of the same model, organizations deploy it once and securely share it through built-in enterprise multi-tenancy.
Every team gets:
- isolated API access
- independent quotas
- separate budgets
- governance policies
- secure tenant isolation
But everyone uses the same underlying model deployment.
That changes the economics of enterprise AI. Instead of duplicating infrastructure, organizations can share it safely.
What that means in practice
With Kubermatic AI, organizations can:
- Deliver LLMs as a Service, so developers consume centrally managed models through secure APIs.
- Deploy once and serve many, eliminating duplicate model deployments.
- Improve GPU utilization, keeping expensive hardware serving requests instead of sitting idle.
- Govern AI centrally, with policies, quotas, and security managed from one platform.
- Run AI anywhere, across on-premises, cloud, hybrid, and sovereign environments.
Built on the Kubermatic platform
Kubermatic AI builds on the open-source technologies that already power enterprise Kubernetes environments.
It brings together:
- Kubermatic Kubernetes Platform (KKP) for multi-cluster lifecycle management
- Kubermatic Developer Platform (KDP) for self-service developer experiences
- KubeOne for Kubernetes lifecycle management
- KubeLB for AI-aware networking
- KubeSG for secrets management
- Kubernetes-native AI frameworks like KServe and llm-d for model serving
Together, these components create a single platform for running AI infrastructure instead of stitching together multiple disconnected tools.
Let’s talk
If you’re building an internal AI platform, operating a Neocloud, or looking for a better way to use your GPU infrastructure, we’d love to show you what Kubermatic AI can do.
Get in touch with our team to learn more, request a demo, or explore how Kubermatic AI fits into your environment.
