Kubermatic AI
A unified AI platform for enterprises, neoclouds, and sovereign AI providers.
Build an AI Factory that puts every GPU to work.
Enterprise GPU utilization can be as low as 5%, meaning as much as 95% of expensive GPU capacity sits idle.
The usual way to serve five teams with the same model is five deployments and five reserved GPU pools, most of them sitting idle.
Kubermatic AI runs the model once and shares it across teams, with each team isolated by its own key and quota - turning separate deployments into one more efficient AI Factory.
Every expensive GPU actually used
Without Kubermatic AI
- One model per team, deployed and run separately
- GPUs sitting idle, most of the time
- One team's spike starves another
- Locked into one vendor's roadmap
- Every new agent needs its own integration
- Every site runs its own AI stack
With Kubermatic AI
- One model, shared safely across every team
- GPUs earning their cost, not sitting idle
- Quotas so no team starves another
- Built on open standards
- Connects to your existing AI agents through MCP
- One platform across every data center, cloud and edge site
All you need to know about Kubermatic AI
Managing AI infrastructure is hard. GPUs, models, teams, and access rules all have to work together, and most organizations end up gluing that together by hand.
A Kubernetes-native AI platform that turns fragmented GPU infrastructure into an AI Factory: shared AI services that run consistently across on-prem, cloud, and sovereign environments.
It provides a common platform to build and run your AI Factory, where teams manage clusters, GPUs, models, networking, security, and policies, while developers consume AI through self-service interfaces and APIs. Kubermatic AI combines Kubernetes fleet management, AI model serving, GPU orchestration, networking, and security into one platform.
- Deliver LLMs as a Service
- Deploy a model once and securely share it across teams
- Maximize GPU utilization through enterprise multi-tenancy
- Centrally govern AI infrastructure
- Support AI workloads across any infrastructure
Key Features
LLMs as a Service
Publish a model once as a managed service. Every team consumes it through a secure API.
Optimized GPU utilization
Use the GPU capacity you already have instead of buying more to sit idle.
Shared models through multi-tenancy
Every tenant draws from the same deployment, with isolated access and its own quota.
Advanced AI networking
Route traffic based on real GPU utilization and inference requirements.
Works With What You Have
Built-in MCP support means your existing AI agents and tools connect directly.
No Lock-In
Every layer is open source and runs on standard Kubernetes tooling, on any cloud or your own hardware.
Comprehensive Auditing
Built-in visibility and compliance support for frameworks like SOC 2 and PCI-DSS.
We shouldn’t let AI become the reason enterprises go back to proprietary infrastructure. The open source ecosystem has already built so many of the underpinning technologies needed to run AI at scale just like we did in the cloud native era. Kubermatic is a great example of how open source technologies can come together to make enterprise AI more open and sovereign.
Frequently Asked Questions
How can I improve GPU utilization?
The biggest lever is eliminating duplicate model deployments. Most organizations reserve dedicated GPU capacity for every team that needs a model, even though that model sits idle most of the time. Sharing one deployment across teams, with quotas and isolated access per tenant, can significantly improve utilization without adding more hardware. Kubermatic AI enables this approach through secure AI multi-tenancy.
Why is enterprise GPU utilization so low?
Enterprise GPU utilization can be as low as 5%, according to VentureBeat, because teams often deploy a separate copy of a model to guarantee isolation from other teams. Each deployment reserves memory and compute whether or not it is actively serving requests, leaving GPUs idle between spikes while other teams wait for capacity. Kubermatic AI helps address this by allowing teams to securely share model deployments.
What is AI multi-tenancy?
AI multi-tenancy is an architecture where multiple teams, customers, or business units share the same underlying model deployment while staying fully isolated from each other, each with its own API keys, quotas, budgets, and policies. It’s the alternative to deploying a separate copy of a model for every tenant. Kubermatic AI provides the platform layer for this shared model approach.
How do you reduce GPU infrastructure costs?
The fastest way to cut GPU costs is to stop paying for duplicate infrastructure. Instead of reserving a separate GPU pool for every team’s model deployment, run one shared deployment with per-team quotas so hardware serves requests instead of sitting idle behind separate deployments. Kubermatic AI helps organizations consolidate these workloads and get more value from existing GPU capacity.
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, letting teams deploy a model once and share it securely instead of duplicating it for every team.
What infrastructure does Kubermatic AI run on?
Kubermatic AI runs on the GPUs and Kubernetes clusters you already operate, on-premises, at the edge, hybrid, or across cloud and sovereign environments. It’s built from open-source components, including KKP, KDP, KubeOne, and KubeLB, so it works with infrastructure you already manage instead of replacing it.
How do you manage AI across multiple locations?
Many organizations now run AI outside the public cloud, in their own data centers, sovereign clouds and edge sites, to keep data under control and response times low. The risk is that each location becomes its own silo, with separate GPUs, models and policies to maintain. Kubermatic AI manages every location from one platform, so models, quotas and policies stay consistent and GPU capacity is shared instead of duplicated.
What is an AI factory?
An AI factory runs AI like a production operation rather than a set of separate team projects. Shared GPUs, shared models and central policies turn data into AI services reliably and at scale. Kubermatic AI is the software layer that runs it, on your own infrastructure and across every location, so the hardware you’ve paid for keeps producing.
