Kubermatic AI
A unified AI platform for enterprises, neoclouds, and sovereign AI providers.
Deploy the model once. Not five times.
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 the same model is five deployments and five reserved GPU pools, most of it sitting idle.
Kubermatic AI runs it once and shares it, with each team walled off by its own key and quota.
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
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
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 shared AI services across on-prem, cloud, and sovereign environments.
It provides a shared platform where infrastructure and platform teams centrally 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.
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.
