Kubermatic AI

The Cost of Idle GPUs

Enterprise AI is driving unprecedented investment in GPU infrastructure, yet many organizations struggle to get value from those investments.
Teams deploy duplicate AI models, reserve GPU resources for individual projects, and build isolated AI environments that are difficult to govern and expensive to operate. The result is a paradox: some teams wait weeks for GPU capacity while other GPUs sit idle.

The challenge is no longer acquiring GPUs: it’s operating them efficiently.
Kubermatic AI transforms fragmented GPU infrastructure into a shared, governed AI platform, allowing organizations to deploy models once, serve multiple tenants, and make better use of the GPU capacity they already have.

What is Kubermatic AI?

Kubermatic AI is a Kubernetes-native AI Platform-as-a-Service for building, operating, and scaling AI infrastructure across on-premises, cloud, hybrid, and sovereign environments.

It provides a common platform for infrastructure teams, platform engineers, and AI developers. Platform teams manage clusters, GPUs, models, networking, security, and policies centrally, 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.

A Different Approach to AI Infrastructure

Traditional infrastructure management treats GPUs, clusters, networking, and storage as resources that individual teams provision and manage.
Kubermatic AI takes a different approach. It adds a platform layer on top that turns this underlying infrastructure into shared, governed AI services. Infrastructure and platform teams manage capacity, models, policies, and security centrally, while developers consume AI through self-service interfaces and APIs.

Instead of every team building and managing its own AI stack, organizations can deploy once, share securely, and make better use of the GPU capacity they already have.

Key Features and Benefits

Hard Multi-tenancy

Complete separation of resources and API surfaces between tenants: isolate tenants, while securely sharing underlying infrastructure.

Bare-metal lifecycle management

Provision, manage, and decommission physical infrastructure efficiently as capacity needs change. Supported by KubeOne and Metal3/Tinkerbell integrations.

Advanced AI networking

Route AI workloads intelligently based on GPU utilization and inference requirements, with built-in security and traffic controls. Supported by KubeLB + Gateway API.

Kubernetes-as-a-Service

Provision and manage Kubernetes environments consistently across bare metal, VMs, and cloud infrastructure.

Templatized deployments

Standardize infrastructure and AI workloads with repeatable, declarative deployment templates.

Audit and logging

Centralize policies, access controls, and audit information across AI infrastructure. Long-term historical audit logs for compliance and governance, CRA and DORA-aligned.

GPU Optimization

Improve GPU utilization through GPU sharing, disaggregated inference, gang scheduling, topology-aware placement, and DRA.

How Kubermatic AI works

Deploy Models Once

Platform teams deploy and manage AI models centrally instead of creating separate deployments for every team or customer.

Share models securely through multi-tenancy

Multiple tenants consume the same model deployment while maintaining isolated API access, quotas, budgets, and policies. The infrastructure is shared; access remains isolated.

Deliver LLMs as a Service

Centrally managed models are exposed through APIs, allowing developers to consume LLMs without managing GPUs, model deployments, or underlying infrastructure.

Optimize GPU utilization

Kubermatic AI manages GPU allocation, scheduling, networking, policies, and secrets across workloads, helping organizations use available GPU capacity efficiently while maintaining centralized control.

Kubermatic AI-PaaS Reference Architecture

The Kubermatic AI-PaaS reference architecture is a layered, Kubernetes-native stack. It connects the underlying infrastructure with the AI services consumed by developers and data scientists.

Each layer is modular and declaratively managed, while the management and control plane provides a consistent operational model across the environment.

Application Layer

Customer AI and ML workloads, including RAG applications, agentic AI, computer vision, code generation, digital twins, and virtual assistants.

Application Platform Layer

AI services and workload frameworks used to deploy and operate models and AI workloads, including KServe, llm-d, Slinky/Slurm, NVIDIA AI Enterprise, NIM Operator, and the KDP service catalog.

Platform Layer

Kubernetes-as-a-Service, bare-metal lifecycle, virtual private cloud, and public cloud integrations, orchestrated by KKP.

Compute Layer

Operating systems, container runtime, GPU drivers, accelerator management, and GPU resource allocation.

Infrastructure Layer

Physical and virtual compute, GPUs, high-performance networking, and storage.

Management and Control Plane

KKP, KDP, and KubeOne provide centralized fleet management, cluster and node lifecycle management, policy enforcement, operations automation, observability, and self-service.

Platform Components

Kubermatic AI is built from modular, Kubernetes-native components that can be combined according to the organization’s infrastructure and workload requirements.