Build Cloud Native AI & ML

Production-Grade AI on Any Infrastructure

Situation

AI and ML Beyond Traditional Infrastructure

Most AI initiatives stall before they ever reach the customer. While data scientists excel at building models in isolated notebook environments, moving those models into production reveals a massive infrastructure challenge. Modern AI/ML requires a complex orchestration of high-performance hardware and specialized software that traditional IT isn’t equipped to manage.

The core challenges include:

“Works on My Machine”: Models fail when moving from isolated notebooks to production.
GPU Inefficiency: Manual resource management leads to high costs and idle hardware.
Operational Silos: Data science teams often end up managing the infrastructure.
Scaling Bottlenecks: Static setups cannot meet the elastic demands of training or inference.

Now is the time for organizations to assess whether their data centers and cloud strategies are ready to handle this surge in AI & ML demand. In many cases, they might need to bring AI to where the data is to support this growth.

By 2028, 95% of new AI deployments will use Kubernetes, up from less than 30% today.

Gartner, Magic Quadrant for Container Management, 2025

How we help

The AI-Native Infrastructure Platform

Kubermatic Kubernetes Platform (KKP) is an official Kubernetes AI Conformant platform. It provides a standardized technical blueprint that ensures models trained on one KKP cluster can move to any other conformant cluster without rewriting code.

KKP is designed to automate IT operations from the infrastructure to the application, easily operating Kubernetes clusters with consistency from the local development cluster to cloud production deployments.

GPU Lifecycle Automation

KKP automates the full lifecycle of GPU nodes provisioning, health monitoring, and decommissioning—with the same consistency as standard CPU workloads.

Accelerate Machine Learning Research

Data scientists can run reproducible experiments on infrastructure that mirrors production, eliminating the "runs on my laptop, fails in the cloud" problem. Research clusters are provisioned in minutes and decommissioned automatically when experiments are complete.

Hardware Efficiency

KKP utilizes Dynamic Resource Allocation (DRA) to eliminate resource fragmentation and the Advanced GPU Machine Type Selector to match hardware to workload requirements without over-provisioning.

Speed Up Inference in Production

KKP and the KubeLB AI Gateway enable ML application deployment across cloud, on-premises, and edge environments. The platform automates accelerator node scaling, health monitoring, and model deployment pipelines while using intelligent routing and gang scheduling for reliable, high-performance inference.

Use Cases

GPU Cluster Management for Data Science Teams

The Mission: Enable multiple teams to share expensive GPU infrastructure without scheduling conflicts or management overhead.
The Application: KKP enforces multi-tenancy through isolated GPU quotas and per-project cost attribution. Automated health monitoring detects hardware faults early, preventing corruption of long-running training jobs.

Sovereign Federated Machine Learning

The Mission: Run collaborative ML training across organizations without centralizing sensitive data, maintaining 100% data residency.
The Application: As demonstrated in Project MELLODDY, KKP coordinates training across distributed clusters. Each organization trains on local data; only encrypted model updates aggregate centrally to protect proprietary information.

Production-Ready ML

The Mission: Rapidly transition from manual GPU setups to automated, production-grade ML infrastructure.
The Application: We support your teams gain a functioning, GPU-enabled cluster fleet and the operational capability to manage multi-cluster ML workloads independently.

Discover Success Stories

Outcome

ML at Production Scale: Faster, Reliably, Anywhere

By standardizing on Kubermatic, organizations eliminate the infrastructure friction that stalls model delivery.

Same Tools from Laptop to Production

Official Kubernetes AI Conformance provides a standardized technical blueprint that ensures models remain portable across cloud, on-prem, and edge environments without rewriting code. This consistency from local notebooks to global production eliminates configuration drift and environment-specific bugs.

Reduce Time from Data to Inference

Standardized ML pipelines eliminate environment setup overhead. Data scientists focus on models and data, not configuring Kubernetes clusters or debugging infrastructure differences between development and production.

Elastic Scaling & Cost Optimization

KKP scales GPU clusters elastically to meet training demand spikes and scales down automatically to reduce costs when training completes. Inference serving scales horizontally across multiple nodes to handle production traffic without manual capacity planning.