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RANCHER

Multi-cluster Kubernetes management for any infrastructure

Apache-2.0

ABOUT

AI teams running Kubernetes-based ML platforms across multiple environments — on-premise GPU clusters, cloud-based training infrastructure, and edge inference nodes — face significant operational overhead managing each cluster independently. Standard Kubernetes distribution lacks centralized RBAC, observability, and application lifecycle management across clusters. Rancher solves this by providing a unified control plane for importing and managing any CNCF-certified Kubernetes cluster, with built-in monitoring with Prometheus and Grafana, multi-tenant RBAC, GitOps-based continuous delivery with Fleet, and an integrated application catalog for one-click deployment of ML infrastructure tools.

INTEGRATION GUIDE

1. Centrally manage on-premise GPU Kubernetes clusters and cloud-based training clusters from a single Rancher dashboard 2. Deploy ML infrastructure tools (Prometheus, Grafana, Kubeflow, MLflow) across clusters with Rancher's application catalog 3. Implement multi-team RBAC for AI platform resources across Kubernetes clusters with AD/LDAP integration 4. Monitor GPU utilization, cluster health, and ML workload resource consumption across all clusters with built-in observability 5. Provision and manage Kubernetes clusters on bare-metal, vSphere, and public clouds using Rancher Kubernetes Engine (RKE)

RANCHER

ABOUT

INTEGRATION GUIDE

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