Thecase for platform-agnostic GPU orchestration in a multi-cluster, multi-tenantworld.
Introduction
As Neoclouds and sovereign entities buildGPU clouds to serve AI workloads, many cloud management software solutionsoffer a bundled Kubernetes distribution as the foundation for their platform.While this approach may seem convenient at first, it introduces a significantlong-term adverse issue: vendor lock-in.
Solutions that tightly couple GPU cloudmanagement software or PaaS with a specific Kubernetes distribution (oftentheir own) quickly become rigid, incompatible with enterprise IT landscapes,and challenging to scale across environments.
In contrast, a modern GPU cloudmanagement software layer must be Kubernetes-aware,not Kubernetes-bound — capable of integrating with any CNCF-compliant K8s cluster, operating across multi-tenant andmulti-cluster environments.
What’s Wrong with Bundled K8s Distributions forGPU Clouds?
What Does Kubernetes-Aware Really Mean?
Being Kubernetes-awaremeans your GPU cloud management software:
- Works across multiple clusters (e.g. per tenant, per region, per environment)
- Can be installed as a controlplane on top of any K8s distribution
- Doesn’t enforce a particular K8s distro
- Integrates with existing K8s-native tools (Prometheus, Grafana, Istio,cert-manager, etc.)
- Supports GPU-aware scheduling (with Run:AI, Ray, SLURM, etc.) as plug-ins,not as dictated dependencies
How aarna.ml GPU CMS GetsIt Right
aarna.mlGPU CMS is a platform-agnosticGPU orchestration solution that’s purpose-built for cloud providers andenterprise AI platforms. It is architected to:
- Work with any CNCF-compliant Kubernetes cluster, including Upstream Kubernetes, OpenShift, EKS, AKS, and SuseRancher/RKE
- Attach to multiple clusters and manage themunder a unified control plane
- Offer strong multi-tenancy by provisioningper-tenant namespaces, RBAC policies, and storage/network isolation —regardless of the underlying K8s distribution
- Integrate with GPU scheduling frameworks likeRay, Run:AI, and SLURM — based on workload types and performance needs
- Orchestrate bare-metal, VM, and container workloads across K8s and non-K8s environments
- Avoid distribution lock-in by focusing onautomation, abstraction, and policy
Whether you're a Neocloud, sovereigncloud, or enterprise Private GPU cloud provider, the aarna.ml approach ensures maximum flexibility, compliance with enterprise architecture,and freedom to evolve yourunderlying platforms.
A Real-World Comparison:
Conclusion
Tightly coupled GPU orchestrationplatforms that require their own Kubernetes distribution may seem attractive inearly stages — but they limit your ability to scale, integrate, and remainagile.
To truly serve diverse AI workloads,multi-tenancy, and hybrid cloud deployments, your platform must be:
- Infrastructure-agnostic
- Kubernetes-aware, not bound
- Supports E2E provisioning and isolation across compute, storage,network and not limited to addressing only PaaS layer
aarna.mlGPU CMS offers exactly that — a powerful orchestrationlayer that integrates with your infrastructure and grows with your business.
To see a live demo or for a free trial,contact aarna.ml
