Comparing OpenNebula with OpenShift, Rafay, and Mirantis k0rdent AI

The AI infrastructure market is evolving quickly. What started as isolated GPU clusters for experimentation is becoming something much larger: production AI factories capable of supporting thousands of GPUs, multiple tenants, distributed sites, hybrid infrastructure, and enterprise-grade operations.

As organizations move from PoCs to large-scale deployments, the choice of management platform becomes increasingly strategic for long-term scalability, operational efficiency, and infrastructure sustainability. The current landscape is increasingly shaped by different approaches represented mostly by OpenNebula, Rafay, Red Hat OpenShift, and Mirantis k0rdent AI. Each platform addresses the AI factory challenge from a different architectural perspective.  

Most AI factory discussions focus too much on individual features. The more important question is architectural philosophy. Some platforms, such as Red Hat OpenShift and Mirantis k0rdent AI, are fundamentally Kubernetes-first. Others, such as Rafay, act more as GPU PaaS overlays on top of existing infrastructure. OpenNebula follows a different path: an infrastructure-first platform that integrates virtualization, cloud orchestration, GPUs, and elastic Kubernetes into a unified operating model.

This distinction becomes increasingly important at scale. An AI factory is not just a Kubernetes cluster. Real deployments combine GPU-intensive training, inference services, VM-based isolation, HPC workloads, multi-tenancy, hybrid infrastructure, networking acceleration, and self-service cloud operations. The underlying platform architecture determines whether the environment becomes more complex over time, or remains manageable as it grows.

Many enterprise and AI-factory workloads still require virtualization, direct IaaS control, or non-Kubernetes deployment models. These include legacy applications, appliance-based software, Windows workloads, specialized networking and security functions, databases, HPC components like Slurm, and workloads that require full OS-level control. Platforms such as Rafay, k0rdent AI, and OpenShift can use bare metal effectively as a Kubernetes and GPU substrate, but they are not primarily designed to expose bare-metal servers or VM infrastructure as general-purpose, multi-tenant IaaS resources for arbitrary workloads.

OpenNebula positions itself differently because it starts from the infrastructure layer. The model combines VMs, elastic Kubernetes, GPU orchestration, multi-tenancy, cloud APIs, and self-service cloud operations within a single integrated platform. This makes it particularly well suited for sovereign AI clouds, GPU-as-a-Service providers, telco clouds, HPC and AI centers, neoclouds, and enterprises operating mixed VM and Kubernetes AI workloads.

OpenNebula’s key differentiation is operational integration. Instead of assembling multiple layers from different vendors, organizations can manage infrastructure, virtualization, Kubernetes, and cloud operations through a unified control plane.

• Rafay focuses on providing a self-service GPU and Kubernetes consumption layer for organizations that already operate Kubernetes infrastructure. This approach is great for organizations already heavily invested in Kubernetes that primarily require a GPU PaaS layer on top of existing infrastructure. The downside is that the underlying VM/IaaS layer is still external and the customer is still more dependent on Kubernetes-centric operational models and proprietary platform services.  
• Red Hat OpenShift is the most mature enterprise Kubernetes-first approach, with the strength being in enterprise Kubernetes operations, AI/MLOps tooling, security, compliance, and the broader Red Hat ecosystem. For organizations standardizing on Kubernetes and Red Hat, OpenShift provides a very complete application platform. However, AI factories are not always purely container-centric environments. VM/IaaS capabilities exist, but they are effectively second-class citizens, with operational complexity increasing significantly in large-scale infrastructure-centric deployments.
• Mirantis k0rdent AI sits between infrastructure orchestration and Kubernetes platform engineering. This positioning is Kubernetes-native and composable, targeting organizations that want distributed AI and GPU infrastructure across hybrid, edge, sovereign, and cloud-native environments. This approach appeals especially to teams with strong Kubernetes expertise, platform engineering capabilities, and declarative operational models. 

Beyond the architectural model and cloud operating model, organizations also need to consider key dimensions such as openness, cost efficiency, scalability, and proven production maturity. These criteria are critical because AI factories are not static environments. They continuously evolve, scale, integrate new GPU generations, and must support multiple teams, tenants, and operational models over time. Choosing the right platform is therefore not only a technical decision, but also a long-term operational and strategic one.

• One of the biggest strategic concerns for organizations building AI factories is long-term dependence on proprietary platforms. Vendor acquisitions, licensing changes, pricing evolution, and ecosystem lock-in can become significant operational and financial risks over time. You cannot build a sovereign AI factory on a platform you do not control. Open infrastructure platforms, like OpenNebula, provide customers with greater infrastructure portability, roadmap independence, ecosystem flexibility, and stronger long-term operational control.
• Cost is another decisive factor. At AI factory scale, even small pricing differences become extremely significant. A difference of only a few cents per GPU-hour can translate into hundreds of thousands of dollars annually in large deployments. The software management layer itself typically represents a small but important fraction of the total AI infrastructure cost, often between 3% and 10%, making operational efficiency and pricing predictability increasingly important. OpenNebula’s infrastructure-first model provides a cost-efficient management layer for large-scale GPU environments.
• Production maturity also matters. AI factories require more than successful demos or proof-of-concept environments. Production deployments require operational stability, distributed scalability, multi-tenancy, reliability, and long-term supportability. This is one of the reasons why infrastructure maturity and operational simplicity are becoming major selection criteria. OpenNebula brings more than 18 years of experience in production cloud environments, including large-scale deployments across telecommunications, cloud providers, research centers, and enterprise infrastructures.

The AI factory market is increasingly dividing into two broad approaches. Kubernetes-first AI platforms focus on platform engineering, AI workflows, and developer tooling. Infrastructure-first AI clouds focus on unified infrastructure operations, virtualization, GPU cloud management, sovereign infrastructure, bare-metal management, and integrated cloud operations. 

No two AI factories are the same. Both models will coexist and, based on our experience, are often within the same AI factory. Some teams will need Kubernetes-native workflows for AI development, training, and inference, while infrastructure teams will still need a robust cloud layer to manage GPUs, VMs, bare metal, networking, storage, tenants, quotas, and distributed sites.

The right choice therefore depends on whether an organization views the AI factory primarily as a Kubernetes platform challenge or as an infrastructure cloud challenge. In practice, successful AI factories will require both layers, often coexisting within the same environment, as already happens at hyperscalers and large cloud providers. This multi-layer approach is also aligned with the NVIDIA NCP Software Reference Guide, which separates AI infrastructure concerns across the Kubernetes platform layer and the underlying infrastructure cloud layer. Kubernetes platforms enable AI workflows, developer productivity, and application lifecycle management, while the infrastructure cloud layer ultimately determines long-term scalability, operational control, cost efficiency, resource sharing, and digital sovereignty. 

This distinction is likely to define the next generation of AI infrastructure platforms optimized for tokens per watt.

From GPU orchestration to multi-tenant cloud operations, OpenNebula provides the foundation for sovereign AI factories at scale. Learn how to build and operate AI infrastructure with full control.