Operationalizing Kubernetes for Virtualization and AI
For two decades, virtualization shaped how enterprises built and managed their IT environments. Now, as organizations pursue hybrid cloud flexibility and prepare for AI-driven workloads, the focus is shifting again. Kubernetes, once the domain of cloud-native developers, is emerging as the new standard for infrastructure.
Why Kubernetes Has Become the Enterprise Standard
What makes Kubernetes so compelling today is its versatility. Organizations are discovering that a single, unified platform can now orchestrate containers, virtual machines (VMs), and AI workloads efficiently. Initially developed as a framework for orchestrating containerized applications, it has matured into a unifying platform that supports three of the most critical priorities in enterprise IT: modern application development, AI/ML workloads, and virtualization. In effect, it gives organizations a single, adaptable framework for running everything from legacy systems to AI-driven applications without rebuilding their infrastructure from the ground up.
Its open, standards-based architecture gives IT teams the flexibility to deploy workloads wherever it makes the most sense: on premises, in the cloud, or at the edge. It also creates anoperational approach that unifies development and infrastructure teams under a consistent model. For enterprises modernizing their environments, Kubernetes is becoming the foundation for what comes next: automation, edge computing, and AI at scale.
Kubernetes for Running AI Workloads
As organizations accelerate toward AI and machine learning, Kubernetes has become the natural choice for managing these complex, compute-intensive workloads. Virtually all AI software is delivered and developed as containerized applications, which makes Kubernetes not just an option but the default operational framework for deploying them. Its ability to scale horizontally, automate resource allocation, and support GPU acceleration makes it an ideal environment. Growing GPU support helps enterprises use those resources more efficiently across distributed clusters and provides flexibility as new AI chips become available.
Kubernetes for Virtualization
Parallel to that, the use of Kubernetes is expanding into virtualization. Through projects such as KubeVirt and enterprise platforms like Red Hat OpenShift Container Platform (OCP) and SUSE Rancher/Harvester, IT teams can now run VMs as workloads within Kubernetes, managed through the same orchestration framework that handles containers, which enables enterprises to keep critical legacy systems operational while adopting modern deployment patterns.
Kubernetes also brings something enterprises have long wanted from virtualization: true portability. Applications built and deployed on Kubernetes can run consistently across on-premises data centers and public clouds. That means development teams can build once and deploy anywhere without rewriting code or worrying about underlying infrastructure differences.
Together, these advances are collapsing traditional boundaries. Developers, data scientists, and infrastructure engineers can all work within a unified environment that supports both cloud-native agility and enterprise reliability. For many organizations, that convergence is what makes Kubernetes an engine to drive the next phase of modern operations, where infrastructure becomes intelligent, automated, and ready for AI-driven workloads.
Adopting Kubernetes for Infrastructure
For most enterprises, the decision to explore Kubernetes is the easy part. The harder work begins with implementation. Moving from traditional virtualization to Kubernetes is less of a technical migration and more of an operational shift that changes how infrastructure is designed, managed, and supported.
One of the first hurdles is the learning curve. Teams that have spent years maintaining VMware environments must now learn how to deploy, monitor, and troubleshoot Kubernetes workloads. The fundamentals of compute, storage, and networking remain the same, but the operational model is different. Day-to-day administrative activities such as patching clusters, managing updates, and maintaining reliable performance require new skills and automation practices.
The migration to Kubernetes demands careful planning and the right tooling. Shifting a VM from VMware into Kubernetes isn’t a simple lift-and-shift. It’s a true migration that involves creating a new VM inside Kubernetes, replicating data, and re-establishing networking and storage configurations. Tools within platforms like OpenShift streamline the process, but success depends on clear architecture, thorough testing, and well-defined compatibility checks before production workloads go live.
Security and Storage: Building an Enterprise-grade Foundation
As Kubernetes adoption expands beyond development environments, security and storage become central to making it enterprise-ready. Each introduces new considerations compared to traditional virtualization.
Security Considerations
Security challenges for Kubernetes include excessive permissions, weak workload isolation, insecure container images, and limited runtime visibility. Excessive permissions are especially common early in the journey when teams grant broad access to accelerate development. Without defined roles and least-privilege policies, those permissions can carry over into production, creating unnecessary exposure and management complexity.
Traditional tools built for static VMs can’t always see into fast-moving, containerized environments. Kubernetes addresses this with native capabilities such as network policies, service meshes, and eBPF-powered workload protection, which provide fine-grained traffic control and deep visibility into how containers communicate. Together, these capabilities extend enterprise-grade segmentation and least-privilege access principles to the microservice level, helping organizations maintain compliance and reduce lateral movement within clusters.
Storage Considerations
When people think of Kubernetes, they often associate it with ephemeral workloads: short-lived containers that can be easily recreated. In enterprise environments, however, Kubernetes is now being used to run persistent, data-driven applications. That evolution demands closer integration between compute, storage, and networking to keep data synchronized and protected across the environment.
In Kubernetes, persistent data is managed through a container storage interface (CSI), which enables organizations to connect the platform to virtually any enterprise storage system. Unlike VMware, which aggregates virtual disks into shared datastores, Kubernetes typically establishes a one-to-one relationship between workloads and their virtual volumes. This design enables flexible, automated storage provisioning but also increases the number of connections that must be managed during failover or maintenance.
To handle those demands, many organizations deploy storage solutions built specifically for container environments, such as Red Hat OpenShift Data Foundation or Portworx, which provide IT professionals with familiar data-management capabilities such as replication, snapshots, and high availability within a Kubernetes context. Together, these advancements deliver the durability and resilience enterprises expect from traditional virtualization while preserving the agility and automation that define modern infrastructure.
Success Is in the Planning
Adopting Kubernetes is a planning challenge where success depends on aligning people, processes, and platforms to support a new operational model. Evolving Solutions can help you navigate each stage of the journey:
- Day 0 – Planning: Assessing workloads, defining architecture, and mapping dependencies
- Day 1 – Deployment: Installing and configuring clusters across on-prem and cloud environments
- Day 2 – Operations: Knowledge transfer, optimization, automation, and ongoing support to keep clusters secure and stable
The goal is to make Kubernetes sustainable. By blending infrastructure expertise with automation and DevOps experience, we help clients build the operational maturity to confidently manage Kubernetes and create a smarter, more agile operational model ready for whatever comes next.
To start planning your Kubernetes-based infrastructure, let’s talk.
