Kubernetes for VMware Administrators: A Multi‑Part Guide

At TechStacksDecoded, our mission is to “Decode the Future of Technology—we break down today’s most powerful tools and platforms so you can build, deploy, and innovate with confidence.” In that spirit, this multi-part series serves as a primer for VMware vSphere admins and seasoned experts alike, guiding you from foundational concepts such as containers versus virtual machines to advanced topics like storage, networking, and workload management in Kubernetes. Along the way, we’ll draw clear comparisons between familiar VMware constructs—vSphere, ESXi, vCenter, DRS, HA, and more—and their Kubernetes counterparts, leveraging what you already know to make Kubernetes less intimidating and more approachable.

Series Roadmap

Part 1 – From Virtual Machines to Containers: Kubernetes Basics – Establishes the foundations by contrasting hardware-centric virtualization with lightweight containerization and explains why an orchestrator like Kubernetes is indispensable in today’s fast-paced, cloud-native landscape—where rapid scaling, elastic resource allocation, true workload mobility, and freedom from vendor lock-in are critical business drivers. We then introduce Kubernetes in VMware terms, mapping familiar concepts (ESXi hosts ➜ Kubernetes nodes, vCenter ➜ control plane, VMs ➜ pods, VM templates ➜ container images) and include a first look at KubeVirt for running VMs alongside containers within a Kubernetes environment.

Part 2 – Kubernetes Architecture in VMware Terms – Deep dive into Kubernetes components and architecture through a VMware lens. We break down the Kubernetes control plane (API server, scheduler, controllers, etc.) and worker nodes (kubelet, container runtime) by comparing them to VMware’s management plane and hypervisors. We’ll also explore multi-tenancy and resource management (Namespaces vs. Resource Pools, etcd vs. vCenter Database) and how Kubernetes achieves high availability for its control plane.

Part 3 – Kubernetes Networking for vSphere Admins – An exploration of container networking and service exposure in Kubernetes, mapped to VMware networking concepts. Learn how Pods communicate, how Services and Ingress provide load balancing and routing (analogous in some ways to virtual switches, distributed switches, and load balancers in VMware environments), and how Container Network Interface (CNI) plugins compare to VMware’s virtual networking (e.g. NSX) in providing network connectivity across nodes.

Part 4 – Persistent Storage in Kubernetes: VMware Perspective – Understand how Kubernetes handles storage for containers. We’ll relate Persistent Volumes and Persistent Volume Claims to familiar VMware storage constructs (datastores, LUNs, and VMDKs) and explain how Kubernetes abstracts storage via drivers (CSI) in a way that’s analogous to VMware’s storage virtualization. Concepts like StorageClasses will be compared to storage profiles you might define in vSphere.

Part 5 – Deploying & Managing Workloads: From VMs to Pods – Bringing it all together, we’ll learn how to run applications on Kubernetes. This includes the deployment of containers (using YAML manifests) versus deploying VMs (via templates or OVFs in vSphere), scaling applications (ReplicaSets/Deployments vs. cloning VMs or using DRS clusters), self-healing and high availability (Kubernetes health checks and automatic rescheduling vs. vSphere HA/FT), and rolling updates (versus updating applications on VMs). We’ll highlight how Kubernetes’ approach to automation and desired state compares to VMware’s tools and what that means for day-to-day operations.

(Now, let’s jump into Part 1!)

Part 1: From Virtual Machines to Containers – Kubernetes Basics

For VMware veterans, the journey to Kubernetes starts with understanding how containers differ from virtual machines and why an orchestration system like Kubernetes has become essential in modern IT environments. In this first part, we’ll establish that foundation and introduce Kubernetes in terms you already know, mapping its key constructs to VMware’s. By the end of this article, you’ll see that Kubernetes isn’t a foreign new world – it builds on many of the concepts you’re already familiar with, just applied to containers instead of VMs.

Virtualization vs. Containerization – The New Paradigm

Virtualization (as implemented in VMware vSphere) abstracts physical hardware into virtual machines (VMs), each with its own operating system. This was revolutionary because it let us run multiple server OS instances on one physical host, with a hypervisor (ESXi) dividing hardware resources among VMs. In contrast, containerization takes abstraction a step further: instead of virtualizing the hardware to run multiple OSes, it virtualizes the operating system so you can run multiple isolated applications on a single OS kernel . A container packages an application with all its dependencies, but shares the host OS kernel with other containers, making it far more lightweight than a full VM.

Figure 1: Virtual Machines vs. Containers. On the left, each VM includes a full guest OS on top of the hypervisor, which sits on the infrastructure. On the right, containers run on a shared host OS (managed by a container engine like Docker), so they don’t need separate guest OS instances. This fundamental difference makes containers much more lightweight and faster to start compared to VMs. For example, a VM might take minutes to boot its OS, whereas a container can start in seconds because it’s just launching an isolated process on an existing OS. Containers still provide isolation, but at the process level rather than hardware level.

In practical terms, containers enable a style of application deployment that is extremely portable and consistent. If you package an application into a container image, it will run the same way on any environment with a container runtime (because all its libraries and dependencies are bundled with it). This solves the “it works on my machine” problem that many VMware admins and developers are familiar with. You no longer need to create a new VM for each environment or worry about OS configuration drift – the container image is a consistent unit that can be deployed anywhere. Many organizations today run containers inside VMs (for example, Docker on a VM in vSphere), which is perfectly fine – but as you deploy more containers, you’ll face the same challenges of scale and management that led you to use vCenter for managing VMs. This is where Kubernetes comes in.

Kubernetes at a Glance: vCenter for Containers

When VMware admins began running hundreds of VMs, vCenter Server became indispensable – it provides a central control plane to manage all those ESXi hosts and VMs (cluster management, scheduling, HA, etc.). Similarly, as organizations run dozens or hundreds of containers, they need a central orchestrator. Kubernetes (K8s) is essentially the vCenter for containers: it’s an open-source platform (originally developed at Google) that automates the deployment, scheduling, and management of containerized applications across clusters of machines. Kubernetes takes container technology (like Docker) and makes it production-ready by adding the management and self-healing capabilities that raw containers alone lack. Just as vSphere elevated VMs from a neat idea (VMware Workstation on a single machine) to a data-center staple, Kubernetes turns isolated containers into a reliable, distributed system.

At a high level, a Kubernetes cluster consists of a control plane (think management layer) and a set of worker nodes. The control plane is often called the master (though Kubernetes now just calls it “control plane”), and it’s analogous to vCenter in many ways – it is the brain of the cluster, exposing an API endpoint for administrators and controlling the overall state of the system. The worker nodes can be physical servers or VMs (even running on ESXi) – these are analogous to your ESXi host servers. Kubernetes nodes are where the actual workloads run, just as ESXi hosts run the VMs. In Kubernetes, the workloads are Pods (we’ll explain these shortly), which are essentially the Kubernetes equivalent of VMs as the unit of deployment.

A key concept in Kubernetes is desired state management. Instead of manually starting and stopping instances (VMs or containers) one by one, in Kubernetes you declare what you want (e.g., “run 3 instances of this application container”) and the system automatically ensures that reality matches the desired state. You typically declare this in YAML configuration files (or via commands). This is similar to how in vSphere you might set a desired state for a cluster (like DRS rules or a certain number of VM replicas across hosts) – but Kubernetes takes it further by dynamically converging on the declared state. If a container (or an entire node) fails, Kubernetes notices it and automatically reschedules the workload on another node to maintain the desired state. In VMware, you achieve high availability with features like HA (automated VM restart on another host) or Fault Tolerance. Kubernetes builds such resilience in at the application level – e.g. using ReplicaSets (equivalent to a desired count of pod replicas) to replace failed pods. We’ll see more of these parallels soon.

Before diving deeper, let’s map some of the core Kubernetes concepts to familiar VMware constructs. This side-by-side comparison will provide a “translation guide” as you continue learning Kubernetes.

Mapping VMware Concepts to Kubernetes

Many Kubernetes concepts have direct (or close) parallels in the VMware world. By leveraging these analogies, you can quickly get a sense of what each Kubernetes component does:

• ESXi Host → Kubernetes Node: An ESXi host is a physical server hypervisor that runs your VMs. Likewise, a Kubernetes node (physical or virtual) runs your container workloads. Just as one ESXi host can power many VMs, a node can host many pods—each a wrapper around one or more containers. You can even deploy Kubernetes on top of vSphere, where every node is itself a VM on an ESXi host.

  
  

• Control-plane link: On a node, the kubelet is the agent that enforces the control plane’s desired state—starting, stopping, and reporting on pods. In the ESXi world, this role is split between the vCenter agent (vpxa) and the host management daemon (hostd). vpxa receives instructions from vCenter, while hostd executes them locally—together mirroring how the kubelet relays commands from the Kubernetes control plane and orchestrates workloads on the node.

  
  

• vCenter Server → Kubernetes Control Plane: vCenter is the centralized management system for your VMware cluster, controlling what runs where on the ESXi hosts. Kubernetes’s control plane (often just called “the master”) serves a similar role . It runs critical processes like the API server (analogous to vCenter’s API/UI endpoint), a scheduler (which decides which node should run a new workload, much like DRS placement decisions), and controller managers (which handle cluster-level functions like replicating pods, similar to how vCenter’s services automate HA or load balancing policies). The control plane also includes a data store called etcd – a distributed key-value database where cluster state is stored – you can liken this to vCenter’s database which stores the inventory and configuration of the environment. One key difference: vCenter itself doesn’t run VMs, whereas in Kubernetes the control plane components could run on dedicated nodes or even on the same nodes as pods in smaller setups. Either way, the Kubernetes control plane is the brains, ensuring the cluster’s desired state is maintained, much like vCenter manages the desired state of a vSphere cluster.

  
  

• Virtual Machine (VM) → Pod: In vSphere, a VM is the fundamental unit of compute, encapsulating an operating system and applications. In Kubernetes, the smallest deployable unit is a Pod, which represents one or more containers that are tightly coupled (usually one application per pod). You can think of a pod as a “lightweight VM” from the ops perspective – it has its own isolated environment, IP address, and allocated resources, but it’s much smaller and shares the node’s OS kernel. Just as a VM might host a single application or service, a pod typically hosts a single primary container (and possibly some helper containers). Each pod gets its own IP address (often from an internal virtual network), just like each VM gets an IP. But unlike VMs, pods are ephemeral and meant to be dynamically created or destroyed by the orchestrator as needed (for scaling or recovery). You don’t usually manage pods by hand one-by-one, instead you define higher-level controllers (like Deployments) that manage pods – conceptually similar to how you might use an automation tool to manage a group of VMs.

  
  

• VM Templates & Images → Container Images: VMware admins often create golden VM templates (or VM clones) to quickly spin up new VMs with a pre-configured OS and software stack. In the container world, the equivalent is a container image. A container image is like a lightweight template for an application – it packages the app and all its dependencies (libraries, runtime, etc.) in a portable format. When you launch a container (or pod), you specify what image to use (for example, an Nginx web server image, or a custom application image you built). The container image concept maps to a VM template in that it’s a pre-built blueprint for a runtime environment, but it’s immutable and much more easily distributed (via container registries, akin to an app store for images). Instead of vCenter templates + ESXi cloning operations, you have Docker/OCI images and the Kubernetes mechanism to pull those images onto nodes. The result is similar – rapid provisioning of new instances – but while a VM template might be several gigabytes, a container image is often only tens or hundreds of megabytes, and launching a new container is nearly instantaneous.

  
  

• Resource Pools → Namespaces: In vSphere, Resource Pools carve a cluster into logical pools where you can apply CPU / memory limits or reservations. Kubernetes achieves a comparable effect with Namespaces, which provide soft isolation—a lightweight security and organizational boundary—inside a single cluster. Namespaces let you segment workloads by team, project, or micro-service domain (e.g., team-alpha and team-beta or payments and analytics) and attach resource quotas, network policies, and RBAC rules to each segment. Although not a direct one-to-one mapping—namespaces also scope object names and access control—you can think of a namespace as a virtual sub-cluster, conceptually similar to how a Resource Pool or folder separates and governs groups of VMs in vCenter.

  
  

• Tags & Metadata: vSphere Tags → Kubernetes Labels: VMware vCenter allows you to assign tags to VMs or other objects for organization, grouping, or identifying VMs with certain roles (e.g., tagging VMs by application or owner). Kubernetes has a powerful metadata system based on labels – key/value pairs attached to objects (pods, nodes, etc.) that identify attributes and group them. Labels in Kubernetes are like an enhanced tagging system; you might label pods with app=web or env=staging. These labels are used by Kubernetes itself to select objects for operations – for instance, a Service will use labels to know which pods to send traffic to. In VMware, tags are mostly for human or script identification, but in Kubernetes, labels are core to how the system functions (selectors use labels to associate pods with replica sets, services, and so on). Think of it this way: vSphere tagshelp you search and group VMs, whereas K8s labels not only group containers but actively drive automation (like auto-scaling and rolling updates targeting specific labeled pods).

  
  

• High Availability (HA) → Kubernetes Self-Healing (ReplicaSets/Deployments): vSphere’s High Availability (HA) feature will restart a VM on another host if the original host crashes. There’s also Fault Tolerance (FT) which keeps a secondary VM running in lockstep as a backup. Kubernetes achieves high availability at the application level using ReplicaSets (often managed via a Deployment). You specify how many instances (replicas) of a pod you want, and Kubernetes ensures that many are running – if one node fails or a pod crashes, Kubernetes will spawn a replacement pod on another node automatically. The difference from VMware FT is that in Kubernetes all replicas are active at the same time (there’s no concept of a “shadow” VM that only takes over on failure). It’s more like running multiple load-balanced VMs for HA, but Kubernetes manages that for you. Additionally, Kubernetes has liveness and readiness probes (health checks) for each pod – if a container inside a pod hangs or becomes unhealthy, Kubernetes can detect it and restart that container (similar to VMware HA’s VM health monitoring via VMware Tools, but at a more granular level). In short, Kubernetes is always watching the desired state (e.g., “5 pods running”) and will self-heal by rescheduling or restarting containers to meet that state. This gives you resilience akin to what VMware HA/FT/DRS provide, but built into the application orchestration.

  
  

• DRS (Dynamic Resource Scheduler) → Kubernetes Scheduler: VMware DRS automatically balances VMs across hosts for optimal performance (moving VMs if one host is overloaded). Kubernetes has a scheduler that initially places pods on nodes based on resource requirements and policies. However, once a pod is running, Kubernetes won’t live-migrate it the way DRS can vMotion VMs; instead, the focus is on placement and letting the app handle scaling. You can set resource requests/limits on pods, and Kubernetes will schedule and pack pods onto nodes respecting those constraints (similar in spirit to how DRS decides initial placement and when to rebalance). If you’re used to DRS rules (affinity rules, etc.), Kubernetes has analogous concepts like affinity/anti-affinity rules for pods to influence scheduling decisions. We’ll explore more on the scheduler in Part 2, but it’s worth noting that Kubernetes’ approach to resource management is declarative – you tell it the desired CPU/memory for each pod and it figures out placement – much like how you set VM resource reservations or shares and let vCenter/DRS handle the rest.

  
  

These comparisons only scratch the surface, but they show that your VMware knowledge provides a strong context for understanding Kubernetes. Next, let’s address a common question VMware admins have: What if I still need to run traditional VMs? Enter KubeVirt.

Running VMs on Kubernetes with KubeVirt

By now, you might be thinking: “Containers are great, but I can’t just replace all my VMs overnight.” Many VMware admins have workloads that can’t be easily containerized (legacy applications, specific OS requirements, etc.). KubeVirt is an open-source project (backed by Red Hat and others) that offers an intriguing solution: it lets you run virtual machines inside a Kubernetes cluster, alongside container workloads. In effect, KubeVirt treats VMs as just another kind of workload in Kubernetes, so you can manage VMs with Kubernetes APIs in a unified control plane. Think of it as running a VM inside a special pod – the VM gets encapsulated in a container runtime (using KVM/QEMU under the hood) but is orchestrated by Kubernetes schedules and policies.

What does this achieve? For one, it means you could gradually shift to a Kubernetes-centric infrastructure withoutabandoning the VM-based apps that are not ready to be containerized. KubeVirt provides a bridge between the VM world and the container world. For example, with KubeVirt you can define a VM (with its CPU, memory, disk needs) as a Kubernetes object and have Kubernetes schedule that VM onto a node, manage its lifecycle, and even connect it to the same networks and storage that containers use. This allows for scenarios like running a legacy Windows VM alongside modern microservices on the same Kubernetes platform. It’s important to note that KubeVirt isn’t meant to replace VMware vSphere’s full range of features – you won’t necessarily use Kubernetes + KubeVirt to run huge monolithic databases with the same performance optimizations VMware provides. Instead, it’s about convenience and gradual transition: “Rather than a direct alternative to VMware, think of Kubernetes with KubeVirt as a strategic bridge” that helps you evolve toward a more cloud-native infrastructure at your own pace.

KubeVirt in action: Suppose you have a small web app that still runs on a Windows Server VM with some custom setup, and you have other parts of the application already running as containers (maybe .NET Core or Java services). With KubeVirt, you could run that Windows VM within your Kubernetes cluster. Your ops processes can then begin to unify – you use kubectl (Kubernetes CLI) or Kubernetes dashboards to manage both the container pods and the VM, monitor their health, and define policies for both. Over time, perhaps that VM can be phased out or converted to a container image, but in the meantime, KubeVirt has bridged the gap. (For a deeper exploration of this approach, see our article “Beyond VMware: Exploring KubeVirt as a Bridge to Containerization” which goes in-depth on using Kubernetes+KubeVirt as a transitional strategy.)

Conclusion and Next Steps

In this first part, we covered the fundamental differences between traditional VMs and containers and introduced Kubernetes using terminology you know from VMware. The key takeaway is that Kubernetes plays a similar role for containers that vSphere plays for VMs – it provides the tools to run applications at scale with resilience and manageability. Many VMware constructs have an analogous concept in Kubernetes, from clusters, hosts, and VMs to clusters, nodes, and pods. As a VMware administrator, you already have a mental model for abstracting and managing infrastructure; Kubernetes extends that model to a cloud-native, developer-friendly paradigm.

In Part 2, we will dig deeper into Kubernetes architecture. We’ll explain the control plane components and node components in detail, using VMware vCenter and ESXi internals as reference points. You’ll learn how Kubernetes handles authentication, scheduling, cluster state storage, and more. By solidifying your understanding of how Kubernetes is built (in a way that parallels vSphere’s design), you’ll be even more confident in navigating and adopting this powerful platform.

Stay tuned as we continue to decode Kubernetes for VMware admins. By the end of this series, you’ll be equipped to leverage both your virtualization expertise and new container orchestration skills – allowing you to build, deploy, and innovate with confidence in a hybrid VMware-Kubernetes world!