Operations | Monitoring | ITSM | DevOps | Cloud

VMware vSphere Kubernetes Service (VKS) is the CNCF-certified Kubernetes runtime built directly into VMware Cloud Foundation (VCF), which delivers a single platform for both virtual machines and containers. VKS enables platform engineers to deploy, manage, and scale Kubernetes clusters while leveraging a comprehensive set of cloud services. And with VKS v3.6, that foundation just got significantly more powerful.

Ever had a load balancer become the bottleneck in an on-prem Kubernetes cluster? You are not alone. Traditional hardware load balancers add cost, create coordination overhead, and can make scaling painful. A Kubernetes-native approach can overcome many of those challenges by pushing load balancing into the cluster data plane.

On paper, lift-and-shift VM migration to Kubernetes sounds simple. Compute can be moved. Storage can be remapped. But many migration projects stall at the network boundary. VM workloads are often tied to IP addresses, network segments, firewall rules, and routing models that already exist in the wider environment. That is where lift-and-shift becomes much harder than it first appears.

Despite the wealth of data available, distilling a coherent narrative from a Kubernetes cluster remains a challenge for modern infrastructure teams. Even with powerful visualization tools like the Policy Board, Service Graph, and specialized dashboards, users often find themselves spending significant time piecing together context across different screens.

If you’re running workloads on Amazon EKS, there’s a good chance you already have some form of network observability in place. VPC Flow Logs have been a staple of AWS networking for years, and AWS has since introduced Container Network Observability, a newer set of capabilities built on Amazon CloudWatch Network Flow Monitor, that adds pod-level visibility and a service map directly in the EKS console.

Since the rise of Large Language Models (LLMs) like GPT-3 and GPT-4, organizations have been rapidly adopting Agentic AI to automate and enhance their workflows. Agentic AI refers to AI systems that act autonomously, perceiving their environment, making decisions, and taking actions based on that information rather than just reacting to direct human input.

As anyone managing one or more Kubernetes clusters knows by now, scaling can introduce an exponentially growing number of problems. The sheer volume of metrics, logs and other data can become an obstacle, rather than an asset, to effective troubleshooting and overall cluster management. Fragmented tools and manual troubleshooting processes introduce operational complexity leading to the inevitable security gaps and extended downtime.

Calico, Cilium, Retina, and Netobserv: Which Observability Tool is Right for Your Kubernetes Cluster? Network observability is a tale as old as the OSI model itself and anyone who has managed a network or even a Kubernetes cluster knows the feeling: a service suddenly can’t reach its dependency, a pod is mysteriously offline, and the Slack alerts start rolling in. Investigating network connectivity issues in these complex, distributed environments can be incredibly time consuming.

In our previous post, we addressed the most common questions platform teams are asking as they prepare for the retirement of the NGINX Ingress Controller. With the March 2026 deadline fast approaching, this guide provides a hands-on, step-by-step walkthrough for migrating to the Kubernetes Gateway API using Calico Ingress Gateway. You will learn how to translate NGINX annotations into HTTPRoute rules, run both models side by side, and safely cut over live traffic.

We recently sat down with representatives from 42 companies to discuss a pivotal moment in Kubernetes networking: the NGINX Ingress retirement. With the March 2026 retirement of the NGINX Ingress Controller fast approaching, platform teams are now facing a hard deadline to modernize their ingress strategy.