Kubernetes Academy - Zesty
How load balancing strategy affects pod utilization and rightsizing accuracy
June 18, 2026
10 MIN READ
Load balancing is one of the most consequential decisions in your cluster. It directly affects CPU and memory utilization, HPA scaling decisions, and what you’re paying for at the end of the month. And yet, it tends to be very low on most teams’ lists. The factor most teams don’t account for is whether their load balancer understands what a request is. That distinction, surprisingly, lies in the difference between Layer 4 vs Layer 7 of your network stack.
Layer 4 vs Layer 7: What’s the difference?
How load balancing functions differently at each layer: Layer 4 (transport layer) load balancing […]
By Ilan Ovadia
R&D Team Lead
Using Karpenter and still overpaying?
March 12, 2026
5 MIN READ
Karpenter has become the autoscaler of choice for many Kubernetes platform teams, and for good reason. It provisions nodes in real time, selects efficient instance types, consolidates underutilized capacity, and removes much of the operational burden associated with traditional node groups. On paper, the value proposition is compelling: the right nodes, at the right time, for the right price. Many teams expect that adopting Karpenter will significantly reduce their infrastructure costs, but oftentimes, the cloud bill barely changes. If you’ve automated scaling but haven’t seen meaningful savings, the issue usually isn’t Karpenter itself. More often, it is a combination of request […]
By Zesty Team
How to avoid costly instance selection mistakes in Karpenter
February 19, 2026
5 MIN READ
Your Karpenter configuration can make or break your cluster’s efficiency. Configure it poorly and you risk locking yourself into expensive nodes that never consolidate. I have seen clusters where Karpenter was installed correctly, yet costs increased, and the root cause was almost always the same: NodePool design.
The key is to configure it widely enough to give it the breadth to make the best decisions for you. This guide provides steps to design NodePools that give Karpenter enough flexibility to optimize while protecting you from costly mistakes. First, Understand What Karpenter Is Optimizing. Before tuning anything, clarify how Karpenter works. What […]
By Ido Slonimsky
Tech Lead, Zesty
Why “Accurate Requests” Still Lead to Cloud Resource Waste
January 29, 2026
6 MIN READ
If you’re operating Kubernetes in production, you probably know this story: you analyze metrics, tune CPU and memory requests, validate them against historical data, and roll changes out carefully. And even though pods are no longer wildly overprovisioned, the cloud bill stays pretty much the same. If that’s familiar, it’s not necessarily caused by poor execution, but likely a result of how K8s is designed to balance elasticity and safety. Accurate requests are necessary, but don’t eliminate waste on their own. This article is a step-by-step guide that explains where that waste comes from, how to recognize it, and how to […]
By Ido Slonimsky
Tech Lead, Zesty
How to deal with workloads HPA can’t handle
December 11, 2025
7 MIN READ
HPA has become one of the most relied-upon tools in Kubernetes operations, mostly due to its ability to cover many common scaling event use cases. However, there are workloads even HPA can’t help. A DaemonSet that claims resources on every node with no ability to scale. A Postgres primary in a StatefulSet, which does not support multiple primary replicas. A batch Job that spikes unpredictably and HPA has no bearing over. These cases expose the limits of replica-based scaling, reminding us that Kubernetes has more workload patterns than HPA was ever meant to address. This article breaks down why HPA falls short in […]
By Ilan Ovadia
R&D Team Lead
A Practical Guide to Kubernetes Requests and Limits
November 20, 2025
5 MIN READ
- Why resource management matters more than most engineers realize. Kubernetes treats CPU and memory as first-class scheduling resources. That means everything from which node a pod lands on to how it behaves under pressure depends on one thing: the resource values you define. Set them too low and you introduce throttling or OOMKills. Set them too high and you waste capacity or block the scheduler from placing workloads efficiently. The official documentation is very explicit: Kubernetes schedules pods based on requests, not limits. This alone has major operational implications, and it is the foundation of everything you will configure next. […]
By Omer Hamerman
Principal DevOps Engineer
Ready to Cut Kubernetes Waste?
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