Modern Kubernetes clusters are not monolithic; they host many applications with very different operational needs. Treating a whole cluster as…
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Why Your Kubernetes Cluster Autoscaler Wastes Resources, and How to Fix It
Platform engineers rely on the Kubernetes cluster autoscaler to control cloud cost. However, the autoscaler is only as efficient as…
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Beyond the JVM: Full-Stack Optimization for Node.js on Kubernetes
The promise of Kubernetes lies in seamless scalability, yet running high-performance Node.js applications often reveals “invisible” friction. While JVM tuning…
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Scaling from the Right Foundation: Introducing HPA-Aware Optimization in Akamas Insights
Modern Kubernetes environments rely heavily on the Horizontal Pod Autoscaler (HPA) and tools like KEDA to manage dynamic workloads. Autoscaling…
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Beyond the “Black Box”: Empowering Developers with GitOps-Driven Optimization
In the modern, cloud-native enterprise, the mantra of “you build it, you run it” has transformed the Application Developer. Once…
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The State of Java on Kubernetes 2026: Why Defaults are Killing Your Performance
Java is the backbone of enterprise software, and Kubernetes is the standard for deployment. You would assume that by 2026…
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Kubernetes Performance Testing for Cloud-Native Apps: How Akamas and Speedscale Close the Loop
Kubernetes went from novelty to default in about a decade. As of 2024, over 60% of enterprises had adopted it…
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Top 10 Kubernetes Performance Pitfalls (and how to avoid them)
If you are a cloud native company, you already know that Kubernetes is a solid choice for deploying and managing…
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Application Performance Optimization on Kubernetes: More Than Just Pods
In 2025, Kubernetes dominates the cloud container landscape. It is the “operating system of the cloud.” Yet many organizations are…
