The ScaleOps Blog – Guy Baron

In our previous post, we explored why Kubernetes cost optimization often falls short. Teams are stuck chasing outdated recommendations instead of addressing inefficiencies in real time. But even with the best automation strategies, organizations face deeper challenges that make cost optimization difficult to implement.

At first, everything in your Kubernetes cluster seems fine. The workloads are running smoothly, and the infrastructure is stable. Then the bill arrives, and it’s way higher than expected. The initial reaction is confusion—how did this happen? 

We’re thrilled to announce that our innovative Pod Placement feature has reached General Availability (GA)! After providing value to many of our customers during the beta program while gathering feedback and performing rigorous real-world validation, ScaleOps is proud to offer this game-changing solution as a production-ready feature for all our customers.

In our previous post, ScaleOps Pod Placement: Optimizing Unevictable Workloads, we introduced the challenges of managing critical, unevictable workloads in dynamic Kubernetes environments. Today, we’re excited to dive deeper into the problem and share how our GA release of Pod Placement effectively solves it, providing up to 50% additional cost savings on top of our standard workload rightsizing.

Amazon recently introduced EKS Auto Mode, a feature designed to simplify Kubernetes cluster management. This new feature automates many operational tasks, such as managing cluster infrastructure, provisioning nodes, and optimizing costs. It offers a streamlined experience for developers, allowing them to focus on deploying and running applications without the complexities of cluster management.

When managing large-scale Kubernetes clusters, efficient resource utilization is key to maintaining application performance while controlling costs. But certain workloads, deemed “unevictable,” can hinder this balance. These pods—restricted by Pod Disruption Budgets (PDBs), safe-to-evict annotations, or their role in core Kubernetes operations—are anchored to nodes, preventing the autoscaler from adjusting resources effectively. The result? Underutilized nodes that drive up costs and compromise scalability. In this blog post, we dive into how unevictable workloads challenge Kubernetes autoscaling and how ScaleOps’ optimized pod placement capabilities bring new efficiency to clusters through intelligent automation.

Kubernetes is hailed for its scalability and flexibility, but managing its resources effectively can be a challenge that results in unnecessary costs. As cloud-based applications scale, organizations often overlook hidden inefficiencies that waste resources, leading to inflated bills. This blog post highlights best practices for optimizing Kubernetes resources to ensure cost efficiency.

In the world of Kubernetes, managing resources efficiently is key to optimizing performance and minimizing costs. ScaleOps streamlines this process by automating resource allocation, allowing platform teams to focus on innovation while providing product teams with the insights they need to optimize their applications. This post explores how ScaleOps simplifies Kubernetes resource management, enhances platform engineering, and ensures secure, efficient operations.