Efficient autoscaling: Keeping performance, reliability, and cost in mind with open source projects

Kubernetes autoscaling comes with trade-offs Performance Cost Reliability It’s all about trade-offs Author bio Posted on October 16, 2025 by Christian Melendez, AWS CNCF projects highlighted in this post During ContainerDays in Hamburg, Kelsey Hightower posed a simple but powerful question: “Why are we still talking about containers?” His point resonated with me deeply — even in the AI era, the cloud-native community is still refining the fundamentals of container orchestration, scalability, and efficiency. In this post, I’ll explore how open source projects like KEDA and Karpenter can help you balance performance, reliability, and cost in Kubernetes autoscaling. When we talk about Kubernetes autoscaling , it’s not just about adding replicas or nodes when demand grows and removing them when it shrinks. You have to balance performance , reliability , and cost — three forces that constantly pull against each other. The way I like to think about these three pillars is as a triangle , like in the following figure. These three pillars create natural tension. Before your application’s performance degrades, you need to add resources — which increases cost. To save on cost, you scale down resources — which can impact reliability. So how do we find the right balance? Let’s explore tools and recommendations for each pillar. Before you start scaling, you need to understand what truly impacts the performance of your application — what matters to your users and stakeholders. Most of the time, they don’t care about CPU or memory usage directly. These may be indicators, but they don’t always tell the full story.