Kubernetes Doesn't Have a Cost Problem. Most Teams Have an Operations Problem.
For years, Kubernetes has been marketed as the platform that solves infrastructure at scale. It automates deployments, recovers from failures, scales applications, and provides a consistent environment regardless of where workloads run. Yet talk to enough engineering teams, and you'll hear a very different story. "Our cloud bill doubled." "We're running twice as many worker nodes as expected."…
Kubernetes, marketed as the platform to solve infrastructure challenges at scale, has proven to be expensive for many organizations. The cost isn't inherent to the platform itself, but rather the inefficient operations surrounding it. Engineers often report doubling their cloud bills and needing twice as many worker nodes than anticipated.
Teams report their platform teams spending more time maintaining Kubernetes than improving it. The issue lies in oversizing resources, poor workload scheduling, underutilized nodes, excessive clusters, and autoscaling without proper observability. These are all operational challenges, not platform limitations.
Contrary to popular belief, Kubernetes has a relatively small footprint. The true cost is derived from the applications running within it and their configurations. A typical deployment might request 2 CPU cores and 4GB memory. However, when you look at actual usage, you might find only 0.18 CPU cores and less than 1GB memory being consumed.
The scheduler doesn't know the difference. It reserves resources based on the application's requests, leading to underutilized clusters where half the available compute power remains idle.
Often, teams intentionally overprovision resources, as it's a safety net against potential outages. While understandable, overprovisioning can lead to wasted resources, longer replacement times for pods, delayed cluster upgrades, and less predictable scaling. The best approach is to size infrastructure based on actual demand, not worst-case scenarios.
Kubernetes best practices once recommended defining both CPU requests and limits. However, many teams are now reconsidering this practice. While memory limits are crucial, CPU limits can be problematic. When a workload hits its limit, Linux throttles the process, reducing performance. Some organizations configure realistic CPU requests while avoiding limits for trusted workloads.
But remember, there's no one-size-fits-all Kubernetes configuration. Each decision should be based on actual workload behavior, not generic best practices.
Autoscaling isn't a catch-all solution. It's a tool designed to solve specific issues, like increasing application replicas or adjusting resource recommendations. Scaling infrastructure that's already underutilized merely accelerates inefficiency. Before enabling any autoscaler, assess whether the current cluster is using resources efficiently. If not, autoscaling won't solve the problem.
Instead of focusing on optimization, start with measurement. Every production cluster should answer questions like which deployments consistently over-request CPU, which namespaces consume the most memory, which nodes remain underutilized, which workloads experience frequent restarts, and which applications face CPU throttling. Tools like Prometheus and Grafana are not optional extras, but operational necessities.
The best optimization project is often the one you decide not to undertake because the metrics show there isn't actually a problem.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
