# Balanced Cluster Autoscaler Profile on Cost-Sensitive GKE Clusters Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/balanced-cluster-autoscaler-profile-on-cost-sensitive-gke-clusters The GKE cluster autoscaler has autoscaling profiles that decide how eagerly it removes nodes. By: PointFive Updated: 2026-09-28 [Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) The short version The GKE cluster autoscaler has autoscaling profiles that decide how eagerly it removes nodes. PointFive Research Cloud cost research at PointFive GCP service [GCP GKE](https://www.pointfive.co/efficiency-hub/cloud-services/gcp-gke) Category [Compute](https://www.pointfive.co/efficiency-hub/service-category/compute) Reference CER-0498 Type Inefficient Configuration ## Explanation Why the waste happens and who it affects. Standard clusters default to the balanced profile, which Google describes as prioritizing keeping more resources readily available for incoming Pods. The optimize-utilization profile instead prioritizes utilization over spare capacity: the autoscaler removes more nodes, removes them faster, and prefers scheduling Pods onto nodes that are already allocated. Because balanced is the default and the profile is set per cluster, batch, CI, data processing and development clusters inherit a setting designed to keep headroom for fast Pod start-up. On those clusters, nodes that finished their work linger as underused VMs before the autoscaler reclaims them, and Pods stay spread across more nodes than necessary. Standard node pools bill for every node VM until it is deleted, so slower scale-down shows up directly as Compute Engine spend. ## Billing model The pricing dimensions that drive this cost. Standard node pool compute Nodes are billed as Compute Engine instances, per second with a one-minute minimum, until the nodes are deleted Balanced profile The default for Standard clusters; keeps more spare capacity so incoming Pods start sooner, at the cost of more billed nodes Optimize-utilization profile Scales down more aggressively and packs Pods onto already allocated nodes, reducing billed node-hours ## How to detect 5 checks to find it in your estate. - Read autoscaling.autoscalingProfile for each Standard cluster (for example with gcloud container clusters describe) and list clusters that are on BALANCED or unset - Prioritize clusters that run batch jobs, CI runners, scheduled data processing or development workloads, where Pod start-up latency is not user facing - Check the GKE Clusters page Notifications column and the Recommendations page for cost savings hints on allocated versus requested resources - Query the container.googleapis.com/cluster-autoscaler-visibility log for scaleDown and noScaleDown events to see how quickly nodes are removed and what blocks removal - Compare node allocatable capacity with the sum of Pod requests per node pool over time, using Cloud Monitoring or GKE cost allocation, to find persistently underused nodes ## How to fix 5 ways to remove the waste. - Switch cost-sensitive and latency-tolerant clusters to the optimize-utilization profile with gcloud container clusters update CLUSTER\_NAME --autoscaling-profile optimize-utilization - Expect the tradeoff: fewer spare nodes means some new Pods wait for a node to be provisioned, so keep balanced on clusters whose serving workloads need fast scale-up - Run batch workloads on dedicated node pools using labels, taints and tolerations so empty nodes can be removed as soon as jobs finish, as Google's GKE cost guidance recommends - Move Pods that block scale-down, such as kube-system Pods without a PodDisruptionBudget, Pods without a controller, or Pods annotated safe-to-evict false, onto separate node pools or fix their configuration - If a buffer is needed for spikes, use low-priority pause Pods sized for the expected burst instead of relying on the balanced profile to keep idle nodes around ## Documentation Vendor references for pricing and configuration. - [About GKE cluster autoscaling docs.cloud.google.com](https://docs.cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler) - [Best practices for running cost-optimized Kubernetes applications on GKE docs.cloud.google.com](https://docs.cloud.google.com/architecture/best-practices-for-running-cost-effective-kubernetes-applications-on-gke) - [View cluster autoscaler events docs.cloud.google.com](https://docs.cloud.google.com/kubernetes-engine/docs/how-to/cluster-autoscaler-visibility) - [Google Kubernetes Engine pricing cloud.google.com](https://cloud.google.com/kubernetes-engine/pricing) ## Related inefficiencies [Browse the library](https://www.pointfive.co/efficiency-hub) - GCP GKE CER-0287 ### [Spot-Only GKE Capacity Without Standard Fallback](https://www.pointfive.co/efficiency-hub/inefficiencies/spot-only-gke-capacity-without-standard-fallback) Workloads are constrained to run only on Spot-based capacity with no viable path to standard nodes when Spot capacity is reclaimed or unavailable. While Spot reduces unit cost, rigid dependence can create hidden costs by requiring standby... Compute - GCP GKE CER-0193 ### [Orphaned and Overprovisioned Resources in GKE Clusters](https://www.pointfive.co/efficiency-hub/inefficiencies/orphaned-and-overprovisioned-resources-in-gke-clusters) As environments scale, GKE clusters tend to accumulate artifacts from ephemeral workloads, dev environments, or incomplete job execution. PVCs can continue to retain Persistent Disks, Services may continue to expose public IPs and... Compute - GCP GKE CER-0270 ### [Orphaned Kubernetes Resources in GKE](https://www.pointfive.co/efficiency-hub/inefficiencies/orphaned-kubernetes-resources) In GKE environments, it is common for unused Kubernetes resources to accumulate over time. Examples include Persistent Volume Claims (PVCs) that retain provisioned Persistent Disks, or Services of type LoadBalancer that continue to front... Compute --- Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.