Why Your GKE Cluster Is Costing Too Much — And How to Fix It
GKE is one of the most powerful platforms in the GCP stack. It’s also one of the easiest to overspend on — not because it’s expensive by default, but because the default configuration is optimised for simplicity, not cost efficiency. Most teams provision a cluster, deploy their workloads, and never revisit the cost model until the bill becomes uncomfortable.
I’ve reviewed GKE cost profiles for engineering teams across Canada and the USA. The overspend is almost always concentrated in a small number of patterns — the same ones, repeated across different organisations. Here’s what I find and how I fix it.
Cluster Autoscaler Is Disabled or Misconfigured
This is the root cause of the majority of GKE cost overruns. Without cluster autoscaler, node pools are static — they run at the size they were provisioned at, regardless of actual workload demand.
A SaaS startup I worked with had provisioned their GKE cluster with 8 nodes to handle a product launch. The launch went well, traffic stabilised at a fraction of peak, and the 8-node cluster kept running. Six months later the team was spending significantly more on compute than their workload required, and no one had flagged it because the cluster was “working fine.”
Cluster autoscaler adds and removes nodes based on pending pod scheduling and node utilisation. Combined with Horizontal Pod Autoscaler at the workload level, it means the cluster size tracks actual demand rather than worst-case assumptions.
The fix: enable cluster autoscaler on every node pool with appropriate minimum and maximum node counts. Set minimum to the number of nodes required for your baseline production load — not zero for production, but often 2-3 rather than 8. Let the autoscaler handle peak capacity. For non-production node pools, minimum of 0 is appropriate with autoscaler handling spin-up when needed.
Workloads Are Not Setting Resource Requests and Limits
GKE schedules pods based on resource requests — the CPU and memory a pod declares it needs. Without resource requests, the scheduler has no information to make good placement decisions, and the autoscaler has no basis for deciding whether a node is actually needed.
When pods don’t set resource requests, one of two things happens. Either the scheduler packs pods onto nodes in a way that leads to actual resource contention (pods fighting for CPU, causing performance issues), or nodes appear underutilised to the autoscaler because the scheduled capacity isn’t reflected in resource requests — so the autoscaler doesn’t scale down nodes that are genuinely idle.
The fix: set resource requests and limits on every workload. Use Vertical Pod Autoscaler in recommendation mode to get data-driven suggestions for right-sizing requests based on actual usage patterns. VPA in recommendation mode gives you the data without automatically changing running workloads — you review the recommendations and apply them deliberately.
Node Pool Machine Types Are Not Matched to Workload Profiles
GKE clusters running mixed workloads — some CPU-intensive, some memory-intensive, some small utility services — often end up with a single node pool type that is a compromise between all of them. The result: memory-intensive workloads are constrained, CPU-intensive workloads pay for unused memory, and small utility pods consume full nodes that could host ten times the workload count.
The right architecture is multiple node pools with machine types matched to workload profiles. CPU-optimised machine types for compute-intensive workloads, memory-optimised for in-memory data processing, standard e2 or n2 types for general application pods. Node pool labels and pod nodeSelector or nodeAffinity ensure workloads land on the right pool.
I see this pattern regularly in regulated enterprise environments — a single n2-standard-8 node pool running everything from stateless APIs to batch processing jobs to internal tooling. Separating workloads into purpose-matched node pools reduces both cost and operational complexity.
Non-Production Environments Are Running 24/7
Dev, staging, and QA environments that run continuously are often the biggest cost savings opportunity in a GKE environment — not because the individual clusters are expensive, but because there are typically several of them and they run whether or not anyone is using them.
Non-production GKE clusters don’t need to run overnight, on weekends, or during public holidays. A scheduled Cloud Scheduler job invoking a Cloud Function to scale node pools to zero outside business hours — and back up at the start of the working day — can reduce non-production compute costs by 60% or more with no impact on developer experience.
For teams using GKE Autopilot for non-production, this is even simpler — Autopilot scales to zero automatically when no workloads are running.
Persistent Volume Claims Are Oversized and Never Cleaned Up
Kubernetes PersistentVolumeClaims backed by GCP persistent disks continue to incur storage charges whether or not they are actively used by a running pod. In environments with active development and frequent deployment, PVCs accumulate — old feature branch environments, test workloads, failed jobs — and are never cleaned up.
The fix: audit PVCs in all namespaces and delete those not bound to running pods. Implement a namespace lifecycle policy in development environments — namespaces older than 7 days with no active pods are automatically cleaned up. Set storage resource quotas per namespace to cap PVC creation.
Egress Costs Are Not Being Tracked
GKE workloads that make frequent calls to external APIs, pull container images from external registries, or send data to external destinations generate egress charges that can be significant and are often invisible until they appear on the bill.
The two most common fixable sources: container images pulled from Docker Hub or external registries rather than Artifact Registry (which has no egress charge within the same region), and application code making redundant external API calls that could be cached or batched.
Moving container image storage to Artifact Registry in the same region as the GKE cluster eliminates image pull egress entirely. For application-level egress, Cloud Monitoring can surface workloads with unexpectedly high outbound traffic — worth reviewing before assuming egress costs are fixed.
No Committed Use Discounts on Baseline Node Pools
Production GKE node pools with stable baseline capacity are ideal candidates for committed use discounts. A 1-year CUD on Compute Engine resources (which GKE standard node pools use) provides up to 37% discount on on-demand pricing. A 3-year CUD goes higher.
The common objection: “we don’t know what our baseline will be in a year.” In practice, production baseline node counts are far more stable than teams assume. The baseline is what the cluster needs with autoscaler at minimum — and that number is knowable from 3 months of usage history.
CUDs and autoscaler are not in conflict. You commit on the baseline, pay on-demand for autoscaled nodes above it. The committed portion of your compute is discounted, the burst capacity is flexible.
How I Approach GKE Cost Reviews
A GKE cost review starts with billing export analysis to identify which node pools, namespaces, and workloads are driving spend. From there I look at autoscaler configuration, resource request coverage, node pool machine type alignment, and non-production scheduling. Most reviews surface 3-4 of the patterns above in the first hour.
I work with engineering teams in Toronto, across Canada, and in the USA. Engagements run as standalone cost reviews or as part of a broader GKE platform architecture engagement. More about my background and approach: https://buoyantcloudtech.com/about/
FAQ
Should I use GKE Standard or GKE Autopilot for cost efficiency?
It depends on workload predictability. Autopilot charges per pod resource request rather than per node, which is cost-efficient for variable workloads with good resource requests set. Standard gives you more control over node pool configuration and is typically more cost-efficient for stable, predictable workloads with CUDs. I covered the decision framework in detail at https://buoyantcloudtech.com/gcp-strategic-insights/.
How do I identify which workloads are consuming the most resources in my GKE cluster?
Use kubectl top pods –all-namespaces for a snapshot, or Cloud Monitoring’s GKE workload metrics for trend analysis over time. Billing export to BigQuery with GKE usage metering enabled gives you per-namespace and per-label cost attribution — essential for identifying which teams or services are driving the most spend.
Can I reduce GKE costs without affecting production reliability?
Yes — the fixes above are designed to reduce cost on idle or oversized capacity, not on production headroom. Enabling autoscaler, right-sizing non-production environments, and purchasing CUDs on stable baseline capacity all reduce cost without reducing reliability. The GKE Operational Excellence architecture I use ensures reliability is designed in through multi-zone deployment and PodDisruptionBudgets, not through raw node count.
What is the relationship between GKE cost and the SCALE Framework?
Cost efficiency is addressed by the Elastic Scalability and Lifecycle Ops pillars of the SCALE Framework (https://buoyantcloudtech.com/scale-framework-gcp-architecture/). Elastic Scalability means the platform scales with demand rather than over-provisioning for peak. Lifecycle Ops means cost posture is actively maintained, not set at launch and forgotten.
Related Reading
– The SCALE Framework: https://buoyantcloudtech.com/scale-framework-gcp-architecture/
– GKE Operational Excellence: https://buoyantcloudtech.com/gke-operational-excellence-resilient-workloads/
– GKE Security Hardening: https://buoyantcloudtech.com/gke-security-hardening-case-study/
– 7 Ways to Reduce GCP Cost: https://buoyantcloudtech.com/7-ways-to-reduce-gcp-cost/
– Strategic IaC and Terraform on GCP: https://buoyantcloudtech.com/strategic-iac-terraform-gcp-guide/
– GCP Architecture & Modernization Services: https://buoyantcloudtech.com/cloud-service/google-cloud-architecture-modernization/
Book a Free GCP Architecture Review: https://buoyantcloudtech.com/contact-gcp-consulting/
