kubernetes
62 articles on kubernetes — what drives the cost, how it is priced, and where the savings actually are.
EKS vs AKS vs GKE cost comparison: what the three managed clusters actually cost
The three big managed Kubernetes services charge similar control-plane fees but diverge sharply on node pricing, load balancers, and free tiers. A like-for-like cluster can differ by 15 to 20 percent across them. Here is the comparison with real numbers.
Kubernetes control plane fees: what you pay just to have a cluster
Before a single pod schedules, most managed Kubernetes clusters bill a flat hourly fee for the control plane. It is small per cluster and enormous across a fleet of fifty. Here is what each provider charges and how to stop paying it fifty times.
Kubernetes node pool sizing for cost: big nodes, small nodes, and the waste between
Node size is one of the highest-leverage cost decisions in Kubernetes and most teams pick by habit. Too small and system overhead eats your capacity, too big and a single pod strands half a node. Here is how to size node pools with real numbers.
Kubernetes bin packing: turning 35 percent utilization into 70 percent
Most Kubernetes clusters run at 25 to 40 percent CPU utilization, which means well over half the node bill buys nothing. Bin packing is the discipline of fitting pods onto fewer nodes. Here is how the scheduler decides and what to change.
Kubernetes overprovisioning waste: the 3x gap between requested and used
Across most clusters, pods request roughly three times the CPU they actually consume, and the cluster buys nodes for the requests. That gap is the largest single line of avoidable Kubernetes spend. Here is where it comes from and how to close it.
Kubernetes persistent volume cost: the storage bill nobody reads
PersistentVolumeClaims quietly provision real cloud disks, and those disks keep billing long after the pod is gone. Storage class choice, over-sized claims, and orphaned volumes routinely add 15 to 25 percent to a cluster bill. Here are the numbers.
Kubernetes load balancer cost per service: why 40 Services cost $1,000 a month
Every Service of type LoadBalancer provisions a real cloud load balancer with its own hourly fee. Teams that expose each microservice this way pay hundreds per month for routing that one ingress controller would handle. Here is the math and the fix.
HPA vs VPA cost impact: which autoscaler actually lowers your Kubernetes bill
Horizontal and Vertical Pod Autoscalers solve different problems and have opposite cost behaviours. HPA can raise your bill while improving latency, VPA usually lowers it by shrinking requests. Here is how each affects the node count you pay for.
Kubernetes GPU node pool cost: why one idle A100 costs more than 30 web servers
GPU nodes cost 10 to 40 times a general-purpose node per hour, and Kubernetes gives you a whole GPU per pod by default. Idle GPU nodes are the most expensive waste in any cluster. Here are the rates and the sharing strategies.
Kubernetes multi-tenancy cost isolation: quotas, limits, and who pays for the noisy neighbour
Shared clusters are far cheaper than cluster-per-team, but only if one team cannot consume the capacity everyone else paid for. ResourceQuotas, LimitRanges, and priority classes are the controls that make shared infrastructure fair. Here is how to set them.
Kubernetes sidecar overhead cost: the 30 percent tax you never budgeted for
Every sidecar container multiplies across every pod in the cluster. A 100m CPU and 128 MB sidecar on 800 pods is 80 vCPU and 100 GB of pure infrastructure. Here is how to measure the tax and decide which sidecars earn their keep.
Agentless Kubernetes cost visibility: pricing clusters without installing anything
In-cluster cost agents need deployment, upgrades, RBAC, and their own compute, and they only report after money is spent. Reading cost straight from Terraform gives you the number before the cluster exists. Here is the tradeoff.
Kubernetes cluster consolidation: merging 20 clusters into 4 and what it saves
Clusters multiply one team at a time until a fleet of twenty exists with no one who chose it. Each carries a control plane fee, a system-pod floor, and its own idle headroom. Consolidation routinely removes 50 to 70 percent of fixed cluster cost.
Shutting down dev and staging Kubernetes clusters at night: a 65 percent cut
Non-production clusters run 168 hours a week and are used for about 45. Scaling node pools to zero outside working hours is the least controversial saving in Kubernetes. Here is what it saves and what breaks.
Why Kubernetes nodes never scale down, and the six settings that fix it
Clusters scale up reliably and scale down almost never, which is why node counts only ever ratchet upward. The causes are a short list of blockers, most of them fixable in an afternoon. Here is the diagnosis and the tuning.
The right spot to on-demand node mix for Kubernetes: how far can you push it
Spot nodes cost 60 to 90 percent less but can disappear with two minutes of warning. The question is not whether to use them but what percentage of the cluster they should be. Here is how to choose the ratio by workload.
Kubernetes cross-zone traffic cost: the $0.02 per GB nobody attributes
Spreading pods across availability zones is good for resilience and expensive for chatty microservices. Every cross-zone hop is billed in both directions. Here is how much it adds and how topology aware routing removes most of it.
Kubernetes CronJob and batch cost: the workloads that quietly keep nodes alive
Batch jobs and CronJobs look free because they finish. In practice they hold nodes open, block scale-down, and provision capacity for a peak that lasts four minutes an hour. Here is how to make batch nearly free.
Model serving autoscaling cost: why scaling GPUs is not like scaling web servers
A web server scales in seconds. A GPU replica loading a 40GB model takes minutes. That gap forces you to keep warm capacity, and warm capacity is where most model serving budgets quietly go.
Multi-model serving cost: packing models onto fewer GPUs
Running twelve models on twelve GPUs is simple and expensive. Packing them onto three, with time-slicing or partitioning, can cut the serving bill by 70 percent when each model only needs a fraction of a device.
Vertex AI vs GKE for GPU workloads: comparing the two GCP paths
On Google Cloud you can run models on Vertex AI or on your own GKE cluster with GPU node pools. The hardware rates are similar, so the decision comes down to cluster fees, discount eligibility, and who operates it.
Self-hosted observability stack cost: the real total for Prometheus, Grafana, and Loki
Running your own monitoring looks free because the software is. Adding up the instances, storage, replication, and engineer time gives a number you can actually compare against a managed bill. Here it is.
Per-host vs per-GB monitoring pricing: which model punishes your architecture
The same workload can cost five times more under one pricing model than the other, and the deciding factor is your container density and telemetry per host. Here is how to work out which side you are on.
OpenTelemetry Collector cost: sizing the pipeline that sits between everything
The collector is infrastructure you now run, and it is also the cheapest place to delete data before it reaches a per-GB meter. Sizing it correctly is a small cost that unlocks a large saving.