cost optimization
798 articles on cost optimization — 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 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.
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.
H100 vs A100 cost per training hour: which GPU is actually cheaper
An H100 costs roughly three times an A100 per GPU-hour, but it can be two to four times faster on modern training workloads. The cheaper GPU is the one with the lower cost per unit of work, not the lower hourly rate. Here is how to do that math.
L4 vs T4 GPU inference cost: the quiet upgrade that pays for itself
The T4 has been the default cheap inference GPU for years, but the L4 costs about 53 percent more per hour while delivering two to three times the throughput on modern models. For inference fleets, that inversion is worth real money.
Self-hosted LLM vs hosted API: where the cost crossover actually is
A hosted API charges per token with no floor. A self-hosted model charges per GPU-hour whether you use it or not. The crossover sits at a specific monthly token volume, and most teams guess it wrong by an order of magnitude.
pgvector vs a managed vector database: the real cost comparison
Adding pgvector to a Postgres instance you already run looks free. A managed vector service starts at several hundred dollars a month. The honest comparison involves index memory, replica sizing, and how much your team wants to operate.
Fine-tuning cost estimation: what a training run really costs
Fine-tuning cost is a product of four numbers: model size, dataset tokens, GPU rate, and how many times you will redo it. Get those on paper and a run that felt unbounded turns into a figure you can approve.
GPU spot interruption economics: when a 70 percent discount is worth the risk
Spot GPUs cost 50 to 70 percent less and can vanish with two minutes of warning. Whether that trade is good depends on one number: how much work you lose per interruption, which is a function of your checkpoint interval.
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.
GPU utilization waste: the most expensive idle resource in your account
A GPU at 25 percent utilization is not 25 percent efficient, it is 75 percent wasted at ten dollars an hour. Measuring real utilization usually reveals the largest single optimization available in an AI budget.