Requests are guessed once
Teams set CPU and memory requests during launch, then forget them. Months later, nodes are full on paper and empty in reality.
All servicesEKS, AKS and GKE
Cut Kubernetes cost without starving production workloads.
Kubernetes waste hides inside requests, limits, idle nodes, oversized node pools and missing ownership. We tune EKS, AKS and GKE so clusters scale to real demand, teams understand their spend and reliability stays protected.
The symptoms behind the search
Autoscaling stops at the node
Cluster autoscaling helps, but it cannot fix poor pod sizing, bad node pools, missing disruption budgets or workloads that cannot move.
Nobody sees service cost
Cloud bills show clusters and nodes. Engineering needs namespace, service, team and environment cost to make better choices.
How we approach it
Kubernetes cost is a capacity, scheduling and ownership problem
A Kubernetes bill is spread across nodes, control planes, storage, load balancers, network transfer, observability and shared services. The cloud invoice does not identify which deployment, namespace or team caused the usage, while cluster metrics do not contain the complete provider price.
CloudForge joins billing data with workload metadata and runtime behavior. We establish allocation first, then adjust resource requests, autoscaling, node pools and purchasing models in a sequence that protects service-level objectives and recovery capacity.
The goal is not maximum utilization at every moment. Production clusters need deliberate headroom for deployments, failures and demand spikes. Optimization makes that headroom visible, justified and tested rather than accidental.
Cluster cost allocation
Map direct and shared Kubernetes cost to the services and teams responsible for it.
- Namespace, label and workload allocation
- Idle and shared platform cost models
- Cost per tenant, service or request
Pod rightsizing
Align CPU and memory requests with representative workload behavior without introducing throttling or OOM risk.
- Percentile and peak usage analysis
- Requests, limits and QoS review
- Staged rollout with SLO validation
Workload and node autoscaling
Coordinate HPA, VPA and node provisioning so scaling loops make compatible decisions.
- Demand-aligned HPA metrics
- Cluster Autoscaler or Karpenter tuning
- Node pool simplification and consolidation
Cloud pricing and hidden cost
Improve rates and remove waste outside pod compute while preserving disruption tolerance.
- Spot diversification and fallback
- Commitment coverage for stable capacity
- Storage, network and telemetry optimization
Practical deliverables, not just advice
The output is designed for engineering teams that need to act: roadmaps, controls, dashboards, automation, runbooks and implementation support.
Requests and limits plan
Right-sized CPU and memory settings based on real utilization, not old guesses.
Autoscaling architecture
Cluster autoscaler, Karpenter, HPA, VPA, node auto-provisioning or provider-native scaling tuned to the workload.
Node pool and Spot strategy
Separate pools for steady, bursty, GPU, memory-heavy and interruptible workloads with safe fallback paths.
Kubernetes cost allocation
Kubecost, labels and dashboards that show spend by namespace, service, owner and environment.
Outcomes your team can keep improving
Allocated cluster cost
Product and platform teams see direct, shared and idle cost in the same model.
Credible resource requests
Scheduling and autoscaling decisions reflect measured workload demand.
Better node utilization
Node pools consolidate without breaking placement, recovery or compliance needs.
Continuous control
Dashboards, policy and review cadence prevent waste from quietly returning.
This engagement is a strong fit when
- EKS, AKS or GKE spend cannot be attributed to teams or products
- Requests are copied from templates and nodes remain underutilized
- HPA, VPA or node autoscaling is unstable or difficult to trust
- Spot and commitment savings are desired but disruption risk is unclear
Principles that guide the work
- Allocate cost before asking teams to optimize it
- Use representative peaks and seasonality, not only averages
- Coordinate pod and node scaling as one capacity system
- Measure OOM kills, throttling, pending pods and SLOs beside savings
From first look to handover
Profile the cluster
We inspect nodes, pods, requests, utilization, scaling behavior, workloads, storage and network cost.
Identify safe moves
We separate low-risk waste from changes that need staging, load testing or a rollback plan.
Tune and automate
We implement autoscaling, sizing, node pool and Spot improvements with observability in place.
Hand over cost visibility
Your team gets dashboards, owners and review rituals so clusters stay lean as services grow.
Tools we can work with
Improve the stack you already have
We usually make your current tools cleaner before recommending a switch. The goal is a better operating model, not a shiny tool migration.
- EKS
- AKS
- GKE
- Karpenter
- Cluster Autoscaler
- HPA
- VPA
- Kubecost
- Prometheus
- Grafana
- Datadog
- Terraform
- Helm
Research the decision before the call
Use these practical CloudForge guides to understand the operating model, tradeoffs and next steps connected to this service.
Kubernetes cost optimization playbookA step-by-step guide to allocation, requests, autoscaling, nodes, Spot and hidden cluster costs.FinOps operating modelConnect Kubernetes unit cost to wider cloud ownership and forecasting.SLOs engineers respectProtect reliability while changing workload and node capacity.
Questions
What people ask before we start
It should not. We separate billing-only changes from runtime changes, stage risky moves, and use health checks, disruption budgets and rollback plans.
Ready to turn this into a working plan?
Book a 30-minute call and we will define the fastest path to measurable cloud savings, safer releases or a more reliable platform.