Kubernetes FinOps: Stop Burning Money on Overprovisioned Clusters

Your Kubernetes cluster is probably wasting 70% of what you're paying for. Developers request 2 CPU and 4GB memory 'just in case'. Nobody ever reclaims it. The cluster autoscaler keeps adding nodes. The CFO keeps asking questions.

The Overprovisioning Problem

Kubernetes scheduling is based on requests, not actual usage. If a pod requests 1 CPU but uses 100m, the scheduler still reserves the full CPU. Multiply by hundreds of pods, and you're paying for nodes that are mostly idle.

The root cause is uncertainty. Developers don't know what their apps need, so they guess high. Operations teams don't want outages, so they don't challenge the requests. The safe choice is expensive.

Right-Sizing with Data

The Vertical Pod Autoscaler (VPA) analyzes actual usage and recommends resource settings. It can auto-apply recommendations or just advise. Start in recommendation mode — review the suggestions before letting it resize pods automatically.

Tools like Kubecost and OpenCost show cost by namespace, label, or workload. When you can say 'the billing service costs $500/month and is using 15% of its allocated resources', the conversation changes. Data beats assumptions.

Spot Instances and Preemptible VMs

Spot instances cost 60-90% less than on-demand. The tradeoff is they can be terminated with short notice. For stateless, fault-tolerant workloads, this is a no-brainer. Web servers behind a load balancer? Run them on spot.

The key is graceful handling. Set pod disruption budgets. Use preStop hooks for cleanup. Spread replicas across multiple instance types and availability zones. When one spot pool gets terminated, others keep running.

Bin Packing and Node Selection

Cluster autoscaler adds nodes when pods can't be scheduled. It removes nodes when they're underutilized. But 'underutilized' has a threshold — often 50%. A node at 51% utilization stays even though half its capacity is wasted.

Configure aggressive scale-down policies. Use pod priority to ensure critical workloads survive consolidation. Karpenter (on AWS) is more aggressive than the standard autoscaler — it right-sizes nodes continuously rather than waiting for scale-down conditions.

Namespace Quotas and Governance

ResourceQuotas limit how much a namespace can consume. LimitRanges set defaults and constraints on individual pods. Together, they prevent runaway resource requests and enforce organizational policies.

Showback reports allocate costs to teams or projects. When the team that requested 32 CPUs sees their monthly bill, they become interested in optimization. FinOps is as much about culture as technology.

Kubernetes cost optimization isn't a one-time project — it's a practice. Start with visibility (what are you spending?), add right-sizing (what should you be spending?), then implement governance (how do you stay there?). The 40%+ savings are real and sustainable.

Key Takeaways

• FinOps for Kubernetes applies financial accountability to cloud-native infrastructure — making engineering teams aware of and responsible for their cloud costs.
• The three pillars of Kubernetes FinOps are visibility (who is spending what), optimization (eliminating waste), and governance (enforcing cost policies at admission time).
• Teams that implement FinOps practices alongside cost tooling reduce Kubernetes cloud spend by 30-50% within the first quarter.

Where Kubernetes Costs Accumulate

Kubernetes makes it easy to create resources and hard to find them when they are no longer needed. Persistent volume claims survive after the workloads that requested them are deleted. Load balancers provisioned for a development service remain online after the service is removed. Node pools scaled up for a traffic event never scale back down because the autoscaler minimum was set too high. These invisible costs compound over time and are invisible in standard cloud billing reports.

CPU and memory over-provisioning is the largest driver of waste in most clusters. When developers set resource requests conservatively high — requesting 2 CPU cores for a service that uses 0.3 in production — the scheduler cannot pack workloads efficiently onto nodes. Nodes are provisioned to satisfy requests, not actual usage. Running Vertical Pod Autoscaler in recommendation mode for three weeks reveals the gap between requests and reality in quantitative terms.

FinOps Tools and Practices That Work

Kubecost provides namespace, deployment, and pod-level cost attribution correlated with cloud billing. Teams can see exactly what their services cost and compare costs over time. Showback reports (here is what your team spent this month) change engineering behavior without requiring chargeback infrastructure.

Resource quota enforcement prevents any single team from over-provisioning. Set namespace-level ResourceQuotas that align with team capacity budgets. Require resource requests on all containers through LimitRange defaults. Teams quickly learn to right-size requests when they have a quota ceiling.

Our Kubernetes consulting practice has helped enterprises across Texas and California implement FinOps programs that cut Kubernetes cloud costs by 35% on average. Schedule a cost audit.