Kubernetes cost optimization that sticks.
Cut Kubernetes waste at the source: rightsize requests and limits, tune autoscaling, put the right workloads on Spot, pack nodes properly and make spend visible per team. FinOps engineered into the platform, so savings hold instead of creeping back next quarter.
For teams whose cluster bill has outgrown its usefulness.
Kubernetes cost optimization earns its keep when spend is climbing faster than load and nobody can explain exactly why. These are the situations where FinOps for Kubernetes pays for itself, whether you are a global scale-up or an established business running a mature estate.
Spend rising faster than traffic
The invoice grows every month while usage is flat. That gap is almost always reserved-but-idle capacity rather than real demand.
Requests set by guesswork
Requests and limits were copied from an old manifest and never revisited. Nodes look busy on paper and near-empty in reality.
No idea who spends what
One cluster, many teams, a single opaque bill. There is no showback, so no owner feels the cost of their own workloads.
Autoscaling that does not scale down
Pods scale up under load but never scale back, and nodes stay warm long after the spike, so you pay peak rates around the clock.
On-demand for everything
Fault-tolerant, stateless workloads run entirely on on-demand capacity because Spot felt risky and nobody had time to design it safely.
Finance asking hard questions
Leadership wants the cloud bill defended line by line. You need unit economics and per-team numbers, not a single lump sum.
Measure first, cut at the source, then govern.
Blunt cost-cutting breaks reliability and the savings evaporate within a quarter. We optimise from evidence: measure real utilisation, remove waste at its root in requests, replicas, nodes and storage, and then wire in governance so spend stays right-sized. Every change is watched against latency and error budgets, so nothing gets cheaper at the cost of the workload.
Principles we hold to on every engagement.
- Utilisation drives decisions. We rightsize from measured CPU and memory, not from the numbers someone typed a year ago.
- Reliability is a constraint, not a casualty. Pod Disruption Budgets, headroom and graceful handling come before any Spot or scale-down change.
- Savings must be defensible. Every recommendation is tied to visibility so the impact is measurable, not asserted.
- Governance beats heroics. Guardrails in templates and CI stop waste returning, so you are not repeating a cleanup every quarter.
- Everything ships as code in your repositories, reviewed like any other change, so your team owns the outcome.
What a cost optimization engagement covers.
Waste assessment
A utilisation audit across requests, limits, replicas, nodes, persistent volumes and egress, ranked by how much reserved-but-idle capacity each is holding.
Rightsizing
Request and limit tuning driven by Vertical Pod Autoscaler recommendations and usage percentiles, with headroom sized to the workload rather than to fear.
Autoscaling tuning
HPA on the right signals, KEDA for event-driven scale-to-zero, and Cluster Autoscaler or Karpenter for node scale-down and consolidation.
Spot & capacity strategy
Spot and preemptible node pools for fault-tolerant workloads, with graceful draining, disruption budgets and an on-demand baseline for the rest.
Bin-packing & topology
Node-pool design, topology spread, affinity and consolidation so pods pack tightly and idle nodes are drained instead of kept warm.
Cost visibility & governance
OpenCost or Kubecost, a labelling convention, showback or chargeback, namespace budgets and CI guardrails so savings do not regress.
Cost optimization is one engagement within our Kubernetes consulting practice. It builds on the autoscaling foundations we set up during Kubernetes migration and pairs naturally with the self-service guardrails delivered through platform engineering.
Our Kubernetes cost optimization process.
Four phases, from measured waste to governed, defensible spend. The architecture review confirms where the money actually goes before anything changes.
Measure
Instrument utilisation and allocate cost with OpenCost or Kubecost, exposing where reserved capacity, idle nodes and egress are hiding.
Rightsize & repack
Tune requests, limits and replicas from real usage, then repack nodes so bin-packing improves and idle capacity is drained.
Autoscale & Spot
Wire HPA, KEDA and Karpenter for scale-down and consolidation, and move fault-tolerant workloads onto Spot with graceful handling.
Govern
Namespace budgets, CI guardrails, showback and alerts so the savings hold and regressions are caught in days, not invoices.
The tools we actually use.
No reseller agreements and no partner quota. Recommendations follow your workloads and your spend profile. Typical building blocks on a cost engagement:
Rightsizing
Vertical Pod Autoscaler in recommendation mode, Goldilocks and usage percentiles to set requests and limits that match reality.
Workload autoscaling
Horizontal Pod Autoscaler on custom and external metrics, and KEDA for event-driven and scale-to-zero workloads.
Node autoscaling
Karpenter for node auto-provisioning and consolidation, or Cluster Autoscaler, choosing instance types and sizes that pack tightly.
Spot & capacity
Spot and preemptible node pools, diversified across instance types and zones, with Pod Disruption Budgets and graceful termination handling.
Cost visibility
OpenCost or Kubecost for allocation, a consistent label taxonomy, and Prometheus and Grafana surfacing spend beside the usual metrics.
Governance as code
Policy checks in CI, default resource templates, and namespace budgets with alerts, all version-controlled alongside your manifests.
Kubernetes cost optimization FAQ.
Where does Kubernetes overspend usually come from?
Most Kubernetes waste is structural rather than dramatic. The biggest sources are over-provisioned requests and limits that reserve far more CPU and memory than pods ever use, idle nodes kept warm because bin-packing is poor, deployments over-replicated for a peak that rarely arrives, oversized or orphaned persistent volumes, and cross-zone or internet egress nobody is watching. Individually each looks small; together they inflate the bill by a large margin because you pay for reserved capacity, not for what actually runs.
How do you rightsize workloads safely?
We start from real usage. Actual CPU and memory consumption is measured over a representative window, then the Vertical Pod Autoscaler in recommendation mode and historical percentiles suggest requests and limits that fit the workload with sensible headroom. Changes roll out gradually, watched against latency and error budgets, so nothing gets throttled or OOM-killed. Rightsizing is never a one-off guess: it is tied to observability so the numbers track the workload as it changes.
Is running on Spot instances safe for production?
Yes, for the right workloads and with the right handling. Stateless, replicated and fault-tolerant services run well on Spot or preemptible capacity, provided you spread across instance types and zones, respect Pod Disruption Budgets, and handle termination notices with graceful draining. We keep a baseline of on-demand or reserved capacity for workloads that cannot tolerate interruption, and let autoscalers fall back automatically when Spot is reclaimed, so resilience is designed in rather than hoped for.
How do you keep costs from creeping back up?
Savings regress without guardrails, so we make cost a governed part of delivery rather than a quarterly cleanup. That means default resource requests and limits in templates, policy checks in CI that flag oversized or unbounded workloads, per-namespace budgets with alerts when a team trends over, and cost surfaced in the same dashboards engineers already use. When a regression shows up it is caught in days, not discovered in the next invoice.
Do you give us cost visibility per team or service?
Yes. With OpenCost or Kubecost and a consistent labelling convention, spend is allocated down to namespace, workload, team and service, including shared and idle costs. That enables real showback or chargeback: each team sees what it consumes, unit economics like cost per request or per tenant become measurable, and conversations about spend move from a single opaque bill to numbers each owner can act on.
Want your cluster spend right-sized and defensible?
Start with the readiness scorecard, or book a free 30-minute architecture call. A senior engineer reviews your cluster and returns a ranked list of where the waste is and what it takes to remove it.