What Is Kubernetes Cost Optimisation?

Kubernetes has become the default platform for running containerized applications, with 82% of organisations now operating it in production. That scale brings a hidden problem: cloud bills that climb faster than anyone budgeted for. Clusters pool compute across many workloads, which makes it genuinely hard to see where the money goes.

Most teams find out the same way. A finance review flags a Kubernetes line item that doubled, and nobody can explain why.

Kubernetes cost optimisation is the practice of reducing cloud spend across Kubernetes clusters without degrading performance, reliability, or developer speed. It combines rightsizing, autoscaling, smarter compute purchasing, cost visibility, and storage efficiency to close the gap between what you provision and what you actually use.

This guide breaks down where Kubernetes costs come from, the strategies that bring them under control, and one cost centre that many teams overlook: persistent storage.

Why Kubernetes costs spiral out of control

Before fixing the problem, you need to understand how the waste accumulates. Most of it traces back to a handful of structural causes.

Compute overprovisioning

This is the single largest cost driver in most clusters. When developers set resource requests and limits for a pod, they pad those numbers to avoid the risk of a workload running short under load. The padding never gets revisited. The result is clusters running at a fraction of their reserved capacity while the cloud provider bills for every reserved core.

The scale of this gap is striking. According to CAST AI's 2025 benchmark, clusters use only 10% of the CPU and 23% of the memory allocated to them, on average. Datadog's research points in the same direction, finding that over 65% of the containers it monitors use less than half of their requested CPU and memory.

The visibility gap

Kubernetes hides cost drivers by design. Your cloud bill shows virtual machine instances, not which namespace, team, or service is responsible for them. Without a way to break spending down to the pod or namespace level, optimisation turns into guesswork. Datadog's State of Cloud Costs report found that 83% of container costs are tied to idle resources, a number that stays invisible until someone maps spend to actual usage.

Data transfer and network fees

Pods that talk to each other across availability zones or regions generate per-gigabyte transfer charges in each direction. On a busy cluster moving terabytes between zones, these fees can add up quietly and rarely show up in anyone's optimisation plan until the bill arrives.

Idle non-production environments

Development and staging clusters often run 24X7, even though they're only used during working hours. A non-production environment provisioned to match production and left running over nights and weekends wastes resources for roughly three-quarters of the week.

Core Kubernetes cost optimisation strategies

Bringing Kubernetes spend under control comes down to a few high-impact practices. Some require process changes; others are architectural.

Rightsize requests and limits

Rightsizing means matching each workload's resource requests to what it actually consumes in production. The goal is to set requests based on observed usage patterns rather than worst-case guesses. Profile a workload over time, look at its real CPU and memory consumption at the 95th percentile to account for legitimate spikes, and adjust requests to fit. This one practice directly attacks the overprovisioning that drives most waste.

A common mistake is rightsizing once and walking away. Workloads change as code ships, so requests drift out of alignment within weeks. Treat rightsizing as a recurring review, not a one-time cleanup.

Apply autoscaling

Autoscaling keeps a cluster sized to current demand instead of permanent peak demand. Three mechanisms work together:

• Horizontal Pod Autoscaler (HPA): Adds or removes pod replicas based on CPU, memory, or custom metrics, absorbing demand spikes without static peak provisioning.
• Vertical Pod Autoscaler (VPA): Adjusts the CPU and memory requests of individual pods based on observed usage, catching the chronic overprovisioning that rightsizing targets.
• Cluster Autoscaler (or Karpenter on AWS): Adds and removes nodes to match scheduling needs, so you stop paying for empty capacity.

Used together, these keep the cluster continuously right-sized. The caveat: Autoscaling is set-and-observe, not set-and-forget. Scaling parameters need periodic review to stay effective.

Buy compute strategically

Purchasing decisions alone can reshape a Kubernetes bill. Most cloud providers offer three pricing models beyond standard on-demand rates:

• Spot instances trade availability for deep discounts, suiting interruptible workloads like batch jobs, CI/CD pipelines, and stateless services. CAST AI found that clusters partially running on spot capacity cut compute costs by 59% on average, and clusters running entirely on spot achieved a 77% reduction.
• Reserved instances and savings plans offer lower rates in exchange for a one- or three-year commitment, ideal for predictable baseline workloads.
• On-demand covers the unpredictable remainder.

The art is matching each workload to the right purchasing model rather than running everything on demand.

Establish cost visibility and allocation

You can't optimise what you can't measure. Cost allocation breaks the cloud bill down past the provider's line items into clusters, namespaces, labels, and pods, then maps that spend to teams, products, or customers. Once engineers see the cost of their own workloads, overprovisioning is likely to decline without anyone mandating it. Visibility is the foundation every other strategy depends on.

Comparing Kubernetes cost optimisation approaches

No single strategy solves the problem. Each addresses a different source of waste, with its own effort and payoff profile.

The right mix depends on your workloads. A cluster of stateless microservices benefits most from spot and autoscaling. A cluster backing databases and data pipelines needs storage optimisation—an area many cost guides overlook.

The overlooked cost centre: Persistent storage

Nearly every Kubernetes cost discussion focuses on compute. That makes sense since compute is the biggest line item. But as stateful workloads move onto Kubernetes—databases, analytics, AI pipelines, and other data-intensive applications—storage can become a significant cost centre that teams often overlook.

The reason it gets missed is that storage waste is quieter than compute waste. An idle node is visible; a stranded volume quietly adds to the bill.

Orphaned and overprovisioned volumes

When a pod is deleted, its persistent volume does not always go with it, depending on the reclaim policy. Over time, clusters accumulate orphaned volumes that no one owns or deletes, each continuing to incur storage charges for data no workload uses. Persistent volumes are also frequently sized for headroom that never gets consumed, the storage equivalent of compute overprovisioning.

Storage tiering

Not all data needs premium performance storage. Logs, backups, and cold data sets sit happily on lower-cost tiers, while only active transactional data justifies high-performance media. Matching each data set to an appropriate storage class, and moving data between tiers as it ages, keeps you from paying top rates for data nobody touches.

Snapshot sprawl

Volume snapshots are cheap to create and easy to forget. Automated snapshot schedules without retention limits produce thousands of snapshots that quietly accumulate storage charges. Setting retention policies that match actual recovery requirements can help prevent the buildup.

For clusters running data-intensive workloads, addressing storage can uncover savings that compute-focused optimisation may miss. Teams that manage costs holistically treat storage as a first-class cost centre, not an afterthought.

Best practices for sustainable cost control

Lasting cost control comes from process, not one-off cleanups. These practices can help keep spend in check as clusters grow:

1. Make cost visible to engineers. Surface the cost of each team's workloads in the tools they already use, such as dashboards and pipeline checks. Awareness can help change behavior faster than policy alone.
2. Treat cost as an engineering metric. Measure it alongside latency and uptime, set thresholds, and review trends in retrospectives. There's no universally "good" cost, only spend that is intentional versus spend that is accidental.
3. Rightsize on a schedule. Revisit requests and limits monthly, since workloads drift as code changes.
4. Automate scaling, then observe it. Configure HPA, VPA, and cluster autoscaling, and review the parameters regularly rather than assuming they stay optimal.
5. Match workloads to purchasing models. Move interruptible work to spot, commit baseline capacity to reserved or savings plans, and use on-demand capacity for the unpredictable remainder.
6. Audit storage quarterly. Hunt for orphaned volumes, oversized claims, and runaway snapshots, and confirm data sits on the right tier.

Common challenges and how to avoid them

Cost optimisation efforts stall for predictable reasons. The first is fear of breaking things. Teams overprovision precisely because they're worried about performance, so any reduction can feel risky. The fix is to base changes on real usage data rather than instinct, which turns a guess into an informed decision.

The second challenge is inconsistent labeling. When pods and namespaces aren't tagged consistently, costs show up as unallocated and optimisation loses its target. Enforcing a labeling standard early makes every later step possible.

The third challenge is treating optimisation as a project rather than a practice. A one-time cleanup delivers a temporary dip, then waste creeps back as workloads change. Building cost review into regular engineering rhythms keeps the savings.

The future of Kubernetes cost optimisation

Things are moving toward automation and earlier intervention. Manual rightsizing and reactive cleanups are giving way to tools that continuously adjust resources in real time based on live usage. As AI and machine learning workloads move onto Kubernetes, the stakes rise: GPU instances cost far more than standard compute, and the same overprovisioning habits applied to them get expensive fast.

Expect cost data to keep moving leftward into the development process, surfacing during deployment rather than weeks later in a finance review. The goal is shifting from cutting bills after the fact to building cost-aware systems from the start. Storage optimisation, long the neglected half of the conversation, will get more attention as stateful and data-intensive workloads become the norm on Kubernetes.

Conclusion

Kubernetes cost optimisation closes the gap between what your clusters reserve and what they actually use. The waste is structural, baked into how Kubernetes pools resources, but it's also addressable through rightsizing, autoscaling, smarter purchasing, clear cost visibility, and disciplined storage management.

The biggest takeaway is that compute is only part of the story. Teams that optimise CPU and memory while ignoring persistent storage leave real savings stranded on the bill. A complete approach treats storage as a first-class cost centre, especially as databases, analytics, and AI workloads move onto Kubernetes.

Start with visibility. Map your spend to namespaces and workloads, find the biggest sources of waste, and address them in order of impact. For the storage side of that equation, Portworx® delivers data services built for Kubernetes, with thin provisioning, automated tiering, and capacity management that keep stateful workloads from quietly inflating your costs. Paired with consumption-based storage through Evergreen//One™, organisations pay for the capacity they use rather than the capacity they guessed they might need, turning storage from a blind spot into a controlled, predictable line item.