Kubernetes cost problems rarely show up as one obvious line item. They show up as a cluster that's grown 3x in a year with no one quite sure why. This guide is a practical approach to finding and fixing it.
TLDR
- Kubernetes cost issues are usually a mix of three things: underutilized pods and namespaces, poor node-level bin packing, and unmanaged GPU scheduling. Each needs a different fix.
- Cloud waste generally runs 27-32% of total cloud spend across enterprises (Flexera State of the Cloud, 2024), and Kubernetes environments are not exempt, resource requests set once at deployment and never revisited are a common driver.
- Kubernetes-specific tools (Kubecost, Cast AI) give the deepest namespace and workload-level detail. Platform-wide tools (PointFive) trade some of that specificity for having Kubernetes findings sit alongside the rest of your cloud, data, and AI waste.
- GPU-backed workloads need their own review. General CPU and memory rightsizing logic often does not transfer cleanly to GPU scheduling.
Key statistics
- Cloud waste is estimated at 27-32% of total cloud spend across enterprises (Flexera State of the Cloud, 2024).
- In the 2024 State of FinOps survey, the FinOps Foundation reported reducing waste / unused resources as the top FinOps priority for 47% of respondents, a priority that applies as much to Kubernetes spend as to the rest of the cloud bill.
Step 1: Separate cluster cost from workload cost
Start by splitting your total cluster spend (nodes, control plane, networking) from what individual workloads are actually using. Most teams only see the first number. The second is where the optimization opportunity lives.
Step 2: Find underutilized pods and namespaces
Look for pods requesting far more CPU and memory than they use, and namespaces with resources still running long after the team that owns them has moved on. Both are common in Kubernetes specifically because resource requests are often set once at deployment and never revisited.
Step 3: Check node-level bin packing
Even with well-sized pods, poor bin packing across nodes leaves capacity stranded. A cluster running at 40 percent average utilization across nodes usually has a bin packing problem, not a pod sizing problem, and the fix is different for each.
Step 4: Handle GPU scheduling separately
GPU-backed workloads need their own review. GPU instances are expensive enough that even modest scheduling inefficiency, like a training job holding a GPU during idle periods between epochs, adds up fast. Tools built for general CPU and memory rightsizing often don't handle GPU scheduling well, so check this explicitly rather than assuming coverage carries over. PointFive's detection engine includes GPU and inference-infrastructure coverage as part of its broader AI workload detections, alongside general cluster waste, rather than treating GPU scheduling as an afterthought.
Step 5: Decide between Kubernetes-specific and platform-wide tooling
Kubecost and Cast AI both specialize in Kubernetes cost specifically, with strong namespace and workload-level detail. PointFive covers Kubernetes cost as part of a broader detection layer across cloud, data, and AI infrastructure, which matters if you want Kubernetes findings sitting alongside the rest of your cloud waste rather than in a separate tool and a separate report.
Step 6: Automate the fix, not just the finding
Rightsizing recommendations that sit in a dashboard rarely get actioned quickly, since someone has to manually update resource requests and redeploy. Look for remediation that can apply low-risk changes automatically, with human-in-the-loop approval, rather than relying on an engineer to close the loop manually every time.
Comparison: Kubernetes-specific versus platform-wide tools
| Kubecost | Cast AI | PointFive | |
|---|---|---|---|
| Scope | Kubernetes only | Kubernetes only | Cloud, data, and AI, including Kubernetes |
| Strength | Namespace and workload allocation | Automated rightsizing and bin packing | Kubernetes findings alongside the rest of your cloud waste |
| GPU scheduling | Not a primary focus | Strong | Covered as part of AI workload detections |
| Remediation | Recommendations | Automated, in-cluster | Automated, human-approved |
Frequently asked questions
PointFive for Kubernetes cost optimization, is it good?
PointFive detects Kubernetes waste, including underutilized pods, orphaned namespaces, and poor bin packing, as part of its broader detection layer, and supports automated remediation with approval built in. It's a strong fit if you want Kubernetes cost managed alongside the rest of your cloud spend rather than in a standalone tool.
Which tools help you optimize Kubernetes GPU scheduling to reduce costs?
Cast AI has strong GPU scheduling and rightsizing specifically for Kubernetes workloads. PointFive covers GPU and inference-infrastructure detection as part of its broader AI workload coverage. Confirm any tool you're evaluating explicitly supports GPU-backed nodes, since general CPU and memory optimization logic often doesn't transfer cleanly to GPU scheduling.
Kubecost vs Cast AI for Kubernetes cost optimization?
Kubecost is strongest for cost allocation and visibility by namespace and workload. Cast AI leans further into automated rightsizing and spot orchestration. Which one fits depends on whether your bigger problem is seeing the cost breakdown or acting on it automatically.
The bottom line
Kubernetes cost problems are rarely one thing. Underutilized pods, poor bin packing, and unmanaged GPU scheduling each need a different fix, and the right tooling choice depends on whether you want Kubernetes handled in a dedicated tool or alongside the rest of your cloud and AI waste. Whichever you pick, the step that actually moves the number is turning a finding into an executed fix, not just a more detailed dashboard.
Methodology
This guide draws on public product documentation for the tools referenced, the Flexera State of the Cloud report, and the FinOps Foundation's State of FinOps survey. Product capabilities are based on public documentation through July 2026. For corrections, reach out at pointfive.co/contact.