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A lightweight, open-source tool for tracking Kubernetes resource usage and costs breakdown per namespace
Ph.D. in Cloud Computing & Founder @ RealOps Labs. Expert in OpenShift Ecosystem & Multi-Cloud Kubernetes. Polyglot Engineer (Go/C++/Python). Building open-source observability tools.
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A reading of the State of FinOps 2026 report, and what it means for the next layer of cloud-native cost intelligence.
Most Kubernetes teams track pod CPU and memory. Almost none track what the cluster actually costs to run. We decided to change that. For the past 6 months, we have been running KubeLedger on our own O
Why we renamed kube-opex-analytics to reflect our focus on accurate ledger-based accounting
Enabling Prometheus Metrics in kube-opex-analytics
On this page
TL;DR: kube-opex-analytics is a new open-source tool designed to help you track resource usage and allocate costs per namespace. It's lightweight, easy to deploy, and features built-in GPU tracking capabilities.
If you're running Kubernetes, you know the drill: Setting requests and limits is an art, and paying the cloud bill is a painful reality.
We've all been there—over-provisioning "just in case," or wondering why the cluster autoscaler is spinning up nodes when half the pods are idle. Efficiency is hard to track without expensive enterprise tools.
That's why I built kube-opex-analytics.
kube-opex-analytics is an open-source tool designed to give you instant visibility into your Kubernetes cluster's resource consumption and cost allocation.
It focuses on what matters most for cost optimization:
Actual Usage vs. Requests: See exactly how much CPU and Memory your namespaces are using compared to what they reserved.
Cost Allocation: Break down costs per namespace based on flexible billing models.
Trends Over Time: visualize hourly, daily, and monthly trends to spot patterns.
GPU Support: First-class citizen support for tracking NVIDIA GPU utilization (because those resources are very expensive!).
Unlike some tools that only show "now," kube-opex-analytics consolidates metrics into hourly, daily, and monthly views. This is crucial for spotting that 3 AM spike or the weekend idle time.
Want to show the Data Science team how much their experiments are costing? The tool allows you to set hourly rates or use a ratio-based cost model to generate accurate cost reports per namespace.
The "Usage Efficiency" view overlays your actual resource usage against your requested capacity. This is your "aha!" moment for right-sizing pods.
With the rise of AI/ML workloads, GPU visibility is non-negotiable. We integrate with the NVIDIA DCGM exporter to show you who is actually saturating those A100s.
You can have it running in your cluster in less than 2 minutes.
Using Kustomize:
kubectl create namespace kube-opex-analytics
kubectl apply -k https://github.com/rchakode/kube-opex-analytics/manifests/kustomize -n kube-opex-analytics
Using Helm:
# Clone the repos
git clone https://github.com/rchakode/kube-opex-analytics.git --depth=1
cd kube-opex-analytics
# Create namespace
kubectl create namespace kube-opex-analytics
# Install with Helm
helm upgrade --install kube-opex-analytics ./manifests/helm -n kube-opex-analytics
# Watch pod status
kubectl get pods -n kube-opex-analytics -w
Once deployed, just port-forward to see the dashboard:
kubectl port-forward svc/kube-opex-analytics 5483:80 -n kube-opex-analytics
Open http://localhost:5483 and start optimizing!
Cloud cost management shouldn't be a luxury. By making this tool open source, I hope to help DevOps engineers and platform teams get the visibility they need without the enterprise price tag.
We are actively looking for contributors! Whether it's a new dashboard feature, a helm chart tweak, or just a bug report—your input is welcome.
GitHub: rchakode/kube-opex-analytics
License: Apache 2.0
Give it a spin and let me know what you think in the comments! 👇