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Excited to share my latest open-source project: KubeCost Guardian
After seeing how many DevOps teams struggle with Kubernetes cost visibility on Azure, I built a full-stack cost optimization platform from scratch.
๐ช๐ต๐ฎ๐ ๐ถ๐ ๐ฑ๐ผ๐ฒ๐:
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Real-time AKS cluster monitoring via Azure SDK
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Cost breakdown per namespace, node, and pod
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AI-powered recommendations generated from actual cluster state
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One-click optimization actions
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JWT-secured dashboard with full REST API
๐ง๐ฒ๐ฐ๐ต ๐ฆ๐๐ฎ๐ฐ๐ธ:
- React 18 + TypeScript + Vite
- Tailwind CSS + shadcn/ui + Recharts
- Node.js + Express + TypeScript
- Azure SDK (@azure/arm-containerservice)
- JWT Authentication + Azure Service Principal
๐ช๐ต๐ฎ๐ ๐บ๐ฎ๐ธ๐ฒ๐ ๐ถ๐ ๐ฑ๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐:
Most cost tools show you generic estimates. KubeCost Guardian reads your actual VM size, node count, and cluster configuration to generate recommendations that are specific to your infrastructure not averages.
For example, if your cluster has only 2 nodes with no autoscaler enabled, it immediately flags the HA risk and calculates exactly how much you'd save by switching to Spot instances based on your actual VM size.
This project is fully open-source and built for the DevOps community.
โญ GitHub: https://github.com/HlaliMedAmine/kubecost-guardian
This project represents hours of hard work, and passion.
I decided to make it open-source so everyone can benefit from it ๐ค ,If you find it useful, Iโd really appreciate your support .
Your support motivates me to keep building and sharing more powerful projects ๐.
More exciting ideas are coming soonโฆ stay tuned! ๐ฅ.
1 Reply
This looks like a useful direction, especially for teams that already have AKS cost data but need better actionability.
A couple of design points I would be interested in seeing are: does it work with read-only Azure RBAC/managed identity, how does it handle shared cluster costs, and can recommendations be mapped back to namespace/team labels? That mapping is usually what turns cost visibility into something teams can actually own.
It may also be worth adding a suppression or approval workflow for recommendations. Some workloads look wasteful from CPU/memory averages, but are intentionally over-provisioned for burst, latency, or compliance reasons. Capturing that context would make the tool much more useful in real platform teams.
