Hi HN,

I built Wozz, an open-source CLI and GitHub Action to catch expensive Kubernetes configs before they merge.

The Motivation I noticed that most cloud cost tools (like Kubecost) only show you the bill 30 days later. By then, the over-provisioned sidecar or massive Java heap is already in production. I wanted something that acts like a unit test for resource requests blocking fat finger mistakes in the PR rather than waiting for the bill.

How it works Wozz runs in two modes:

In CI/CD (The Linter): It parses the git diff of your manifests (deployment.yaml, etc.), calculates the cost delta (requests × replicas), and posts a comment if the change exceeds a threshold (e.g., +$50/mo). It also checks HorizontalPodAutoscaler limits to flag worst-case scaling risks.

Locally (The Auditor): It scans your current kubecontext to compare reserved requests vs. actual live usage (kubectl top). This helps find the "Sleep Insurance" gap—where devs request 4GB RAM just to be safe, but the app only uses 200MB.

Implementation Details

Stack: TypeScript/Node.js.

Math: Instead of querying AWS Cost APIs (which requires sensitive creds and is slow), it uses a configurable Blended Rate (e.g., $0.04/GB/hr) to estimate costs deterministically.

Privacy: It runs 100% locally or in your runner. No manifests or secrets are sent to any external server.

Repo https://github.com/WozzHQ/wozz

Feedback I’m currently using a static Blended Rate for the cost math to keep the tool fast and stateless. I’m curious if this approximation is accurate enough for your team's guardrails, or if you strictly require real-time Spot Instance pricing to trust a tool like this?