Show HN: Burn, K8s cost CLI that pulls actual spot prices per instance type

A new open-source Kubernetes cost monitoring tool called Burn provides real-time spot pricing per instance type, AI-powered cost analysis, and Slack integration without requiring cluster agents or configuration files. The tool, available via Homebrew, Docker, and Helm, identifies which workloads can safely move to spot instances and detects load balancer costs across AWS EKS, Azure AKS, GCP GKE, and on-premise clusters.

Your Kubernetes cluster is burning money. Find out where. No agent to deploy. No dashboard to maintain. No YAML to configure. Just install and run. Zero setup — brew install , run one command, get answers. No cluster agent, no persistent storage, no config files. Full cost coverage — Compute, storage, load balancers, and GPU costs with real-time cloud pricing. AI-powered — Ask questions in plain English, get kubectl commands you can copy-paste. Slack-native — /burn for instant cost reports. /burn ask "..." for AI analysis. Cloud + on-prem — Works with AWS EKS, Azure AKS, GCP GKE, and on-premise clusters. Spot readiness — Identifies which workloads can safely move to spot instances with real-time discount and interruption rate. Ingress LB detection — Detects load balancers from both Services and Ingress resources, with hostname deduplication. Time-aware — --period 7d for weekly averages instead of point-in-time snapshots. Homebrew brew install tanrikuluozlem/burn/burn Upgrade brew upgrade tanrikuluozlem/burn/burn Binary VERSION=$ curl -s https://api.github.com/repos/tanrikuluozlem/burn/releases/latest | grep tag name | cut -d'"' -f4 | tr -d 'v' && \ curl -L "https://github.com/tanrikuluozlem/burn/releases/latest/download/burn ${VERSION} $ uname -s | tr ' :upper: ' ' :lower: ' $ uname -m | sed 's/x86 64/amd64/;s/aarch64/arm64/' .tar.gz" | tar xz Docker docker pull ghcr.io/tanrikuluozlem/burn:latest Helm git clone https://github.com/tanrikuluozlem/burn.git helm install burn ./burn/charts/burn Go go install github.com/tanrikuluozlem/burn/cmd/burn@latest macOS:If you see a Gatekeeper warning, run: sudo xattr -d com.apple.quarantine $ which burn Cost breakdown without Prometheus burn analyze With Prometheus pass your Prometheus URL burn analyze --prometheus http://prometheus:9090 7-day average burn analyze --prometheus http://prometheus:9090 --period 7d Drill into a namespace burn analyze --prometheus http://prometheus:9090 --namespace argocd Spot readiness burn analyze --prometheus http://prometheus:9090 --spot Real-time spot discount and interruption rate per instance type. Get cluster-wide or namespace-specific recommendations: burn analyze --prometheus http://prometheus:9090 --period 7d --ai burn analyze --prometheus http://prometheus:9090 --namespace app-backend --ai burn ask --prometheus http://prometheus:9090 "why is argocd so expensive?" Example: burn analyze --namespace app-backend --period 7d --ai NAMESPACE: app-backend 3 pods, $17.19/mo ────────────────────────────────── POD CPU REQ→USED MEM REQ→USED COST/MO app-backend-deploy-0001 200m → <1m 256Mi → 9Mi $5.73 app-backend-deploy-0002 200m → <1m 256Mi → 9Mi $5.73 app-backend-deploy-0003 200m → <1m 256Mi → 128Mi $5.73 RECOMMENDATIONS ─────────────── The app-backend namespace costs $17.19/mo across 3 pods, but CPU efficiency is critically low at ~0.1% — pods request 200m CPU each while p95 usage is under 0.31m. 1. Rightsize CPU Requests using p95 data app-backend-deploy-0001: p95 CPU is 0.22m → recommend 1m 1.5x p95 app-backend-deploy-0002: p95 CPU is 0.30m → recommend 1m 1.5x p95 app-backend-deploy-0003: p95 MEM is 128Mi 50% eff — leave as-is $ kubectl set resources deployment app-backend -n app-backend \ --requests=cpu=1m,memory=14Mi --limits=cpu=200m,memory=256Mi 2. app-backend-ingress LB $19.71/mo costs more than the namespace The load balancer alone exceeds the $17.19/mo compute cost. If internal-only, switch to ClusterIP to eliminate the LB cost. $ kubectl patch svc app-backend-ingress -n app-backend \ -p '{"spec": {"type": "ClusterIP"}}' 3. Enable VPA in Recommend Mode Prevent over-provisioning from recurring with continuous p95 tracking. $ kubectl apply -f vpa-app-backend.yaml Requires ANTHROPIC API KEY environment variable. Run burn as a Slack bot: burn serve --port 8080 --prometheus http://prometheus:9090 --period 7d | Command | What you get | |---|---| /burn | Full cost report — nodes, namespaces, idle cost, LB, storage | /burn ns argocd | Pod-level breakdown for a namespace | /burn ask "what is the single biggest waste?" | AI analysis with kubectl commands | - Create a Slack App at https://api.slack.com/apps https://api.slack.com/apps - Add Slash Command: /burn → point to your server URL + /slack - Set SLACK SIGNING SECRET and ANTHROPIC API KEY environment variables - Expose the server e.g., ngrok for testing, load balancer for production Burn works with on-premise and GPU clusters. Set your own resource rates: burn analyze \ --cpu-price 0.05 \ --ram-price 0.008 \ --gpu-price 3.00 \ --storage-price 0.10 Without custom pricing, cloud-equivalent rates are used as defaults. Kubernetes API → nodes, pods, PVCs, services, ingresses Prometheus → actual CPU & memory usage optional Cloud Pricing → real VM, storage, and GPU prices AWS, Azure, GCP ↓ Cost Engine → compute, storage, load balancers, GPU, idle detection ↓ CLI / Slack / AI Recommendations | Priority | Source | When | |---|---|---| | 1 | AWS/Azure pricing API | AWS credentials available — real-time, region-aware | | 2 | Embedded pricing DB | No credentials — 600+ AWS, 300+ Azure instances, updated weekly | | 3 | Static fallback | Unknown instance type — estimates based on instance family | Storage and load balancer costs are fetched from cloud APIs when available, with static fallbacks. Usage-based charges data processing, LCU depend on traffic volume and are not included. GPU nodes are detected automatically and priced via ratio-based cost splitting. git clone https://github.com/tanrikuluozlem/burn.git helm install burn ./burn/charts/burn \ --set prometheus.url=http://prometheus:9090 \ --set schedule="0 9 1-5" apiVersion: batch/v1 kind: CronJob metadata: name: burn-report spec: schedule: "0 9 1-5" jobTemplate: spec: template: spec: containers: - name: burn image: ghcr.io/tanrikuluozlem/burn:latest args: - analyze - --prometheus - http://prometheus-server.monitoring:80 - --period - 7d - --ai - --slack env: - name: ANTHROPIC API KEY valueFrom: secretKeyRef: name: burn-secrets key: anthropic-api-key - name: SLACK WEBHOOK URL valueFrom: secretKeyRef: name: burn-secrets key: slack-webhook-url restartPolicy: OnFailure | Variable | Description | Required for | |---|---|---| ANTHROPIC API KEY | Claude API key | --ai , ask , serve | SLACK WEBHOOK URL | Slack webhook URL | --slack | SLACK SIGNING SECRET | Slack app signing secret | serve | | Flag | Description | |---|---| --cpu-price | CPU cost per core per hour on-prem | --ram-price | RAM cost per GiB per hour on-prem | --gpu-price | GPU cost per unit per hour on-prem | --storage-price | Storage cost per GiB per month on-prem | --spot | Show spot instance readiness details | Cloud clusters use real pricing automatically. These flags are for on-premise clusters where pricing is not available from a cloud provider. make build Build binary make test Run tests make lint Run linter Apache 2.0 — See LICENSE /tanrikuluozlem/burn/blob/main/LICENSE for details.