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

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.

brew install tanrikuluozlem/burn/burn

brew upgrade tanrikuluozlem/burn/burn

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 pull ghcr.io/tanrikuluozlem/burn:latest

git clone https://github.com/tanrikuluozlem/burn.git
helm install burn ./burn/charts/burn

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)

burn analyze

burn analyze --prometheus http://prometheus:9090

burn analyze --prometheus http://prometheus:9090 --period 7d

burn analyze --prometheus http://prometheus:9090 --namespace argocd

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

• Create a Slack App at https://api.slack.com/apps - Add Slash Command: /burn

→ point to your server URL +/slack

• Set SLACK_SIGNING_SECRET

andANTHROPIC_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

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

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 for details.