burn

README

burn

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.

Why burn

• 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. Fetches real-time pricing from AWS and Azure APIs.
• 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.
• 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.

Install

# 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)

Quick start

# 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

NAMESPACE: argocd (3 pods, $41.73/mo)
──────────────────────────────────
POD                 CPU REQ→USED  MEM REQ→USED   COST/MO
argocd-deploy-0001  500m → 28m    512Mi → 299Mi  $13.91
argocd-deploy-0002  500m → 1m     512Mi → 41Mi   $13.91
argocd-deploy-0003  500m → 5m     512Mi → 7Mi    $13.91

AI recommendations

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

Ask questions in plain English

Requires ANTHROPIC_API_KEY environment variable.

Slack integration

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

Slack setup

1. Create a Slack App at https://api.slack.com/apps
2. Add Slash Command: /burn → point to your server URL + /slack
3. Set SLACK_SIGNING_SECRET and ANTHROPIC_API_KEY environment variables
4. Expose the server (e.g., ngrok for testing, load balancer for production)

On-prem and GPU clusters

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.

How it works

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

Pricing sources

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.

Deploy to Kubernetes

Helm

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"

CronJob (daily Slack reports)

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

Configuration

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)

Cloud clusters use real pricing automatically. These flags are for on-premise clusters where pricing is not available from a cloud provider.

Development

make build    # Build binary
make test     # Run tests
make lint     # Run linter

License

Apache 2.0 — See LICENSE for details.