burn

README

burn

Your Kubernetes cluster is burning money. Find out where.

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

Kubernetes Cost Report (7d avg)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Monthly: $350 | Idle: $117 (33%)
Nodes: 5 | Pods: 77

NAMESPACES
──────────
NAMESPACE            PODS  CPU REQ→USED  MEM REQ→USED   COST/MO
argocd               4     2.0 → 30m     2.0Gi → 393Mi  $56
amazon-cloudwatch    11    1.6 → 82m     829Mi → 1.3Gi  $44
kube-system          21    1.4 → 52m     1.6Gi → 757Mi  $41
...and 7 more namespaces
Idle (unallocated)                                     $117
─────────────────────────────────────────────────────────
Total                                                  $350

LOAD BALANCERS
──────────────
NAME                        NAMESPACE    COST/MO
app-ingress                 app-prod     $16

COST BREAKDOWN
━━━━━━━━━━━━━━
Compute:         $350
Storage:         $0
Load Balancers:  $16
Network:         $0
Total:           $366

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

# Namespace cost breakdown
burn analyze

# With Prometheus for actual usage data
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 (4 pods, $56/mo)
──────────────────────────────────
POD                                CPU REQ→USED  MEM REQ→USED   COST/MO
argocd-application-controller-0    500m → 23m    512Mi → 346Mi  $14
argocd-server-5bdc77f5b6-njxc6     500m → 1m     512Mi → 34Mi   $14
argocd-dex-server-8fc854b84-pxqh5  500m → <1m    512Mi → 20Mi   $14
argocd-redis-7fd8bb554b-zqdcz      500m → 2m     512Mi → 5Mi    $14

AI recommendations

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

Burn sends your cluster data to Claude and returns prioritized, actionable recommendations with real node names and ready-to-run commands:

RECOMMENDATIONS
───────────────
All 5 nodes are on-demand t3.large instances with 26-41% idle rates,
wasting $117/month. Converting to Spot saves up to $277/month.

[!!] 1. Convert All 5 Nodes to Spot
   All 5 on-demand t3.large nodes have 26-41% idle cost, wasting $117/month.
   Switching to Spot saves up to $277/month (~79% discount).
   ⚠️ Only for stateless workloads (Deployments with >1 replica).
   $ eksctl create nodegroup --cluster=CLUSTER --region=eu-central-1 --spot --nodes=5

[!!] 2. Right-size over-provisioned pods
   argocd-dex-server requests 500m CPU but uses 0.12m (0.0% efficiency), $14/month.
   $ kubectl set resources deployment argocd-dex-server -n argocd \
     --requests=cpu=10m,memory=64Mi

[!] 3. Remove idle debug pods in dev and qa
   Two rds-debug pods costing $5.7/month each with near-zero usage.
   $ kubectl delete pod rds-debug -n app-api-dev

Total potential savings: $277/mo

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 "why is argocd so expensive?"	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 for 600+ AWS and 300+ Azure instances is embedded and updated weekly via GitHub Actions. Storage and load balancer costs are fetched from cloud APIs at runtime. 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.