KubeOpt

The Cost Engineer for Kubernetes

KubeOpt analyzes your Kubernetes clusters, identifies cost optimization opportunities, and generates actionable implementation plans with copy-paste kubectl commands.

Works with Azure AKS, AWS EKS, and Google GKE.

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What It Does

• Connects to your cloud provider APIs and Kubernetes clusters
• Runs 16 optimization algorithms (rightsizing, HPA, storage, networking, node pools, anomaly detection)
• Calculates actual vs optimal costs with specific dollar savings per resource
• Generates a 3-week implementation plan with kubectl commands ready to execute
• Dashboard with cost breakdowns, workload analysis, and optimization scores

Claude AI Integration (MCP)

Ask Claude about your Kubernetes costs in plain English.

KubeOpt ships an MCP server (mcp_server/) that exposes 6 tools over stdio transport. Once connected, Claude Desktop, Cursor, or Windsurf can query your cluster data directly — no copy-pasting dashboards.

Tools exposed:

Tool	What it does
list_clusters	List all monitored clusters with cost data
get_cost_summary	Portfolio-level cost summary across all clusters
get_cluster_analysis	Detailed analysis for a specific cluster
get_recommendations	Actionable recommendations sorted by savings impact
analyze_cluster	Trigger a fresh analysis and poll until complete
get_pod_costs	Per-pod cost breakdown, filterable by namespace

Prerequisites

• KubeOpt running locally (python main.py) or deployed on Railway
• Python virtual environment with dependencies installed (pip install -r requirements.txt)

Claude Desktop

Edit ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "kubeopt": {
      "command": "/path/to/kubeopt/.venv/bin/python3",
      "args": ["-m", "mcp_server.server"],
      "env": {
        "KUBEOPT_API_URL": "http://localhost:5001",
        "KUBEOPT_USERNAME": "kubeopt",
        "KUBEOPT_PASSWORD": "your-password"
      }
    }
  }
}

Restart Claude Desktop after saving.

Cursor

Open Cursor Settings → MCP and add a new server entry:

{
  "kubeopt": {
    "command": "/path/to/kubeopt/.venv/bin/python3",
    "args": ["-m", "mcp_server.server"],
    "env": {
      "KUBEOPT_API_URL": "http://localhost:5001",
      "KUBEOPT_USERNAME": "kubeopt",
      "KUBEOPT_PASSWORD": "your-password"
    }
  }
}

Windsurf / Codeium

Edit ~/.codeium/windsurf/mcp_config.json (create it if it doesn't exist):

{
  "mcpServers": {
    "kubeopt": {
      "command": "/path/to/kubeopt/.venv/bin/python3",
      "args": ["-m", "mcp_server.server"],
      "env": {
        "KUBEOPT_API_URL": "http://localhost:5001",
        "KUBEOPT_USERNAME": "kubeopt",
        "KUBEOPT_PASSWORD": "your-password"
      }
    }
  }
}

Restart Windsurf after saving.

Example prompts

What are my top 3 cost savings opportunities across all clusters?
Which pods are costing the most in the production namespace?
Give me a summary of total Kubernetes spend this month.
What's the optimization score for my staging cluster?
Trigger a fresh analysis on cluster prod-aks-eastus and report back.

For more on the protocol: modelcontextprotocol.io

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GitHub Action

Run a Kubernetes cost scan on every pull request or on a schedule. The action posts a summary as a PR comment (upserted on re-runs) and writes results to the GitHub Step Summary.

What you get on each PR:

## KubeOpt Cost Scan — 2026-04-27

| Cluster          | Provider | Monthly Spend | Savings Available |
|------------------|----------|---------------|-------------------|
| prod-eks-us-east | AWS      | $4,120        | $890/mo           |
| staging-aks-weu  | Azure    | $1,340        | $210/mo           |

**Total potential savings: $1,100/mo**

<details>
<summary>Top opportunities</summary>

1. $540/mo — prod-eks-us-east — Rightsize 6 over-provisioned node groups
2. $350/mo — prod-eks-us-east — Enable HPA on 4 deployments with static replicas
3. $210/mo — staging-aks-weu  — Remove 3 idle nodes outside business hours

</details>

Setup

1. Add secrets to your repository

Go to Settings → Secrets and variables → Actions and add:

Secret	Value
KUBEOPT_URL	URL of your KubeOpt instance (e.g. https://demo.kubeopt.com)
KUBEOPT_USERNAME	KubeOpt username
KUBEOPT_PASSWORD	KubeOpt password

2. Create .github/workflows/cost-scan.yml

name: K8s Cost Scan

on:
  pull_request:
    types: [opened, synchronize]
  schedule:
    - cron: '0 8 * * 1'   # Every Monday at 08:00 UTC
  workflow_dispatch:

permissions:
  contents: read
  pull-requests: write

jobs:
  cost-scan:
    name: KubeOpt Cost Scan
    runs-on: ubuntu-latest
    steps:
      - name: Run KubeOpt cost scan
        id: kubeopt
        uses: kubeopt/kubeopt@v1
        with:
          kubeopt-url:      ${{ secrets.KUBEOPT_URL }}
          kubeopt-username: ${{ secrets.KUBEOPT_USERNAME }}
          kubeopt-password: ${{ secrets.KUBEOPT_PASSWORD }}
          top:              5
          post-comment:     ${{ github.event_name == 'pull_request' && 'true' || 'false' }}

      - name: Print savings to log
        if: always()
        run: echo "Total savings available: ${{ steps.kubeopt.outputs.total-savings }}/mo"

Inputs

Input	Required	Default	Description
kubeopt-url	yes		URL of your KubeOpt instance
kubeopt-username	yes	kubeopt	KubeOpt username
kubeopt-password	yes		KubeOpt password
cluster-id	no	(all clusters)	Scan a specific cluster only
top	no	5	Number of top savings opportunities to show
post-comment	no	true	Post results as a PR comment

Outputs

Output	Description
total-savings	Total potential monthly savings in USD
scan-summary	Full markdown summary (use in downstream steps)

Scan a specific cluster

- uses: kubeopt/kubeopt@v1
  with:
    kubeopt-url:      ${{ secrets.KUBEOPT_URL }}
    kubeopt-username: ${{ secrets.KUBEOPT_USERNAME }}
    kubeopt-password: ${{ secrets.KUBEOPT_PASSWORD }}
    cluster-id:       prod-eks-us-east-1
    top:              10

Notes

• The action checks out kubeopt/kubeopt@v1 at runtime to run the scan. No local install needed.
• PR comments are upserted: re-running the action updates the existing comment rather than adding a new one.
• Requires pull-requests: write permission to post comments.
• The action does not modify your cluster. It is read-only.

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Architecture

                         KubeOpt Platform
 +----------------------------------------------------------+
 |                                                          |
 |   React SPA (Recharts)     FastAPI REST API (v2)         |
 |   frontend/dist/           presentation/api/v2/          |
 |                                                          |
 +---------------------------+------------------------------+
                             |
          +------------------+------------------+
          |                  |                  |
  +-------v------+  +-------v------+  +--------v-------+
  |  Algorithms  |  |  Analytics   |  |  ML Models     |
  |  (16 modules)|  |  Collectors  |  |  Anomaly Det.  |
  |  rightsizing |  |  Processors  |  |  CPU Optimizer |
  |  HPA, storage|  |  Scorer      |  |  Workload Cls  |
  +--------------+  +--------------+  +----------------+
          |                  |                  |
  +-------v------------------v------------------v-------+
  |              Cloud Provider Abstraction              |
  |  6 interfaces: Auth, Executor, Metrics, Costs,      |
  |                Accounts, Inspector                   |
  +---+-----------------+-----------------+-------------+
      |                 |                 |
  +---v---+        +----v----+       +----v----+
  | Azure |        |   AWS   |       |   GCP   |
  | (AKS) |        |  (EKS)  |       |  (GKE)  |
  +-------+        +---------+       +---------+

Hosted Services (not in this repo)

Service	Purpose	Endpoint
Plan Generation	Generates optimization plans	plan.kubeopt.com
AI Chat	Conversational cluster analysis	ai.kubeopt.com
License Manager	License validation	license.kubeopt.com

These services require a PRO or ENTERPRISE license. The core analysis engine works without them.

Quick Start

Prerequisites

• Python 3.11+
• Node.js 18+ (for frontend build)
• Cloud provider credentials (Azure, AWS, or GCP)

Run Locally

# Clone and install
git clone https://github.com/kubeopt/kubeopt.git
cd kubeopt
pip install -r requirements.txt

# Set up credentials (copy and fill in your values)
cp .env.example .env

# Build frontend
cd frontend && npm install && npm run build && cd ..

# Run
python main.py
# Open http://localhost:5001

Run with Docker

docker build -t kubeopt .
docker run -p 5001:5001 --env-file .env kubeopt

Run via CLI

npx kubeopt clusters          # List clusters
npx kubeopt analyze <id>      # Run analysis
npx kubeopt report <id>       # View report

Cloud Provider Setup

Azure (AKS)

Set these environment variables:

AZURE_SUBSCRIPTION_ID=your-subscription-id
AZURE_CLIENT_ID=your-client-id
AZURE_CLIENT_SECRET=your-client-secret
AZURE_TENANT_ID=your-tenant-id

Requires a Service Principal with Reader role. See docs/setup/AZURE-SETUP.md.

AWS (EKS)

AWS_ACCESS_KEY_ID=your-access-key
AWS_SECRET_ACCESS_KEY=your-secret-key
AWS_DEFAULT_REGION=us-east-1

Requires IAM user with EKS, Cost Explorer, and CloudWatch read access.

Google Cloud (GKE)

GCP_SERVICE_ACCOUNT_KEY={"type":"service_account",...}
GCP_BILLING_DATASET=your_billing_dataset
GCP_BILLING_ACCOUNT_ID=your-billing-account-id

See docs/setup/GCP-BILLING-SETUP.md.

Project Structure

kubeopt/
  algorithms/          16 optimization algorithm modules
  analytics/           Cost collectors, processors, cluster scorer
  application/         Orchestrator, command generators
  infrastructure/
    cloud_providers/   Azure, AWS, GCP adapters (6 interfaces each)
    services/          Auth, caching, license validation, settings
    persistence/       Database, analysis engine
  machine_learning/    Anomaly detection, CPU optimizer, workload classifier
  presentation/
    api/v2/            FastAPI routers, schemas, dependencies
  frontend/            React SPA (TypeScript, Recharts, Tailwind)
  shared/
    standards/         16 YAML-based optimization standards
    config/            Application configuration
  mcp_server/          MCP server (6 tools, stdio transport)

Technology Stack

Component	Technology
Backend	Python 3.11+, FastAPI, uvicorn
Frontend	React 19, TypeScript, Vite, Recharts, Tailwind CSS
ML	Pandas, NumPy, Scikit-learn
Cloud	Azure SDK, boto3, google-cloud SDK
Database	SQLite (dev), PostgreSQL (prod)
Deployment	Docker, Railway, Kubernetes

License

Apache License 2.0. See LICENSE.

The core analysis engine is open source and free to use. Plan generation and AI chat are hosted services that require a PRO or ENTERPRISE license.

Contributing

See CONTRIBUTING.md for guidelines.

Security

To report security vulnerabilities, email support@kubeopt.com. See SECURITY.md.

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Built by Nivaya Technologies