K8scost

pip install cognis-k8scost
k8scost scan .            # → prioritized findings in seconds

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ k8scost-emit --version
k8scost 0.1.0

$ k8scost-emit --help
usage: k8scost [-h] [--version] [--format {table,json}]
               [--cpu-price CPU_PRICE] [--mem-price MEM_PRICE]
               {analyze} ...

Kubernetes cost & rightsizing advisor (no Prometheus required).

positional arguments:
  {analyze}
    analyze             cost + rightsizing report for workloads

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}
                        output format (default: table)
  --cpu-price CPU_PRICE
                        $ per vCPU-hour
  --mem-price MEM_PRICE
                        $ per GiB-hour

Blocks above are real k8scost output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
"findings": [
    {
        "id": "1234567890",
        "title": "Potential Kubernetes Cluster Compromise",
        "description": "A suspicious pod was detected in a Kubernetes cluster.",
        "labels": ["kubernetes", "compromise"],
        "observables": [
            {"type": "ip", "value": "192.168.1.100"},
            {"type": "domain", "value": "example.com"}
        ]
    }
]
}

Usage — step by step

1. Install (Python 3.8+, stdlib only):

   pip install k8scost
   

   Input is a workload JSON file (no Prometheus or cluster access required).
2. Analyze workloads for cost and rightsizing recommendations:

   k8scost analyze workloads.json
   

3. Override pricing and get detailed per-container advice:

   k8scost --cpu-price 0.031 --mem-price 0.004 analyze workloads.json --advise
   

4. Read the output as JSON (or stream workloads via stdin with -):

   kubectl get deploy -A -o json | k8scost --format json analyze - | jq '.summary'
   

   JSON includes a summary (total/recommended cost, potential savings, savings_pct) and per-workload workloads[].
5. Use in CI — surface monthly savings on every infra change:

   k8scost --format json analyze workloads.json | jq '.summary.potential_monthly_savings'
   

Contents

• Why k8scost? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Why k8scost?

k8s FinOps

k8scost is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

Features

• ✅ Parse Cpu
• ✅ Parse Mem
• ✅ Parse Workloads
• ✅ Analyze
• ✅ Summarize
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

pip install cognis-k8scost
k8scost --version
k8scost scan .                       # scan current project
k8scost scan . --format json         # machine-readable
k8scost scan . --fail-on high        # CI gate (non-zero exit)

Example

$ k8scost scan .
  [HIGH    ] K8S-001  example finding             (./src/app.py)
  [MEDIUM  ] K8S-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[addresses + transactions] --> P[k8scost<br/>cluster + trace]
  P --> OUT[sanctions xref / report]

Use it from any AI stack

k8scost is interoperable with every popular way of using AI:

• MCP server — k8scost mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe k8scost scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

How it compares

	Cognis k8scost	Kubecost 3.0
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Built in the spirit of Kubecost 3.0, re-framed the Cognis way. Missing a credit? Open a PR.

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (k8scost mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

Install — every way, every platform

pip install "git+https://github.com/cognis-digital/k8scost.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/k8scost.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/k8scost.git" # uv
pip install cognis-k8scost                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/k8scost:latest --help        # Docker
brew install cognis-digital/tap/k8scost                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/k8scost/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/k8scost	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

• statuskit — Self-hosted status page with incident timeline and subscribers
• probesite — Synthetic uptime and Playwright checks exported to Prometheus
• alertmux — Alert dedup, correlation, and routing in front of Grafana / PagerDuty
• cloudbill — Multi-cloud cost report, anomaly detection, and FOCUS export
• otelbox — One-command OpenTelemetry collector + dashboards bundle

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If k8scost saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license ([email protected]). See LICENSE.

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