gke-cost-analysis — Claude Code skill for GKE cost analysis
Analyze GKE billing exports alongside live cluster metrics. Use it to compare project, namespace, or workload costs and investigate what is driving a bill.
npx skills add google/skills --skill gke-cost-analysis -a claude-codeWhat gke-cost-analysis does
The skill answers GKE cost questions using detailed billing exports in BigQuery and cost allocation data. Namespace and workload breakdowns require GKE Cost Allocation to be enabled on the cluster. Labels enter billing exports from that point forward, with no historical backfill; queries also need the full BigQuery table path.
For cost investigations, it compares pod resource requests with live usage from kubectl top. Autopilot charges are based on requested pod resources, while Standard costs track provisioned node VMs and the cluster management fee. The skill includes adaptable bq query templates for workload and namespace breakdowns, plus read-only inspection commands such as gcloud billing budgets list.
This is for analysis, not for changing cluster configuration. Use gke-cost-optimization for rightsizing, VPA manifests, or ComputeClass selection.
What it helps with
- Compare GKE costs across projects
- Find the most expensive namespace
- Investigate a cluster cost spike
- Compare pod resource requests with live usage
- Check billing budgets and workload costs
Example prompts for the agent
- Compare GKE costs across my projects for the last 30 days.
- Find the most expensive namespace and show me a BigQuery query.
- Compare `kubectl top pods --all-namespaces --containers` with pod resource requests.
How to install gke-cost-analysis
1Skills CLI (Vercel)
npx skills add google/skills --skill gke-cost-analysis -a claude-code2Manually: put the files in place
~/.claude/skills/gke-cost-analysis/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/gke-cost-analysis/
1Skills CLI (Vercel)
npx skills add google/skills --skill gke-cost-analysis -a codex2Manually: put the files in place
.agents/skills/gke-cost-analysis/SKILL.mdFor all projects use ~/.agents/skills/
Manually: put the files in place
.cursor/skills/gke-cost-analysis/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install google/skills gke-cost-analysis2Manually: put the files in place
.github/skills/gke-cost-analysis/SKILL.md1Skills CLI (Vercel)
npx skills add google/skills --skill gke-cost-analysis -a gemini-cli2Platform CLI
gemini skills install https://github.com/google/skills --path skills/cloud/gke-cost-analysisManually: put the files in place
.devin/skills/gke-cost-analysis/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
From the same set172
The whole set →Similar
Questions about gke-cost-analysis
How do I install gke-cost-analysis in Claude Code?
Run npx skills add google/skills --skill gke-cost-analysis -a claude-code.
What does gke-cost-analysis do?
It analyzes GKE costs using BigQuery billing exports, cost allocation data, and live cluster metrics. It can break down costs by project, namespace, or workload and help investigate cost drivers.
What is required for namespace-level GKE cost breakdowns?
Enable GKE Cost Allocation on the cluster and provide the full BigQuery billing table path. Labels appear in billing exports from enablement onward; there is no historical backfill.
Source text
SKILL.md · Apache-2.0
---
name: gke-cost-analysis
metadata:
version: "1.0.1"
category: CloudObservabilityAndMonitoring
description: >-
Answer natural language questions and perform analysis on GKE cluster and
workload costs using BigQuery billing exports, cost allocation data, and live
cluster monitoring metrics. Use when querying GKE costs across projects,
namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking
cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod
requests vs. actual utilization (`kubectl top`). Don't use for applying cost
optimization changes, creating rightsizing manifests (VPA/MPA), or selecting
ComputeClasses (use gke-cost-optimization instead).
---
# GKE Cost Analysis
This skill provides guidance on answering natural language questions about
GKE-related costs, billing reports, and utilization analysis.
## Attribution
**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you
MUST read [Usage Attribution](references/attribution.md). For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix `gcloud` with
`CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gke-cost-analysis)"`
and append `agent-skills/1.0 (skill:gke-cost-analysis)` to `User-Agent` headers.
## Overview
When users ask about GKE costs (e.g., "What are my costs across projects?",
"What's my most expensive namespace?", "Why is my cluster cost spiking?"), use
this skill to provide a structured and expert response using BigQuery billing
exports, cost allocation metadata, and live cluster metrics.
## Instructions
When handling a cost-related question:
1. **Provide a Direct Answer**: Address the specific cost question or
analytical request clearly and concisely.
2. **Explain BigQuery Integration**: Explain how to query BigQuery for
historical cost breakdown. Note that GKE costs originate from the GCP
Billing Detailed BigQuery Export (`gcp_billing_export_resource_v1_*`).
3. **Check & Verify Cost Allocation**: Explain that GKE Cost Allocation must be
enabled on the cluster (`--enable-cost-allocation`) for namespace, label,
and workload-level billing granularity. If queries return empty labels,
provide the `gcloud` command to enable it.
4. **Analyze Pricing Drivers & Utilization**: When diagnosing cost drivers,
explain whether the cluster is in Autopilot (billed by requested pod
CPU/memory) or Standard mode (billed by underlying VM node size + control
plane fees), and compare live utilization (`kubectl top`) against
provisioned requests.
5. **Provide Actionable Commands/Queries**: Provide concrete BigQuery CLI (`bq
query`) commands or read-only `gcloud`/`kubectl` inspection commands. Prefer
`bq` over BigQuery Studio when available.
## Key Points & Pricing Drivers
- **Data Source**: GKE costs come from GCP Billing Detailed BigQuery Export.
The user must provide the full path to their BigQuery table (dataset name
and table name containing the Billing Account ID).
- **Granularity Requirement**: GKE Cost Allocation
(`--enable-cost-allocation`) must be enabled on the cluster to populate
`goog-k8s-cluster-name`, `k8s-namespace`, `k8s-workload-name`, and
`k8s-workload-type` labels in BigQuery.
- **Autopilot vs. Standard Cost Drivers**:
- **Autopilot Pricing**: Billed directly on pod resource requests
(`requests.cpu`, `requests.memory`, ephemeral storage). Over-requested
pods drive up billing regardless of whether the pod actively uses those
CPU cycles or memory.
- **Standard Pricing**: Billed on provisioned node pool VMs (`e2`, `n4`,
`c3`, etc.). Idle nodes or multiple low-utilization dev clusters drive
excess infrastructure costs.
- **Cluster Management Fee**: ~$0.10/hour per cluster applies to BOTH
Standard and Autopilot modes. The free tier waives it for one eligible
cluster per billing account.
- **Credits & Discounts Impact**: When analyzing `cost` versus
`cost_before_credits`, note that Committed Use Discounts (CUDs) and Spot VMs
appear as credits or reduced rate charges in the billing export.
- **Tools & Syntax**: BigQuery CLI (`bq`) is preferred. When writing Standard
SQL queries, use a dot (`.`) instead of a colon (`:`) to separate the
project ID and dataset name (`{project_id}.{dataset_name}.{table_name}`).
- **Defaults**: Assume last 30 days, row limit 10, ordering by cost descending
(`ORDER BY cost DESC`), unless specified otherwise.
## Live Cluster & Cost Monitoring
Use read-only CLI commands to inspect current cluster budgets, node utilization,
and pod resource consumption vs. requests:
```bash
# View billing budgets for an account (requires Cost Management API)
gcloud billing budgets list --billing-account={billing_account} --quiet
# View live node resource utilization across the cluster
kubectl top nodes
# View pod resource usage across namespaces (compare against requested limits to diagnose waste)
kubectl top pods --all-namespaces --containers
```
> **Warning — cluster mutation, not read-only:** Enabling GKE cost allocation
> modifies the cluster. Get explicit user confirmation before running it, and
> note that namespace/workload labels populate in the billing export only from
> enablement onward (no historical backfill).
>
> ```bash
> gcloud container clusters update {cluster_name} \
> --enable-cost-allocation \
> --region {region}
> ```
## Applying Cost Optimizations
To apply rightsizing changes based on analysis (such as setting up `VPA`
recommendation mode, adjusting CPU/memory to `P95 * 1.2`, configuring Spot VMs
via `nodeSelector` or `ComputeClass`, enforcing `ResourceQuotas`, or selecting
machine types and CUDs), use the **`gke-cost-optimization`** skill.
## BigQuery Query Templates
Ready-to-adapt `bq query` templates — single workload cost, per-workload
per-cluster breakdown, per-namespace breakdown — with the placeholder policy
and defaults (30 days, `LIMIT 10`, `ORDER BY cost DESC`) are in
[references/billing-queries.md](references/billing-queries.md). All parameters
(dataset, table, project, cluster, etc.) must be replaced with user values.
Note: Checking that the `goog-k8s-cluster-name` label exists scopes the total
billing data specifically to GKE costs.
© google · Apache-2.0 · google/skills