# Gke Cost Analysis

> **Trigger**: Use when working with GKE Cost Analysis — Google Kubernetes Engine configuration and management.

- Skill: `loopyluci/gke-cost-analysis` (Agent Skill)
- Install (CLI): `npx skillmds@latest add loopyluci/gke-cost-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/gke-cost-analysis/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: LoopyLuci (https://skillmd.com/u/loopyluci)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/loopyluci/gke-cost-analysis

---


**Trigger**: Use when working with GKE Cost Analysis — Google Kubernetes Engine configuration and management.

# GKE Cost Analysis

This skill provides guidance on answering natural language questions about
GKE-related costs, billing reports, and utilization analysis.

## 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.) plus a cluster management fee ($0.10/hour). Idle nodes or
        multiple low-utilization dev clusters drive excess infrastructure costs.
-   **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

# Verify/Enable GKE cost allocation on a cluster for namespace-level billing tracking
gcloud container clusters update {cluster_name} \
    --enable-cost-allocation \
    --region {region}

# 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
```

## 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.

## Example BigQuery Queries

Use these queries as templates to answer questions. All parameters (dataset,
table, project, cluster, etc.) must be replaced with user values.

### Cost of a Single Workload in a Single Cluster

```sql
bq query --nouse_legacy_sql '
SELECT
  SUM(cost) + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)) AS cost,
  SUM(cost) AS cost_before_credits
FROM {billing_export_table} AS bqe
WHERE _PARTITIONTIME >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
  AND project.id = "{project_id}"
  AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "goog-k8s-cluster-location" AND l.value = "{region}")
  AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "goog-k8s-cluster-name" AND l.value = "{cluster_name}")
  AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "k8s-namespace" AND l.value = "{namespace}")
  AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "k8s-workload-type" AND l.value = "{workload_type}")
  AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "k8s-workload-name" AND l.value = "{workload_name}")
;
'
```

### Cost of Each Workload in Each Cluster

```sql
bq query --nouse_legacy_sql '
SELECT
  project.id AS project_id,
  (SELECT l.value FROM bqe.labels AS l WHERE l.key = "goog-k8s-cluster-location" LIMIT 1) AS cluster_location,
  (SELECT l.value FROM bqe.labels AS l WHERE l.key = "goog-k8s-cluster-name" LIMIT 1) AS cluster_name,
  (SELECT l.value FROM bqe.labels AS l WHERE l.key = "k8s-namespace" LIMIT 1) AS k8s_namespace,
  (SELECT l.value FROM bqe.labels AS l WHERE l.key = "k8s-workload-type" LIMIT 1) AS k8s_workload_type,
  (SELECT l.value FROM bqe.labels AS l WHERE l.key = "k8s-workload-name" LIMIT 1) AS k8s_workload_name,
  SUM(cost) + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)) AS cost,
  SUM(cost) AS cost_before_credits
FROM {billing_export_table} AS bqe
WHERE _PARTITIONTIME >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
  AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "goog-k8s-cluster-name")
GROUP BY 1, 2, 3, 4, 5, 6
ORDER BY 7 DESC
LIMIT 10
;
'
```

### Cost Breakdown by Namespace in a Cluster

```sql
bq query --nouse_legacy_sql '
SELECT
  (SELECT l.value FROM bqe.labels AS l WHERE l.key = "k8s-namespace" LIMIT 1) AS k8s_namespace,
  SUM(cost) + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)) AS net_cost,
  SUM(cost) AS gross_cost
FROM {billing_export_table} AS bqe
WHERE _PARTITIONTIME >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
  AND project.id = "{project_id}"
  AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "goog-k8s-cluster-name" AND l.value = "{cluster_name}")
GROUP BY 1
ORDER BY 2 DESC
LIMIT 10
;
'
```

Note: Checking that the `goog-k8s-cluster-name` label exists scopes the total
billing data specifically to GKE costs.