AgentSkillsCN

gcp-cost-optimizer

分析 GCP 成本,提供优化建议,包括承诺使用折扣、资源规格调整以及闲置资源清理。当您需要优化 GCP 支出、或深入分析 GCP 费用时,可使用此功能。

SKILL.md
--- frontmatter
name: gcp-cost-optimizer
description: Analyzes GCP costs and provides optimization recommendations including committed use discounts, rightsizing, and unused resources. Use when optimizing GCP spending or analyzing GCP costs.

GCP Cost Optimizer

Quick Start

Analyze GCP costs and implement optimization strategies to reduce spending.

Instructions

Step 1: Analyze current costs

bash
# View billing data
gcloud billing accounts list

# Export billing data to BigQuery
gcloud billing accounts projects link PROJECT_ID \
  --billing-account=BILLING_ACCOUNT_ID

# Query costs
bq query --use_legacy_sql=false \
  'SELECT service.description, SUM(cost) as total_cost
   FROM `project.dataset.gcp_billing_export`
   WHERE DATE(_PARTITIONTIME) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
   GROUP BY service.description
   ORDER BY total_cost DESC'

Step 2: Identify optimization opportunities

Committed Use Discounts:

  • 1-year or 3-year commitments
  • Up to 57% savings for Compute Engine
  • Up to 70% savings for Cloud SQL

Sustained Use Discounts:

  • Automatic discounts for running instances
  • Up to 30% for instances running >25% of month

Preemptible VMs:

  • Up to 80% savings
  • Suitable for fault-tolerant workloads

Rightsizing:

  • Use Recommender API for suggestions
  • Downsize overprovisioned instances
  • Adjust machine types

Step 3: Implement cost-saving measures

bash
# Get rightsizing recommendations
gcloud recommender recommendations list \
  --project=PROJECT_ID \
  --location=us-central1 \
  --recommender=google.compute.instance.MachineTypeRecommender

# Apply recommendation
gcloud recommender recommendations mark-claimed \
  RECOMMENDATION_ID \
  --project=PROJECT_ID \
  --location=us-central1 \
  --recommender=google.compute.instance.MachineTypeRecommender

Best Practices

  1. Enable billing export to BigQuery
  2. Set up budget alerts
  3. Use labels for cost allocation
  4. Review Recommender suggestions monthly
  5. Implement committed use discounts for stable workloads
  6. Use preemptible VMs for batch processing
  7. Clean up unused resources regularly
  8. Optimize storage classes