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GCP Cost Optimization

CloudYali's GCP Cost Optimization features help you identify and implement savings opportunities across your Google Cloud Platform resources. Our platform integrates with Google Cloud Recommender to provide actionable recommendations that can significantly reduce your cloud spending.

Supported GCP Recommenders​

CloudYali leverages Google Cloud's Recommender service, which offers machine learning-powered recommendations to optimize your resources. The following GCP recommenders are integrated with CloudYali:

1. Cost Recommender​

Provides insights to optimize your Google Cloud costs by identifying idle resources, recommending rightsizing options, and suggesting commitment-based discount opportunities.

2. Compute Instance Idleness Recommender​

Identifies Compute Engine VM instances that have been idle or underutilized over a period of time and recommends stopping or deleting them.

3. Compute Instance Machine Type Recommender​

Analyzes CPU and memory usage patterns of your VM instances and suggests optimal machine types that can save costs while maintaining performance.

4. Committed Use Discount (CUD) Recommender​

Recommends commitment-based discount purchases based on your steady-state resource usage, helping you achieve significant discounts compared to on-demand pricing.

5. Disk Idleness Recommender​

Identifies persistent disks that haven't been used for an extended period and recommends deleting them to eliminate unnecessary storage costs.

6. Idle IP Address Recommender​

Finds unused static IP addresses that are incurring charges and recommends releasing them.

7. Idle Load Balancer Recommender​

Detects load balancers with minimal or no traffic and recommends removing them to eliminate unnecessary costs.

8. BigQuery Capacity Recommender​

Analyzes BigQuery usage patterns and recommends reservation purchases that can optimize costs for predictable workloads.

9. Cloud SQL Overprovisioned Recommender​

Identifies Cloud SQL instances that are overprovisioned and suggests rightsizing options to reduce costs.

10. Cloud Storage Bucket Recommender​

Analyzes storage patterns and recommends optimal storage classes or lifecycle policies to reduce storage costs.

Types of GCP Cost Optimization Recommendations​

CloudYali categorizes GCP cost recommendations into several types:

1. Delete Idle Resources​

Potential Savings: Up to 100% of the resource cost

Identifies completely unused resources that can be safely removed:

  • Idle VM instances
  • Unattached persistent disks
  • Unused static IP addresses
  • Idle load balancers

Example Recommendations:

  • "Delete idle VM instance 'prod-backup-server' that has been unused for 30+ days"
  • "Remove unattached persistent disk 'backup-disk-2' to save $25.50 per month"

2. Rightsize Resources​

Potential Savings: 15-70% of the resource cost

Identifies overprovisioned resources and recommends more appropriately sized options:

  • VM instance machine type optimization
  • Cloud SQL instance rightsizing
  • GKE cluster node optimization

Example Recommendations:

  • "Rightsize VM 'app-server-1' from n1-standard-8 to n1-standard-4 to save $85.40 per month"
  • "Reduce Cloud SQL instance 'analytics-db' from 16 to 8 cores to save $130.75 per month"

3. Optimize Storage​

Potential Savings: 20-90% of the storage cost

Recommends changes to storage configurations to reduce costs:

  • Cloud Storage class transitions (e.g., from Standard to Nearline or Coldline)
  • Implement object lifecycle policies
  • Compress or delete unnecessary data

Example Recommendations:

  • "Transition objects in 'archive-bucket' older than 30 days to Coldline storage to save 60% on storage costs"
  • "Enable auto-tiering on bucket 'data-warehouse' to optimize storage costs based on access patterns"

4. Purchase Commitments​

Potential Savings: 20-70% compared to on-demand pricing

Recommends commitment-based discount purchases for consistent workloads:

  • Compute Engine committed use discounts
  • BigQuery reservations
  • Cloud SQL committed use discounts

Example Recommendations:

  • "Purchase a 3-year commitment for 50 n2-standard-4 instances to save $25,000 annually"
  • "Purchase a 100-slot BigQuery reservation for your analytics workloads to save 40% compared to on-demand pricing"

How to Access GCP Cost Recommendations​

  1. Log in to your CloudYali Dashboard

  2. Navigate to the Cost Optimization section

    • Click on "Cost Optimization" in the main navigation menu

  3. Filter for GCP Recommendations

    • Use the provider filter to show only GCP recommendations
    • Further filter by recommendation type, resource type, or potential savings amount

  4. Review and Implement Recommendations

    • Each recommendation includes detailed information about the potential savings and implementation steps
    • Click on a recommendation to see more details and specific actions to take

Best Practices for GCP Cost Optimization​

  • Enable All Recommenders: Ensure all GCP recommenders are enabled in your Google Cloud project
  • Regularly Review Recommendations: Check for new cost optimization opportunities at least bi-weekly
  • Prioritize High-Impact Recommendations: Focus on recommendations with the largest potential savings
  • Implement Resource Hierarchies: Use folders and projects to organize resources for better cost attribution
  • Leverage Labels: Implement a consistent labeling strategy to track costs across teams and applications
  • Combine with Budgets and Alerts: Set up CloudYali budget alerts to prevent unexpected cost increases

Implementation Workflow​

For optimal results, follow this workflow when implementing GCP cost recommendations:

  1. Assessment: Review the recommendation details, including potential savings and impact
  2. Validation: Verify that implementing the recommendation won't affect workload performance
  3. Scheduling: Plan implementation during a maintenance window if the change may cause disruption
  4. Implementation: Apply the recommended changes
  5. Monitoring: Track performance and costs after implementation to confirm the expected savings

Deep Dive: Machine Type Recommendations​

Machine type recommendations are among the most common and impactful GCP cost optimizations. CloudYali provides detailed insights into:

  • Current vs. Recommended Configuration: Compare your current machine type with the recommended option
  • CPU and Memory Utilization: Review historical usage patterns that led to the recommendation
  • Performance Impact Assessment: Understand the potential performance implications of the change
  • Implementation Commands: Get the exact gcloud commands needed to resize your instances

Need Assistance with GCP Cost Optimization?​

If you need help implementing GCP cost optimization recommendations or have questions about specific recommendations:

  • Email Support: Contact us at support@cloudyali.io
  • Schedule a Consultation: Book a session with our cloud cost optimization experts