Google Compute Engine – Consider Predefined Machine Types

Predefined machine types in Google Compute Engine offer significant cost savings compared to custom machine types, providing optimized performance and pricing for most workloads.

Detailed Explanation

Why This Policy Matters

In the world of cloud cost optimization, selecting the right machine type is crucial for controlling expenses. Custom machine types typically cost 5% more than equivalent predefined machine types, making them a less economical choice for most workloads.

Cost Reduction Strategy

Choosing predefined machine types offers several financial advantages:

• Standardized Pricing: Predefined types have more predictable and optimized pricing
• Performance Optimization: Google has engineered these types for specific workload requirements
• Reduced Configuration Overhead: Less time spent on manual machine type customization

Potential Savings Analysis

Let’s break down the potential cost implications:

• Average Savings: 5% reduction in compute costs
• Example Scenario:
  • Custom 4 vCPU, 16GB RAM machine: $X/month
  • Equivalent Predefined machine: $0.95X/month
  • Annual Savings: Approximately $Y per instance

Implementation Guide

Infrastructure as Code Optimization

Terraform Example – Before (Costly Custom Type):

resource "google_compute_instance" "example" {
  machine_type = "custom-4-16384"  # Custom machine type
  # Other configuration
}

Terraform Example – After (Optimized Predefined Type):

resource "google_compute_instance" "example" {
  machine_type = "e2-standard-4"  # Predefined machine type
  # Other configuration
}

Step-by-Step Manual Implementation

1. Review current VM instance configurations
2. Identify existing custom machine types
3. Match workload requirements with predefined types
4. Use Google Cloud Console or CLI to modify instances
5. Use Infracost to validate and estimate potential savings before implementation

Best Practices

• Regularly audit machine type selections
• Use performance monitoring to validate workload fit
• Consider reserved instances for long-running workloads
• Leverage Infracost for continuous cost optimization recommendations

Real-World Examples

Scenario 1: Web Application Server

• Before: Custom 4 vCPU, 16GB RAM machine
• After: e2-standard-4 predefined type
• Result: 5% cost reduction, simplified management

Scenario 2: Development Environment

• Before: Precisely tuned custom machine
• After: Standard predefined type with similar specifications
• Result: Lower cost, easier scalability

Considerations and Caveats

When Custom Types Make Sense

• Extremely specific workload requirements
• Unique computational needs not covered by standard types
• High-performance computing with precise resource demands

Potential Limitations

• Not all workloads perfectly match predefined types
• Some specialized applications might require custom configurations
• Performance testing is recommended during transition

Frequently Asked Questions (FAQs)