Predefined machine types in Google Compute Engine offer significant cost savings compared to custom machine types, providing optimized performance and pricing for most workloads.
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
Review current VM instance configurations
Identify existing custom machine types
Match workload requirements with predefined types
Use Google Cloud Console or CLI to modify instances
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)
How do I know which predefined type to choose?
Analyze your current resource utilization, review Google Cloud’s predefined type specifications, and match closest to your existing configuration.
Will changing to a predefined type impact performance?
In most cases, predefined types offer equivalent or better performance. Always conduct thorough testing during migration.
How can Infracost help with this optimization?
Infracost provides detailed cost analysis and recommendations, helping you identify and implement machine type optimizations across your infrastructure.
Are there any risks in switching to predefined types?
Minimal risks exist, but always test thoroughly and have a rollback plan. Monitor performance during and after the transition.
How often should I review my machine type selections?
Recommend quarterly reviews to ensure ongoing optimization and alignment with changing workload requirements.
Create Free Account
This policy is supported in Infracost and available in the free trial. Sign up today and scan your code using our entire library of FinOps policies.