Cutting AWS Costs with Compute Savings Plans

Managing cloud expenditures in a rapidly scaling environment often feels like chasing a moving target. As organizations transition from monolithic architectures to dynamic, containerized, and serverless workloads, traditional cost-saving mechanisms like Standard Reserved Instances (RIs) often fall short due to their rigidity. For a Senior Cloud Architect, the goal is not just to reduce the bill, but to do so without sacrificing the agility that cloud-native architectures provide. This is where AWS Compute Savings Plans (CSP) become the cornerstone of a sophisticated financial engineering strategy.

Compute Savings Plans offer a flexible pricing model that provides significant discounts—up to 66%—in exchange for a commitment to a consistent amount of hourly compute spend (measured in $/hour) for a one- or three-year term. Unlike EC2 Instance Savings Plans or RIs, which tie you to specific instance families or regions, Compute Savings Plans automatically apply to spend across Amazon EC2, AWS Fargate, and AWS Lambda. This cross-service elasticity is vital for modern production environments where a workload might migrate from an EC2-based Kubernetes cluster to Fargate or transition into an event-driven Lambda architecture.

Architecture and Core Concepts

The fundamental advantage of Compute Savings Plans lies in their "application logic." AWS applies the commitment to your usage starting with the workload that yields the highest percentage discount. If you have a mix of C5 instances in us-east-1 and M5 instances in eu-west-1, the billing engine dynamically calculates where the CSP provides the most value every hour.

In a multi-account environment managed via AWS Organizations, the benefit of a Savings Plan is even more pronounced. If the purchasing account does not exhaust the hourly commitment, the remaining discount "floats" to other accounts within the consolidated billing family. This prevents the "stranded capacity" issue common with regional RIs.

The diagram above illustrates the "Waterfall Effect." The Savings Plan is purchased at the management level, and the AWS billing engine automatically prioritizes the application of the commitment to the resource with the greatest discount rate relative to its On-Demand price.

Implementation: Programmatic Recommendation Retrieval

While the AWS Management Console provides a user-friendly interface for purchasing plans, production-grade environments require programmatic analysis to integrate with internal FinOps dashboards or automated procurement workflows. Using the AWS SDK for Python (boto3), we can query the Cost Explorer API to retrieve precise recommendations based on historical usage patterns.

The following script demonstrates how to extract recommendation details, specifically focusing on the ESTIMATED_MONTHLY_SAVINGS and the RECOMMENDED_HOURLY_COMMITMENT.

import boto3
from datetime import datetime, timedelta

def get_compute_savings_plan_recommendations(lookback_days=30):
    ce = boto3.client('ce')
    
    # Fetch recommendations for a 3-year term, No Upfront
    try:
        response = ce.get_savings_plans_purchase_recommendation(
            SavingsPlansType='COMPUTE_SP',
            TermInYears='THREE_YEARS',
            PaymentOption='NO_UPFRONT',
            LookbackPeriodInDays=f'SEVEN_DAYS'
        )
        
        recommendations = response['SavingsPlansPurchaseRecommendation']['SavingsPlansPurchaseRecommendationDetails']
        
        for rec in recommendations:
            hourly_commitment = rec['RecommendationDetailData']['HourlyCommitmentToPurchase']
            est_monthly_savings = rec['RecommendationDetailData']['EstimatedMonthlySavingsAmount']
            current_avg_hourly_spend = rec['RecommendationDetailData']['CurrentAverageHourlySpend']
            
            print(f"--- Recommendation Details ---")
            print(f"Recommended Hourly Commitment: ${hourly_commitment}")
            print(f"Estimated Monthly Savings: ${est_monthly_savings}")
            print(f"Current Average Hourly Spend: ${current_avg_hourly_spend}")
            print(f"Estimated Savings Percentage: {rec['RecommendationDetailData']['EstimatedSavingsPercentage']}%")
            
    except Exception as e:
        print(f"Error retrieving recommendations: {str(e)}")

if __name__ == "__main__":
    get_compute_savings_plan_recommendations()

This script allows architects to bypass the manual console and feed data into custom alerting systems. When implementing this, ensure the IAM identity has the ce:GetSavingsPlansPurchaseRecommendation permission.

Best Practices and Service Comparison

Choosing between different commitment models requires understanding the trade-offs between flexibility and the depth of the discount.

The architectural recommendation for most growth-stage companies is to utilize Compute Savings Plans for the "base layer" of usage. If you have a highly stable, legacy workload that you know will remain on a specific instance family (e.g., R5) in a specific region for three years, only then should you consider EC2 Instance Savings Plans to capture that extra 6% margin.

Performance and Cost Optimization

Optimization is not a one-time event; it is a cycle. A common mistake is over-committing. If your hourly commitment is $10.00/hour but your usage drops to $8.00/hour during a deployment or a scaling event, you still pay for the $10.00. This is known as "under-utilization."

To maximize ROI, architects should target a "Coverage" metric of 70-80% rather than 100%. This allows the remaining 20% to be handled by Spot Instances (for stateless tasks) or On-Demand (for unexpected spikes), preventing wasted spend during troughs.

The strategy here is to treat the Savings Plan as your "floor." By analyzing the minimum spend over the last 90 days, you can safely commit to that amount without risking under-utilization.

Monitoring and Production Patterns

In production, monitoring the state of your Savings Plans is critical. Plans expire, and without a renewal strategy, costs can jump by 40% overnight. A robust pattern involves monitoring the Utilization and Coverage metrics through AWS Budgets and CloudWatch.

• Utilization: Percentage of your commitment you are actually using. If this is below 100%, you are over-committed.
• Coverage: Percentage of your total eligible spend covered by the plan. If this is low, you have room for more savings.

The state machine above represents the lifecycle of a Savings Plan. The "RightSizing" phase is crucial; as you optimize your code (e.g., moving from X86 to Graviton), your hourly spend will decrease. Because Compute Savings Plans apply to Graviton instances automatically, you don't need to exchange your plan—you simply see the discount applied to the new, lower-cost ARM instances.

Conclusion

Compute Savings Plans represent the most significant evolution in AWS cost management, shifting the burden of manual resource mapping from the architect to the AWS billing engine. By committing to a dollar amount rather than a specific resource, organizations can maintain the freedom to innovate—switching from EC2 to Fargate or adopting Graviton—without financial penalty. The key to success lies in programmatic monitoring, maintaining a "coverage" buffer to avoid over-commitment, and leveraging the waterfall application logic to ensure every dollar of commitment is applied to the highest-value resource.

References

• https://aws.amazon.com/savingsplans/compute/
• https://docs.aws.amazon.com/savingsplans/latest/userguide/what-is-savings-plans.html
• https://aws.amazon.com/blogs/aws-cloud-financial-management/10-things-you-should-know-about-savings-plans/