Reducing AWS Costs by $10K Monthly: A Strategic Approach

When I joined BukuWarung.com, one of the first challenges I encountered was the rapidly increasing AWS infrastructure costs. The company was experiencing rapid growth, but the cloud spending was growing even faster than the user base. After a comprehensive analysis, I implemented a multi-faceted cost optimization strategy that resulted in over $10,000 in monthly savings.

The Challenge

Our AWS bill had grown to over $45,000 per month, and the trend was accelerating. The main cost drivers were:

• Over-provisioned EC2 instances running at 15-20% utilization
• Unattached EBS volumes accumulating over time
• Development environments running 24/7
• Lack of reserved instances for predictable workloads
• Inefficient data transfer patterns

The Strategy

1. Automated Resource Rightsizing

I developed a Python-based tool using boto3 that analyzed CloudWatch metrics to identify underutilized resources:

import boto3
import datetime
from collections import defaultdict

def analyze_ec2_utilization():
    ec2 = boto3.client('ec2')
    cloudwatch = boto3.client('cloudwatch')
    
    instances = ec2.describe_instances()
    recommendations = []
    
    for reservation in instances['Reservations']:
        for instance in reservation['Instances']:
            if instance['State']['Name'] == 'running':
                # Get CPU utilization for the last 30 days
                cpu_util = get_average_cpu_utilization(
                    cloudwatch, 
                    instance['InstanceId']
                )
                
                if cpu_util < 20:
                    recommendations.append({
                        'instance_id': instance['InstanceId'],
                        'instance_type': instance['InstanceType'],
                        'cpu_utilization': cpu_util,
                        'recommendation': suggest_instance_type(cpu_util)
                    })
    
    return recommendations

2. Automated Scaling Policies

Implemented intelligent auto-scaling groups with custom metrics:

• Predictive scaling based on historical patterns
• Target tracking for optimal performance-cost balance
• Scheduled scaling for known traffic patterns

3. Resource Lifecycle Management

Created automated workflows for:

• Development environment scheduling (start 9 AM, stop 6 PM)
• Orphaned resource cleanup (unattached volumes, unused security groups)
• Snapshot lifecycle policies with intelligent retention

Implementation Results

The implementation was rolled out in phases over 8 weeks:

Phase 1: Quick Wins (Weeks 1-2)

• Terminated unused instances: $2,400/month savings
• Removed unattached EBS volumes: $800/month savings
• Implemented dev environment scheduling: $1,500/month savings

Phase 2: Rightsizing (Weeks 3-5)

• Downsized over-provisioned instances: $3,200/month savings
• Optimized EBS volume types: $600/month savings

Phase 3: Reserved Instances (Weeks 6-8)

• Purchased strategic reserved instances: $2,800/month savings
• Implemented Savings Plans: $1,200/month savings

Monitoring and Alerting

To ensure sustainable cost management, I implemented:

Cost Anomaly Detection

def setup_cost_alerts():
    ce = boto3.client('ce')
    
    # Create anomaly detector
    response = ce.create_anomaly_detector(
        AnomalyDetector={
            'MonitorArn': 'arn:aws:ce:::monitor/SERVICE',
            'MonitorName': 'DailySpendMonitor',
            'MonitorType': 'DIMENSIONAL',
            'MonitorSpecification': json.dumps({
                'Dimension': 'SERVICE',
                'MatchOptions': ['EQUALS'],
                'Values': ['Amazon Elastic Compute Cloud - Compute']
            })
        }
    )

Real-time Dashboards

Built comprehensive dashboards showing:

• Daily/weekly/monthly spend trends
• Service-wise cost breakdown
• Optimization opportunity tracking
• ROI metrics for implemented changes

Key Learnings

1. Start with Low-Hanging Fruit

Quick wins build momentum and demonstrate immediate value to stakeholders.

2. Automate Everything

Manual processes don’t scale and are error-prone. Automation ensures consistency and enables continuous optimization.

3. Monitor Continuously

Cost optimization is not a one-time activity. Continuous monitoring and alerting are essential.

4. Balance Cost and Performance

Never compromise critical performance metrics for cost savings. The goal is optimization, not degradation.

Tools and Technologies Used

• AWS Cost Explorer API for historical analysis
• CloudWatch for performance metrics
• Lambda functions for automated cleanup
• Python/boto3 for custom tooling
• Terraform for infrastructure as code
• Datadog for unified monitoring

Conclusion

The $10,000+ monthly savings we achieved didn’t happen overnight, but the systematic approach and automation we put in place continue to deliver value. More importantly, we established a culture of cost consciousness and built tools that scale with the business.

Key takeaways for implementing similar optimizations:

1. Measure first - Establish baselines before making changes
2. Automate early - Manual processes don’t scale
3. Monitor continuously - Cost optimization requires ongoing attention
4. Think holistically - Consider the entire cost lifecycle
5. Maintain performance - Never sacrifice reliability for cost

The success of this project led to similar initiatives across GCP environments, ultimately saving the company over $200,000 annually in cloud infrastructure costs.

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Want to learn more about cloud cost optimization strategies? Feel free to reach out - I’m always happy to discuss infrastructure efficiency and automation approaches.