40% AWS Cost Reduction Through Infrastructure Optimization

Overview

A mobile app company with 2M+ users was experiencing rapidly escalating AWS costs that were eating into their runway. Despite knowing they were overspending, they lacked the expertise to identify and fix inefficiencies.

Industry: Mobile App Company Company Size: 30-50 employees Timeline: 2 months Technologies: AWS (EC2, RDS, S3, CloudWatch), Terraform, Grafana

The Challenge

The startup's AWS bill had grown from $10k/month to $38k/month in just 6 months, but user growth didn't justify this increase.

Specific Pain Points

• Runaway costs with no clear explanation
• Over-provisioned resources "just in case"
• No cost monitoring or alerting
• Development/staging environments running 24/7
• Inefficient database usage with expensive instance types
• Lack of visibility into what was driving costs
• No tagging strategy to track spending by service

Business Impact

The CFO's concern: "At this rate, we'll burn through our Series A funding 6 months ahead of schedule."

The Solution

I conducted a comprehensive infrastructure audit and implemented targeted optimizations:

1. Cost Analysis & Audit

First, I mapped all AWS spending:

• Enabled AWS Cost Explorer with detailed tagging
• Categorized costs by environment, service, and team
• Identified top 10 cost drivers
• Created cost allocation dashboard

Key Finding: 60% of costs were in non-production environments.

2. Right-Sizing EC2 Instances

Analyzed actual resource utilization:

# Analysis revealed most instances at 15-25% CPU utilization
# Recommendations:
- t3.xlarge → t3.large (production web servers)
- m5.2xlarge → m5.xlarge (application servers)
- Converted steady-state workloads to Savings Plans

Result: 35% reduction in EC2 costs

3. RDS Optimization

Database instances were severely over-provisioned:

Before:

• db.r5.4xlarge (16 vCPU, 128GB RAM) for staging
• db.r5.2xlarge (8 vCPU, 64GB RAM) for production
• Continuous backups for all databases

After:

• db.t3.large for staging (scheduled start/stop)
• db.r5.xlarge for production (sufficient for actual load)
• Optimized backup retention (7 days vs 35 days)
• Enabled Multi-AZ only for production

Result: $8k/month savings on RDS alone

4. Environment Scheduling

Implemented automatic start/stop for non-production:

# Lambda function to stop dev/staging at 8 PM, start at 8 AM
import boto3
from datetime import datetime

def lambda_handler(event, context):
    ec2 = boto3.client('ec2')
    rds = boto3.client('rds')

    # Stop tagged resources during off-hours
    if is_off_hours():
        stop_dev_resources(ec2, rds)
    else:
        start_dev_resources(ec2, rds)

Result: 70% reduction in dev/staging costs (ran only 50 hours/week instead of 168)

5. S3 Lifecycle Policies

S3 storage costs were $4k/month with no lifecycle management:

{
  "Rules": [
    {
      "Id": "MoveToIA",
      "Status": "Enabled",
      "Transitions": [
        {
          "Days": 30,
          "StorageClass": "STANDARD_IA"
        },
        {
          "Days": 90,
          "StorageClass": "GLACIER"
        }
      ]
    },
    {
      "Id": "DeleteOldLogs",
      "Status": "Enabled",
      "Expiration": {
        "Days": 90
      },
      "Filter": {
        "Prefix": "logs/"
      }
    }
  ]
}

Result: $2.5k/month savings on S3

6. CloudWatch Optimization

CloudWatch Logs were costing $2k/month:

• Reduced log retention from 365 days to 30 days
• Filtered out verbose debug logs from production
• Moved long-term logs to S3 Glacier

Result: 60% reduction in CloudWatch costs

7. Reserved Instances & Savings Plans

Committed to 1-year Savings Plans for baseline usage:

• Compute Savings Plans for steady EC2 usage
• RDS Reserved Instances for production databases
• CloudFront Savings Bundle

Result: 25% discount on committed usage

8. Monitoring & Alerting

Implemented cost monitoring to prevent regression:

• Daily cost anomaly alerts
• Weekly cost reports by service
• Budget alerts at 80% and 100% thresholds
• Grafana dashboard for real-time cost tracking

The Results

Cost Reduction

• Monthly AWS bill: $38k → $23k (40% reduction)
• Annual savings: $180k
• ROI on consulting: 60x in first year

Detailed Breakdown

Performance Impact

Zero degradation in application performance:

• Same response times
• Same uptime (99.9%)
• Same user experience
• Actually improved with better monitoring

Business Impact

• Extended runway by 6 months
• Freed up budget for hiring
• Improved unit economics
• Built cost-conscious culture

Technologies Used

• AWS Services: EC2, RDS, S3, CloudWatch, Lambda, Cost Explorer
• Infrastructure as Code: Terraform
• Monitoring: CloudWatch, Grafana
• Automation: Lambda (Python), CloudWatch Events
• Cost Management: AWS Cost Explorer, custom scripts

Key Optimizations Summary

1. ✅ Right-sized EC2 instances (35% savings)
2. ✅ Optimized RDS instances and backups (57% savings)
3. ✅ Scheduled dev/staging environments (70% savings)
4. ✅ Implemented S3 lifecycle policies (62% savings)
5. ✅ Reduced CloudWatch retention (60% savings)
6. ✅ Purchased Savings Plans (25% discount)
7. ✅ Enabled cost monitoring and alerts
8. ✅ Established tagging strategy

Best Practices Implemented

Tagging Strategy

All resources now tagged with:

Environment: production | staging | development
Service: web | api | database | cache
Team: engineering | data | ml
CostCenter: product | infrastructure

Cost Governance

• Monthly cost review meetings
• Approval required for instances > t3.medium
• Automatic termination of untagged resources
• Cost attribution to teams

Long-Term Sustainability

Created documentation and training:

• Infrastructure cost guide
• Right-sizing playbook
• Monthly cost review process
• Team training on cost-conscious development

Lessons Learned

1. Most companies overprovision by 30-50% - "Just in case" is expensive
2. Dev/staging shouldn't cost more than production - Schedule them
3. Monitoring prevents cost creep - What gets measured gets managed
4. Start with the biggest wins - Focus on RDS and EC2 first
5. Performance doesn't require over-provisioning - Right-sizing works

Client Testimonial

"We were burning cash on AWS without understanding why. The audit revealed we were paying for the equivalent of 5 empty offices running 24/7. The optimizations paid for themselves in 3 weeks and gave us 6 extra months of runway." - CTO

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