How We Cut AWS Costs by 85% for a Healthcare Client

From $487 to $73/month: A step-by-step case study showing exactly how we optimized AWS infrastructure for a production healthcare application handling 50K daily users.

Before Optimization
$487/mo

Traditional EC2 architecture running 24/7

After Optimization
$73/mo

Serverless architecture with usage-based pricing

The Challenge: A dental practice management platform was spending nearly $500/month on AWS despite serving only 50,000 daily active users. After analyzing their usage patterns, we discovered the infrastructure was vastly over-provisioned for the actual workload.

🔍 The Initial Setup (What Was Wrong)

When we audited their AWS account, here's what we found:

💡 Key Finding

The application had 95% of its traffic between 9 AM - 5 PM PST. EC2 instances sat completely idle 70% of the time, yet we were paying for 100% uptime. This is the perfect serverless use case.

📋 Our 5-Step Optimization Process

Step 1: Traffic Pattern Analysis

We analyzed 30 days of CloudWatch metrics to understand actual usage. Peak traffic: 5M requests/month during business hours. Off-peak: Nearly zero traffic.

Step 2: Architecture Redesign

Migrated from EC2 to serverless: Lambda for API, Aurora Serverless for database, S3 + CloudFront for static assets. Compare architectures in our serverless vs traditional guide.

Step 3: Database Optimization

Switched from always-on RDS to Aurora Serverless v2. Automatically scales from 0.5 to 2 ACUs based on demand. Cost: $43/month vs $245/month.

Step 4: Network Cost Reduction

Eliminated NAT Gateway by moving Lambda to public subnets with VPC endpoints. Saved $45/month instantly.

Step 5: Resource Cleanup & Monitoring

Deleted unused snapshots, set CloudWatch log retention to 7 days, right-sized everything. Set up cost alerts to prevent drift.

💰 Complete Cost Breakdown

Before (Traditional Architecture):

After (Serverless Architecture):

Monthly Savings: $414 (85% reduction)

Annual Savings: $4,968

🚀 The Migration Process

Week 1: Planning & Architecture

We created a detailed migration plan, designed the new serverless architecture, and set up the development environment. Used our serverless development services for the migration.

Week 2: Development & Testing

Converted the monolithic application to Lambda functions, implemented API Gateway, migrated database to Aurora Serverless. Comprehensive testing in staging environment.

Week 3: Gradual Rollout

Blue-green deployment with Route 53 weighted routing. Started at 10% traffic, monitored for issues, gradually increased to 100%. Zero downtime migration.

Week 4: Optimization & Monitoring

Fine-tuned Lambda memory allocation, implemented caching strategies, set up comprehensive monitoring and alerting. Used techniques from our Lambda cost optimization guide.

📊 Performance Impact

Beyond cost savings, the migration delivered significant performance improvements:

🎯 Key Lessons Learned

  1. Measure Before Optimizing: CloudWatch metrics revealed the actual usage patterns
  2. Right-Size Everything: Most resources were 3-5x larger than needed
  3. Serverless Isn't Always Cheaper: But for this sporadic workload pattern, it was perfect
  4. Hidden Costs Matter: NAT Gateway alone was $45/month doing nothing
  5. Set Up Alerts: Cost anomaly detection prevents future waste

🔧 Technical Implementation Highlights

# Lambda function optimizations applied: - ARM64 architecture (20% cost savings) - Right-sized memory (512MB optimal for this workload) - Connection pooling for database - Efficient cold start optimization - Provisioned concurrency for peak hours only # Database optimization: - Aurora Serverless v2 (0.5 min, 2 max ACU) - Auto-pause enabled after 5 minutes of inactivity - Connection pooling through RDS Proxy

💡 Could This Work for Your Application?

This optimization strategy works best when:

Use our AWS Cost Calculator to estimate your potential savings. For detailed comparisons, see our EC2 vs Serverless cost analysis.

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