Revolutionizing a Healthcare Company’s Hybrid Infrastructure: Achieving Cost Optimization with AWS
Introduction
One of the country’s leading healthcare providers found itself at a crossroads. While their infrastructure supported day-to-day operations, it was fragmented across on-premises servers and AWS, resulting in high costs and limited scalability. Leadership recognized the need to modernize, not just to reduce expenses, but to improve reliability and prepare for future growth.
This case study outlines how the company moved to a fully optimized AWS setup, balancing performance, security, and cost efficiency, while keeping mission-critical healthcare systems online throughout the process.
The Challenge
The organization faced several pressing issues:
Complex Migration Needs – Moving critical workloads to AWS without service interruptions.
Coordinating Multiple AWS Deployments – Rolling out numerous AWS services simultaneously while maintaining consistency across teams.
Soaring Monthly Costs – Infrastructure expenses had climbed to $1.5M per month.
Hybrid Infrastructure Complexity – Managing a blend of legacy on-premises equipment and partial AWS usage.
Project Timeline
Understanding the Hybrid Environment: 10–15 days
Migration Phase: 4 months
Cloud Deployment: 3 months
Cost Optimization: 2 months
Before Migration: The Starting Point
On-Premises Systems
Physical servers hosting older applications and databases
Local storage arrays for sensitive patient data
Traditional networking gear (routers, switches, firewalls)
On-site backup and disaster recovery setups
AWS Usage Before Migration
Limited mostly to testing and development
Occasional use of S3 for storage and EC2 for test workloads
Route 53 and AWS Load Balancers directing traffic to on-prem systems
After Migration: AWS at the Core
Key AWS Services Deployed
Amazon EC2
Amazon RDS
Amazon S3
Amazon CloudFront
AWS Lambda
Amazon SES
Amazon CloudWatch
Amazon CloudFormation
Amazon EKS
Amazon Glue
Amazon Redshift
Post-migration, the company’s AWS bill was still close to $1M/month, so the next phase focused on aggressive cost optimization.
Cost-Optimized Migration Approach
Landing Zone Planning
The migration began with designing a robust AWS landing zone — covering account structures, VPC networking, security baselines, monitoring, and identity integration. Poor setup here would have created costly compliance issues down the road.
Right-Sizing Resources
Instead of over-provisioning, workloads were carefully sized to match actual demand. Usage patterns guided every decision, and adjustments were made dynamically throughout migration.
These steps alone saved $40K–45K per month during the migration phase.
Cloud Deployment Cost-Saving Measures
Automated Test Server Shutdowns – Lambda scripts stopped non-essential test servers when not in use, saving $20K–25K/month.
Jenkins for CI/CD – Eliminated dependency on AWS DevOps services, saving $30K–40K/month in development labor.
EKS Optimization – Prometheus tracked pod-level usage, enabling Horizontal Pod Autoscalers to match pod count to actual need. This trimmed 20% off EKS costs (~$30K/month) across four clusters.
Idle ELB Removal – Custom Lambda functions identified and removed underutilized load balancers, cutting another 10–15% from EKS-related expenses.
NAT Gateway Adjustments – Reconfiguring NAT usage reduced inter-AZ data transfer costs.
Combined, these changes trimmed $90K–100K/month from AWS costs.
Additional Service Optimizations
Instance Sizing Reviews – Matching workloads to optimal instance types
Query and Job Tuning – Using indexes, filtering, and efficient data formats like Parquet to reduce compute needs
Batch Processing – Grouping workloads to reduce API call overhead
S3 Lifecycle Policies – Moving infrequently accessed data to Glacier or Glacier Deep Archive
S3 Intelligent-Tiering – Automatically shifting objects between storage tiers
Data Compression – Cutting storage footprint by 40–50%, directly reducing monthly S3 costs
These refinements reduced operational expenses by an additional $20K–25K per month.
The Outcome
Within months, the company:
Reduced monthly infrastructure costs from $1.5M to ~$800K — a savings of up to 20% ($150K–200K/month).
Built a secure, scalable AWS environment capable of handling future healthcare applications.
Improved operational efficiency and reduced manual intervention.
Conclusion
This transformation shows that healthcare organizations don’t have to choose between high performance and cost efficiency. By carefully planning the migration, right-sizing resources, and applying targeted cost controls, this provider now runs a modern cloud-native infrastructure that’s lean, reliable, and ready for the future. cost. By leveraging the power of the cloud and AI, we empowered the client to enhance service delivery, drive innovation, and achieve sustainable growth in an ever-evolving healthcare ecosystem.
