Our client is a healthcare diagnostic equipment provider operating a data analytics platform that processes and stores large volumes of medical data.
As the platform grew, so did infrastructure costs—especially for AWS S3 storage and RDS database workloads.
Right-Sizing Instances - match compute resources to real workload needs, avoiding over-provisioning.
Leveraging Reserved & Spot Instances - lock in lower rates for predictable workloads or tap into deeply discounted spare capacity.
Storage Tiering & Lifecycle Policies - use S3 Intelligent-Tiering, Glacier, and automated lifecycle rules to move data to cheaper storage classes.
Scheduling Non-Critical Resources - automatically shut down dev, test, or analytics environments outside working hours.
Monitoring & Continuous Optimization - use AWS Cost Explorer, CloudWatch, and third-party tools to track usage and adjust over time.
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