Skip to main content
AI-Driven AWS Cost Optimization for Enterprise Infrastructure
Cloud & FinOps Intelligence

AI-Driven AWS Cost Optimization for Enterprise Infrastructure

Enterprise Healthcare Technology Organization

Overview

Implemented an AI-driven FinOps program for a digital health technology company, using machine learning-based cost analysis and predictive modeling to optimize a mature, multi-service AWS infrastructure. Achieved 35-50% overall cost savings through intelligent rightsizing, lifecycle management, and commitment-based pricing — without compromising performance, compliance, or availability.

AI-Driven AWS Cost Optimization for Enterprise Infrastructure — overview visual

Client Profile

IndustryDigital Health Technology / Remote Patient Monitoring & Telehealth
RegionNorth America
HeadquartersSouthwest USA (New Mexico)
OperationsNationwide
Company SizeMid-Sized Enterprise (~200-300 employees)
Core BusinessComprehensive health technology solutions enabling elderly/chronically ill patients to live independently while maintaining digital connection to healthcare providers.
Key Services
Remote Patient Monitoring (real-time vitals via Bluetooth)Emergency Response (fall-detection)Predictive Health (AI analytics for early decline detection)

The Challenge

Optimize costs across a mature, multi-service infrastructure (production and non-production)

Maintain performance, compliance, and availability while reducing spend

Target: 35-50% overall cost savings

Solution Architecture

AI-Powered Cost Analysis: ML models analyzing usage patterns and predicting optimal resource allocation.

Multi-Environment Optimization: Segregated prod and non-prod for targeted savings.

Compute Layer: EC2 fleets (rightsized + Graviton); ECS/Fargate clusters (Spot for non-critical).

Storage Layer: S3 with intelligent lifecycle rules; EBS GP3 volumes; RDS snapshot optimization.

Managed Services: Redshift/OpenSearch RIs; NAT Gateway replacement with PrivateLink.

Governance: Centralized monitoring via Cost Explorer; IaC-enforced tagging.

Features & Capabilities

AI-Driven Rightsizing

ML analysis identifying over-provisioned resources with automated recommendations

Instance Migration

Graviton instances (40% better price-performance); GP3 EBS volumes (~20% savings)

Intelligent Discount Strategies

Hybrid model of Convertible RIs (up to 60% off), Compute Savings Plans, Spot Instances

Storage Optimization

AI-informed S3 lifecycle policies transitioning to Glacier/Deep Archive based on access patterns

Logging Reduction

CloudWatch log retention optimization (~40% savings)

Data Transfer Savings

PrivateLink for internal traffic (reduced NAT Gateway charges)

Predictive Cost Modeling

AI forecasting future spend based on usage trends

Technology Stack

Compute
Amazon EC2 (Graviton), ECS/Fargate (Spot/Graviton), Compute Optimizer
Storage
Amazon S3 (Glacier/Deep Archive), EBS (GP3), RDS snapshots
Databases & Analytics
Amazon RDS, Redshift, OpenSearch
Networking
NAT Gateway, AWS PrivateLink
Cost Tools
Amazon CloudWatch, AWS Cost Explorer
Secrets Management
AWS Secrets Manager to SSM Parameter Store
DevOps
Terraform, CloudFormation, Python automation scripts
AI/ML Layer
Cost prediction models, usage pattern analysis, anomaly detection

Results & Impact

Overall Cost Savings

0

35-50% reduction across infrastructure

Compute Savings

Up to 60% on eligible workloads via RIs and Graviton

Log Storage Savings

~0%

~40% reduction through retention optimization

Data Transfer Savings

Significant reduction via PrivateLink

Architecture Modernization

Graviton adoption, GP3 migration, Spot integration

CategoryCloud & FinOps Intelligence

Have a Similar Challenge?

We'd love to hear about your project and explore how we can help.

Start a Conversation