AWS Cloud Cost Optimization & Infrastructure Efficiency

At a Glance

$1,483/month savings | $17,796 annually | 33% cost reduction | Performance maintained

A leading AI-powered election analysis platform reduced AWS costs from $4,410 to $2,927 monthly, achieving $1,483 savings while maintaining performance during high-traffic election coverage.

The Challenge

An AI-driven election analysis platform experienced rapid user growth during election season. Their AWS infrastructure scaled quickly, but monthly bills reached $4,410. The engineering team, focused on AI model development and election coverage features, lacked time for infrastructure optimization.

Unused resources accumulated - rapid development cycles left behind orphaned EC2 instances, unattached EBS volumes, idle load balancers, and unused elastic IPs. Each resource generated monthly charges, creating resource sprawl waste.

Over-provisioned EKS infrastructure used larger instance types than workload requirements demanded. CloudWatch metrics showed consistent low CPU/memory utilization indicating over-provisioning, meaning they were paying for unused capacity.

Limited infrastructure visibility - the engineering team lacked systematic cost monitoring and resource tagging. Identifying waste required manual AWS Console navigation across services, making it difficult to track spend.

The Solution: Systematic Cost Optimization

DevKraft deployed a data-driven cost optimization strategy combining resource cleanup, Kubernetes right-sizing, and ongoing cost monitoring.

The 2-phase optimization approach proceeded strategically.

Phase 1: Resource optimization - cost analysis, resource inventory, unused cleanup, EKS node right-sizing, storage optimization, and validation, delivering $1,483/month savings.

Phase 2: Continuous monitoring - cost tracking, budget alerts, optimization recommendations, monthly reviews, and capacity planning, delivering sustained efficiency.

Key optimization actions addressed multiple areas. Unused resource cleanup identified and deleted idle EC2 instances, unattached EBS volumes, unused load balancers, and elastic IPs no longer serving traffic.

EKS node group optimization analyzed CloudWatch metrics for CPU/memory utilization and right-sized node groups to appropriate instance types, reducing compute costs by 25-30%.

Storage optimization optimized EBS volume types, implemented S3 lifecycle policies, and cleaned up old snapshots, reducing storage costs without impacting data availability.

Cost monitoring and governance implemented AWS Cost Explorer dashboards, budget alerts, and resource tagging for ongoing visibility into spending trends and optimization opportunities.

Cost Reduction Timeline

August 2024 baseline: $4,410.88 - initial cost analysis, resource inventory, and utilization analysis conducted.

September 2024 post-optimization: $2,927.34 - unused resource cleanup completed, EKS node groups optimized. Monthly savings: $1,483.54 (33% reduction).

October 2024 validation: $4,781.87 - base infrastructure remained optimized at $2,900. Additional costs from strategic expansions: new GPU VMs for AI model inference ($1,300), Windows VMs for specific workloads, increased data transfer during election coverage (~$370).

Net result: maintained $1,483/month savings on base infrastructure.

Transformative Business Impact

$1,483 average monthly savings ($17,796 annually)- reduced AWS baseline spend from $4,410 to $2,927, achieving 33% cost reduction without performance impact.

Optimized EKS infrastructure - right-sized node groups with appropriate instance types, improving resource utilization from 40-50% to 70-80%.

Systematic resource cleanup eliminated unused EC2 instances, EBS volumes, and load balancers, establishing resource tagging and lifecycle management.

Maintained platform performance - zero degradation in response times or availability, successfully handled high-traffic election coverage events.

Cost visibility established - implemented AWS Cost Explorer dashboards and budget alerts, enabling proactive cost management and optimization.

Strategic benefits delivered $17,796 annual cost reduction improving unit economics and profitability. Improved resource efficiency with EKS node groups operating at 70-80% utilization versus previous 40-50%. Scalability foundation where optimized infrastructure supports growth without proportional cost increases. Flexibility for growth as cost savings enabled strategic investments in GPU infrastructure for enhanced AI capabilities.

Key Innovation: Data-Driven Optimization

Success came from using CloudWatch metrics to identify actual resource utilization patterns before making changes. This evidence-based approach ensured right-sizing decisions maintained performance while reducing costs.

Phased validation approach implemented optimizations incrementally with monitoring between changes, allowing validation of each optimization's impact before proceeding, minimizing risk.

Differentiated strategic versus waste spending - separated necessary strategic investments (GPU VMs for AI) from wasteful spending (unused resources), enabling cutting waste while supporting business-critical growth initiatives.