The Challenge
An enterprise running extensive AWS workloads faced $2.4M annual spend with 40% waste—850+ over-provisioned EC2 instances, 12TB of unoptimized EBS snapshots, and 45TB of S3 storage without lifecycle policies across 47 accounts. Manual cost analysis consumed 120 hours monthly yet missed optimization opportunities hidden in complex usage patterns.
The Solution
We implemented AI-powered cost optimization using Amazon Bedrock with Claude 3.5 Sonnet and AWS Cost Management APIs through AWS MCP server. The solution achieved 34% cost reduction ($816K annual savings), reduced analysis time from 120 to 8 hours monthly, and transformed cost management from reactive to proactive.
Architecture Overview
Core AI and Intelligence Engine
Amazon Bedrock with Claude 3.5 Sonnet (400K input/20K output tokens):
- Discovery: Analyzed 850+ instances across 47 accounts, identified 340 idle resources ($28K monthly waste), discovered 12TB of EBS snapshots >180 days old, mapped 45TB S3 storage patterns
- Analysis: Processed 2.5M cost data points monthly, identified rightsizing for 420 instances (35% oversized average), detected RI coverage gaps ($15K monthly on-demand premium), predicted costs with 92% accuracy
- Recommendations: Generated ROI-prioritized actions (high: $50K+, medium: $10K-$50K, low: <$10K), automated low-risk approvals, provided implementation scripts
AWS MCP Server Integration:
Real-time access to Cost Explorer API, Trusted Advisor, Compute Optimizer, RI/Savings Plans utilization, and spending patterns.
Data Management and Analytics
Amazon RDS (PostgreSQL db.r6g.xlarge, 500GB, Multi-AZ): 18-month cost history, optimization tracking, ROI calculations, 7-day backup retention
Amazon S3 (Intelligent-Tiering, 15GB): Daily cost reports, AI recommendations, implementation logs, 90-day lifecycle to Glacier, KMS encryption
Amazon OpenSearch (t3.medium.search, 3 nodes, 150GB/node): Sub-500ms query latency, real-time anomaly detection, custom dashboards, 30-day retention
Infrastructure Optimization
EC2 Optimization:
- Rightsizing: 280 instances downsized (t3.2xlarge→t3.xlarge, m5.4xlarge→m5.2xlarge), $180K annual savings, zero performance impact
- Scheduling: 120 non-prod instances automated (dev: 8am-6pm weekdays), reduced 168→50 hours weekly, $85K savings
- Spot Instances: 150 workloads migrated, 70% cost reduction, 8-instance-type diversification, $120K savings with 99.2% availability
Reserved Instances & Savings Plans:
- 3-year Convertible RIs for 340 instances (45% discount), $210K annual savings
- Compute Savings Plans ($85K commitment, 15% additional discount) covering Lambda, Fargate, EC2
Storage Optimization:
- EBS: Data Lifecycle Manager with automated retention, 8TB archived (75% reduction), 4TB orphaned snapshots deleted, $42K savings
- S3: 28TB to Intelligent-Tiering, 12TB to Glacier Deep Archive, object expiration for temporary data, $65K savings
Lambda Optimization:
Memory rightsizing (1024MB→512MB for 180 functions), Graviton2 migration (15% faster, 20% cheaper), provisioned concurrency for 25 latency-sensitive functions (83% P95 latency reduction), $18K savings
Workflow Orchestration
Continuous Optimization:
- Daily: Scan 47 accounts, analyze spending anomalies, generate prioritized recommendations with ROI
- Weekly: Top 50 cost drivers analysis, trend comparisons, RI/Savings Plans coverage review
- Monthly: Executive assessment, quarterly forecast, new service evaluation, strategy updates
- Automated Remediation: Low-risk auto-execute, medium-risk single approval, high-risk change management, full audit logging
Security and Compliance
Encryption: AES-256 (RDS, S3, OpenSearch) with customer-managed KMS keys, 90-day rotation, TLS 1.3 for transit, VPC endpoints
Access Control: Least-privilege IAM roles (bedrock:InvokeModel, ce:GetCostAndUsage, ec2:DescribeInstances), AWS Organizations with SCPs, cross-account roles, automated tagging
Audit: CloudTrail logging (90-day S3 retention, Glacier archival), SIEM integration, SOC 2/ISO 27001/GDPR/CCPA compliance, cost allocation tags
Monitoring and Operations
Real-time Monitoring: CloudWatch dashboards (5-min granularity), service-level breakdowns, anomaly detection (>15% variance alerts), custom optimization KPIs
Reporting: Executive dashboards (monthly trends, YoY comparisons, 34% reduction metrics), engineering insights (per-team allocation, rightsizing status, RI coverage)
Measurable Impact
Cost Savings:
- 34% reduction: $2.4M→$1.58M ($816K annual savings), 100% availability maintained, 1,850% ROI
Operational Efficiency:
- 93% time reduction: 120→8 hours monthly, 85% task automation, 5x faster decisions
Resource Optimization:
- EC2: $180K rightsizing + $85K scheduling + $120K Spot + $210K RI = $595K
- Storage: $42K EBS + $65K S3 = $107K
- Lambda: $18K optimization
Strategic Value: Proactive management with 92% forecast accuracy, FinOps Level 3 maturity, finance-engineering collaboration, repeatable framework
Conclusion
This AI-powered architecture demonstrates how Amazon Bedrock and AWS MCP server transform cloud financial management from manual processes to automated intelligence. By analyzing 2.5M data points monthly, the solution achieved 34% cost reduction ($816K savings) while reducing analysis time by 93%. The combination of Bedrock's automation, AWS Cost Management APIs, and scalable infrastructure delivers enterprise-scale optimization—turning cloud spending from liability into strategic advantage.