aws-cost-optimization

name	aws-cost-optimization
description	Evaluate AWS costs and generate actionable optimization suggestions. Covers EC2, S3, RDS, Lambda, ECS/EKS, and data transfer. Use when analyzing AWS spending, right-sizing resources, choosing pricing models (RI/Savings Plans/Spot), implementing tagging strategies, setting budget alerts, or auditing infrastructure for cost waste.

AWS Cost Optimization

Evaluate current AWS costs and produce actionable optimization suggestions across compute, storage, networking, and managed services.

Do not use this skill when

• The task targets Azure, GCP, or a non-AWS provider
• The task is about general application performance without a cost dimension

Instructions

1. Identify the AWS services in scope (EC2, S3, RDS, Lambda, etc.).
2. Apply the Cost Optimization Framework top-to-bottom: Visibility → Right-Sizing → Pricing → Architecture.
3. For each service, check the Service-Specific Quick Reference below.
4. Generate a prioritized list of suggestions with estimated savings.
5. For Terraform/CLI/boto3 implementation details, open references/implementation-playbook.md.

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Cost Optimization Framework

1. Visibility — Know What You Spend

Action	Tool / Service
Cost allocation tags on every resource	AWS Tag Editor, Tag Policies
Monthly/daily spend dashboards	Cost Explorer, QuickSight
Budget alerts at 50 %, 80 %, 100 %	AWS Budgets
Anomaly detection	AWS Cost Anomaly Detection
Per-team / per-env cost breakdown	Cost Categories, Linked Accounts

2. Right-Sizing — Stop Over-Provisioning

Signal	Action
CPU < 20 % sustained	Downsize instance family or switch to Graviton
Memory < 30 % sustained	Use memory-optimized → general purpose
EBS IOPS < provisioned	Switch gp3 / reduce provisioned IOPS
Idle resource (EIP, ELB, NAT, EBS)	Delete or stop
Lambda memory > 2× needed	Run aws lambda get-function-configuration and tune

Use AWS Compute Optimizer and Trusted Advisor for automated right-sizing recommendations.

3. Pricing Models — Pay Less Per Unit

Model	Savings	Best For	Commitment
On-Demand	0 %	Spiky, unpredictable	None
Savings Plans (Compute)	up to 66 %	Steady compute (EC2, Fargate, Lambda)	1 or 3 yr
Savings Plans (EC2 Instance)	up to 72 %	Known instance family & region	1 or 3 yr
Reserved Instances (Standard)	up to 72 %	Steady-state, known type	1 or 3 yr
Reserved Instances (Convertible)	up to 54 %	Steady-state, flexible type	1 or 3 yr
Spot Instances	up to 90 %	Fault-tolerant batch, CI/CD, HPC	None (2-min notice)

Decision heuristic:

1. Steady 24/7 → Savings Plan or RI
2. Batch / stateless → Spot with On-Demand fallback
3. Unknown workload → start On-Demand, analyze with Cost Explorer, then commit

4. Architecture — Spend Smarter

Pattern	Why
Serverless First	Zero idle cost; Lambda, Step Functions, EventBridge
Graviton (ARM) instances	20-40 % cheaper at same performance
Multi-tier S3 storage	Auto-transition hot → IA → Glacier → Deep Archive
Caching (ElastiCache, CloudFront)	Reduce origin hits and data transfer
VPC Endpoints	Eliminate NAT Gateway data processing charges
Regional consolidation	Reduce cross-region transfer costs

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Service-Specific Quick Reference

EC2

• Use Graviton (c7g, m7g, r7g) for 20-40 % cost reduction.
• Mix Spot + On-Demand via Capacity-Optimized allocation in ASGs.
• Enable auto-scaling with target tracking (CPU 60-70 %).
• Schedule dev/staging instances off-hours with Instance Scheduler.

S3

Storage Class	Use Case	vs Standard
Standard	Frequently accessed	baseline
Standard-IA	Accessed < 1×/month	–45 %
One Zone-IA	Non-critical, infrequent	–60 %
Glacier Instant Retrieval	Quarterly access, ms retrieval	–68 %
Glacier Flexible Retrieval	Annual access, hours retrieval	–78 %
Deep Archive	Compliance / 7-yr retention	–95 %

Implement S3 Intelligent-Tiering when access patterns are unpredictable. Use S3 Lifecycle rules for deterministic transitions (see playbook).

RDS / Aurora

Environment	Recommended Tier
Development	db.t4g.micro – db.t4g.small
Staging	db.t4g.medium – db.t4g.large
Production	db.r7g.xlarge + read replicas

• Use Aurora Serverless v2 for variable traffic.
• Enable RDS Reserved Instances for production.
• Use Aurora I/O-Optimized if I/O costs > 25 % of total DB cost.

Lambda

• Right-size memory with AWS Lambda Power Tuning.
• Use Graviton2 (arm64 architecture) for ~34 % cost reduction.
• Enable Provisioned Concurrency only when cold-start SLA < 100 ms.
• Prefer Step Functions over chained Lambdas to avoid idle billing.

ECS / EKS

• Use Fargate Spot for fault-tolerant tasks (up to 70 % savings).
• Use Compute Savings Plans for steady Fargate workloads.
• For EKS: enable Karpenter for intelligent node provisioning.
• Right-size task/pod CPU and memory with Container Insights.

Data Transfer

Path	Cost	Mitigation
Same AZ	Free	Co-locate services
Cross-AZ	$0.01/GB each way	Use AZ-aware routing
Internet egress	$0.09/GB first 10 TB	CloudFront ($0.085/GB), S3 Transfer Acceleration
Cross-region	$0.02/GB	Consolidate regions; use Global Accelerator
NAT Gateway processing	$0.045/GB	VPC Endpoints for S3/DynamoDB

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Tagging Strategy (Mandatory)

Every AWS resource MUST have these tags:

Tag Key	Example	Purpose
Environment	production	Filter by env
Project	navigator	Cost allocation
CostCenter	engineering	Chargeback
Owner	[e-mail]	Accountability
ManagedBy	terraform	Audit

Enforce via AWS Organizations Tag Policies and SCP deny rules.

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Cost Evaluation Workflow

When asked to evaluate costs, follow this sequence:

1. Inventory: List services, instance types, storage volumes, and data flows.
2. Tag audit: Check for missing cost-allocation tags.
3. Utilization check: Review CloudWatch metrics (CPU, memory, IOPS, network).
4. Pricing check: Compare current pricing model vs optimal (RI/SP/Spot).
5. Architecture review: Identify unnecessary data transfer, missing caching, idle resources.
6. Report: Produce a table of findings with:
   • Resource / Service
   • Current monthly cost (estimated or from Cost Explorer)
   • Suggested action
   • Estimated savings (% and $)
   • Effort (low / medium / high)
   • Risk (low / medium / high)

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Tools

• AWS Cost Explorer — Spend trends, forecasting, RI/SP recommendations
• AWS Compute Optimizer — EC2, EBS, Lambda right-sizing
• AWS Trusted Advisor — Idle resources, under-utilized instances
• AWS Cost Anomaly Detection — ML-based spend anomaly alerts
• AWS Budgets — Threshold alerts and auto-actions
• Kubecost — Kubernetes cost allocation (EKS)

Implementation Details

For Terraform, AWS CLI, and boto3 code examples, see implementation-playbook.md.

Related Skills

• aws-serverless — Serverless architecture patterns (Lambda, API Gateway, DynamoDB)
• production-dockerfile — Containerization best practices