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/aws-billing-and-cost-management

@b33847d

Analyze AWS costs, find savings, manage budgets, evaluate Savings Plans and Reserved Instances, right-size EC2/Lambda/RDS/EBS with Compute Optimizer, look up service pricing, query CUR with Athena, detect cost anomalies, scope costs to billing views, and monitor Free Tier usage. Triggers on: AWS bill, cost analysis, reduce spend, savings plan, reserved instance, right-size, budget alert, cost optimization, pricing, free tier, cost anomaly, CUR, cost audit, billing view, billing view ARN.

Use this Skill: https://skilld.dev/gh/aws/agent-toolkit-for-aws/aws-billing-and-cost-management

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referencessavings-plans.md

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Savings Plans

Pricing note: All prices shown are approximate as of early 2026 and may change. Always verify current pricing via the Price List API before reporting to users.

Plan Types

Type Discount Flexibility Covers
Compute SP Up to 66% Any family, size, region, OS EC2, Fargate, Lambda
EC2 Instance SP Up to 72% Any size, OS within family+region EC2 only
Database SP Up to 35% Any engine, family, size, region Aurora, RDS, DynamoDB, ElastiCache, DocumentDB, Neptune, Keyspaces, Timestream, DMS, OpenSearch
SageMaker SP Up to 64% Any family, size, region SageMaker

Default recommendation: Compute SP for most users. The 6% discount gap vs EC2 Instance SP is not worth the inflexibility.

Default payment: No Upfront for first-time buyers to minimize risk.

How Recommendations Are Calculated

The recommendation engine analyzes usage over a lookback period (7, 30, or 60 days), considering every usage hour including nights and weekends. It selects a commitment ($/hr) that maximizes savings while maintaining high utilization.

Utilization = committed dollars used ÷ committed dollars purchased. Target >95%.

Savings = On-Demand cost − (SP cost + remaining On-Demand cost).

Savings compare to On-Demand prices only. The estimatedMonthlyCost and estimatedMonthlySavings in Cost Optimization Hub are monthly figures. The EstimatedOnDemandCostWithCurrentCommitment in additional details covers the lookback period — do NOT conflate lookback-period costs with monthly costs.

SP vs Reserved Instances

Feature Savings Plans Reserved Instances
Flexibility High (Compute SP covers EC2+Fargate+Lambda) Low (service-specific)
Capacity reservation No Yes (AZ-scoped RI — Standard or Convertible)
Marketplace resale No Yes (Standard RI only)
AWS recommendation Preferred Legacy, still supported

SPs apply AFTER RI discounts. SPs do NOT apply to Spot usage.

Use RIs only when: capacity reservation needed in specific AZ, want to sell on Marketplace, or very stable single-instance-type workload.

Gotchas

  • 7-day return window (conditional): SPs with hourly commitment ≤$100, purchased in the past 7 days AND in the same calendar month, can be returned for a full refund. Usage covered by the returned plan is re-rated to On-Demand. Outside this window, commitment is binding for the full term.
  • Compute SP does NOT cover RDS — use Database SP
  • SP doesn't provide capacity reservation — use ODCR separately
  • EKS control plane ($0.10/hr) is NOT covered by any SP
  • DynamoDB Reserved Capacity is deprecated in favor of Database SP
  • Start with Cost Explorer recommendations — they analyze actual usage patterns

CLI Commands

# Get SP purchase recommendation
aws ce get-savings-plans-purchase-recommendation \
  --savings-plans-type COMPUTE_SP \
  --term-in-years ONE_YEAR \
  --payment-option NO_UPFRONT \
  --lookback-period-in-days SIXTY_DAYS

# Check utilization
aws ce get-savings-plans-utilization \
  --time-period Start=2026-03-01,End=2026-04-01 \
  --granularity MONTHLY

# Check coverage
aws ce get-savings-plans-coverage \
  --time-period Start=2026-03-01,End=2026-04-01 \
  --granularity MONTHLY

Source: SKILL.md on GitHub

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    This skill provides comprehensive instructions for analyzing and optimizing AWS costs. It incorporates security-conscious patterns, such as recommending sandboxed execution via the AWS MCP server and requiring deterministic Python scripts for all mathematical calculations to ensure data integrity and accuracy.

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