Cloud Cost Optimization Analyzer

name: Cloud Cost Optimization Analyzer
slug: finops-cost-analyzer
description: Analyze and optimize cloud costs across AWS, Azure, GCP with rightsizing, reserved instances, waste detection, and FinOps best practices.
capabilities:
- Multi-cloud cost analysis (AWS, Azure, GCP)
- Resource rightsizing recommendations
- Reserved instance and savings plan optimization
- Waste and idle resource detection
- FinOps framework implementation
- Cost anomaly detection and alerting
inputs:
- cloud_provider: string (aws|azure|gcp|multi)
- cost_data_source: string (billing API, cost explorer, CSV export)
- optimization_targets: array (compute|storage|network|database)
- budget_constraints: object {monthly_budget, alert_threshold}
- time_range: string (7d|30d|90d|custom)
outputs:
- cost_analysis_report: object {current_spend, waste_identified, savings_potential}
- rightsizing_recommendations: array of {resource_id, current_size, recommended_size, savings}
- commitment_recommendations: array of {service, plan_type, term, savings}
- waste_inventory: array of {resource_id, type, idle_days, monthly_cost}
- action_plan: prioritized list of optimization actions
keywords:
- cloud cost optimization
- finops
- rightsizing
- reserved instances
- savings plans
- cost anomaly detection
- waste detection
- TCO optimization
- budget management
version: 1.0.0
owner: cognitive-toolworks
license: MIT
security:
- Read-only access to billing APIs
- No modification of live resources without approval
- Secure handling of cost data (may contain sensitive business info)
- Audit logging of all recommendations
links:
- https://aws.amazon.com/aws-cost-management/
- https://azure.microsoft.com/solutions/cost-optimization/
- https://cloud.google.com/cost-management
- https://www.finops.org/framework/

Purpose & When-To-Use

Primary trigger conditions:

• Monthly cloud bill shows unexpected increase (>10% variance)
• Budget alert fired indicating overspend
• Regular cost optimization review cycle (quarterly FinOps practice)
• Pre-budget planning phase requires accurate TCO estimates
• Executive request for cloud cost reduction initiatives
• Migration to cloud requires cost modeling
• Reserved instance or savings plan renewal decision needed

When NOT to use this skill:

• Real-time cost tracking (use native dashboards instead)
• One-time spot instance pricing lookup (use pricing calculators)
• Architecture design phase (use cloud-aws-architect first)

Value proposition: Identifies 30-40% of typical cloud waste through systematic analysis of rightsizing, commitment discounts, and idle resources using FinOps principles.

Pre-Checks

Required inputs validation:

NOW_ET = "2025-10-25T22:42:30-04:00"

assert cloud_provider in ["aws", "azure", "gcp", "multi"], "Invalid cloud provider"
assert cost_data_source is not None, "Cost data source required"
assert time_range in ["7d", "30d", "90d"] or matches_custom_format(time_range)
assert len(optimization_targets) > 0, "At least one optimization target required"

# Data freshness check
if cost_data_age > 24h:
    warn("Cost data is stale; recommendations may be outdated")

# Minimum data volume check
if time_range == "7d" and total_resources < 10:
    suggest("Use 30d+ range for better trend analysis")

Authority checks:

• AWS: Cost Explorer API enabled, ce:GetCostAndUsage permission
• Azure: Cost Management API access, Reader role on subscriptions
• GCP: Cloud Billing API enabled, billing.accounts.getSpendingInformation permission

Source citations (accessed 2025-10-25T22:42:30-04:00):

• AWS Cost Management: https://aws.amazon.com/aws-cost-management/
• Azure Cost Optimization: https://azure.microsoft.com/solutions/cost-optimization/
• GCP Cost Management: https://cloud.google.com/cost-management
• FinOps Framework: https://www.finops.org/framework/
• FinOps Principles: https://www.finops.org/framework/principles/

Procedure

Tier 1 (≤2k tokens): Quick Cost Health Check

Goal: Identify top 3 cost optimization opportunities in <5 minutes.

Steps:

1. Fetch cost summary for specified time_range
2. Group by service/resource type

3. Calculate total spend and trend (% change from previous period)

4. Quick waste scan (top offenders only)

5. Stopped instances still attached to storage
6. Unattached EBS volumes / Azure managed disks / GCP persistent disks
7. Unused Elastic IPs / Public IPs / Static external IPs

8. Load balancers with zero traffic (last 7 days)

9. Commitment coverage check

10. Calculate % of compute spend covered by reserved instances / savings plans / committed use discounts

11. If <60% coverage → flag as optimization opportunity

12. Output quick wins (3 highest impact items)

13. Example: "Delete 15 unattached EBS volumes → save $450/month"
14. Example: "Purchase EC2 Savings Plan (3-year) → save $2,400/month"
15. Example: "Rightsize 8 over-provisioned RDS instances → save $1,200/month"

Token budget checkpoint: ~1.5k tokens for API calls, analysis, and output formatting.

Tier 2 (≤6k tokens): Comprehensive Cost Analysis

Goal: Generate detailed, actionable cost optimization plan with quantified savings.

Extends T1 with:

1. Rightsizing analysis
2. Fetch CloudWatch / Azure Monitor / GCP Monitoring metrics (CPU, memory, network utilization)
3. Identify resources with <20% utilization over 90th percentile
4. Recommend downsizing (example: m5.2xlarge → m5.xlarge saves $150/month)

5. Calculate savings per resource: (current_price - recommended_price) * hours_per_month

6. Reserved instance / Savings plan optimization

7. Analyze historical usage patterns (30d minimum, 90d preferred)
8. Identify stable workloads eligible for commitments
9. Calculate break-even point: upfront_cost / monthly_savings
10. Recommend commitment term (1-year vs 3-year) based on usage stability
11. AWS: Compare Compute Savings Plans vs EC2 RIs vs convertible RIs
12. Azure: Compare Reserved VM Instances vs Azure Hybrid Benefit

13. GCP: Compare Committed Use Discounts (CUD) vs Sustained Use Discounts (SUD)

14. Storage optimization

15. Identify candidates for lifecycle policies (hot → cool → archive)
16. AWS S3: Recommend Intelligent-Tiering or S3 Glacier transitions
17. Azure Blob: Recommend tiering to Cool or Archive
18. GCP Cloud Storage: Recommend Nearline or Coldline classes

19. Calculate savings: (current_storage_cost - optimized_cost) * TB_stored

20. Network cost optimization

21. Identify cross-region / cross-AZ traffic (expensive)
22. Recommend VPC endpoints / Private Link to avoid NAT gateway costs

23. Flag public internet egress (most expensive path)

24. Cost anomaly detection

25. Calculate baseline spend (average + 2 std deviations)
26. Flag spikes >20% above baseline

27. Attribute anomaly to specific service/resource

28. FinOps maturity assessment (basic)

    • Tag compliance: % resources with required cost allocation tags
    • Budget variance: actual vs budgeted spend
    • Commitment utilization: % of purchased RI/SP actually used
    • Waste ratio: idle_cost / total_cost

Authority sources (accessed 2025-10-25T22:42:30-04:00):

• AWS Reserved Instances: https://aws.amazon.com/ec2/pricing/reserved-instances/
• AWS Savings Plans: https://aws.amazon.com/savingsplans/ (up to 72% savings)
• Azure Reserved Instances: https://learn.microsoft.com/azure/cost-management-billing/reservations/
• Azure Hybrid Benefit: https://azure.microsoft.com/pricing/hybrid-benefit/ (up to 36% for Windows Server)
• GCP Committed Use Discounts: https://cloud.google.com/compute/docs/instances/committed-use-discounts-overview
• FinOps "Crawl, Walk, Run" maturity: https://www.finops.org/framework/

Output: JSON report with sections: cost_summary, quick_wins (T1), rightsizing_recommendations, commitment_recommendations, storage_optimization, network_optimization, anomalies, finops_maturity_score.

Token budget checkpoint: ~5k tokens (includes T1 + extended analysis + detailed output).

Tier 3 (not implemented for T2 skill)

Reserved for future enhancements: predictive cost forecasting, ML-based anomaly detection, multi-account/org-wide consolidation, custom FinOps policies.

Decision Rules

When to abort:

• Cost data source returns 403/401 → insufficient permissions; emit setup instructions
• Cost data empty or <7 days → insufficient data for analysis
• All optimization targets already at maximum efficiency (rare) → report "no action needed"

Ambiguity thresholds:

• Rightsizing confidence: Only recommend if utilization <20% for 90% of time period (avoids false positives from bursty workloads)
• Commitment recommendations: Require 90d minimum history AND <10% variance in daily usage to recommend 3-year term
• Anomaly detection: Only flag if >20% deviation AND >$100 absolute difference (avoid noise)

Prioritization logic:

1. Highest ROI first: savings_per_month / implementation_effort
2. Effort scale: Low (delete unused) < Medium (rightsize) < High (migrate architecture)
3. Quick wins: Zero-downtime changes (stop unused, delete orphaned) rank highest
4. Risk-adjusted: Downsizing production workloads requires manual approval; rank lower

FinOps principle application (accessed 2025-10-25T22:42:30-04:00):

Per FinOps Foundation principles (https://www.finops.org/framework/principles/):

• "Everyone takes ownership": Tag all recommendations with owning team/project via cost allocation tags
• "Centralized optimization": Reserved instance / savings plan purchases centralized; this skill generates recommendations for central FinOps team
• "Variable cost model as opportunity": Emphasize autoscaling and spot instances as cost-saving strategies

Output Contract

Schema (JSON):

{
  "cost_analysis_report": {
    "period": "2025-09-25 to 2025-10-25",
    "cloud_provider": "aws",
    "total_spend": 47500.32,
    "waste_identified": 14250.10,
    "savings_potential": {
      "monthly": 12800.00,
      "annual": 153600.00,
      "percentage": 26.9
    }
  },
  "quick_wins": [
    {
      "category": "unused_resources",
      "description": "Delete 12 unattached EBS volumes",
      "monthly_savings": 360.00,
      "implementation_effort": "low",
      "risk_level": "none"
    }
  ],
  "rightsizing_recommendations": [
    {
      "resource_id": "i-0a1b2c3d4e5f6g7h8",
      "resource_type": "ec2_instance",
      "current_type": "m5.2xlarge",
      "recommended_type": "m5.xlarge",
      "utilization_avg": 18.5,
      "monthly_savings": 150.00,
      "confidence": "high"
    }
  ],
  "commitment_recommendations": [
    {
      "service": "ec2_compute",
      "plan_type": "compute_savings_plan",
      "term": "3_year",
      "upfront": "partial",
      "monthly_commitment": 5000.00,
      "monthly_savings": 1200.00,
      "break_even_months": 4.2
    }
  ],
  "waste_inventory": [
    {
      "resource_id": "vol-0123456789abcdef",
      "type": "ebs_volume_unattached",
      "idle_days": 45,
      "monthly_cost": 30.00
    }
  ],
  "action_plan": [
    {
      "priority": 1,
      "action": "Delete unused resources",
      "impact": "high",
      "effort": "low",
      "items_count": 27,
      "monthly_savings": 810.00
    }
  ]
}

Required fields: cost_analysis_report (with total_spend, savings_potential), action_plan (prioritized).

Optional fields: rightsizing_recommendations, commitment_recommendations (only if applicable).

Examples

# Example: AWS cost optimization for over-provisioned workload
input:
  cloud_provider: aws
  cost_data_source: cost_explorer_api
  optimization_targets: [compute, storage, network]
  budget_constraints:
    monthly_budget: 50000
    alert_threshold: 0.90
  time_range: 90d

output:
  cost_analysis_report:
    total_spend: 47500.32
    waste_identified: 14250.10
    savings_potential:
      monthly: 12800
      annual: 153600
      percentage: 26.9
  quick_wins:
    - category: unused_resources
      description: Delete 12 unattached EBS volumes
      monthly_savings: 360
  rightsizing_recommendations:
    - resource_id: i-0a1b2c3d4e5f
      current_type: m5.2xlarge
      recommended_type: m5.xlarge
      utilization_avg: 18.5
      monthly_savings: 150

Quality Gates

Token budgets (enforced):
- T1: ≤2,000 tokens - quick health check with top 3 cost optimization opportunities
- T2: ≤6,000 tokens - comprehensive analysis with rightsizing, commitments, storage optimization, anomalies, and FinOps maturity
- T3: (not implemented) - reserved for predictive cost forecasting, ML-based anomaly detection, multi-account consolidation

Accuracy requirements:

• Cost calculations must match cloud provider billing (±2% tolerance)
• Rightsizing recommendations validated against 90th percentile utilization metrics
• Commitment savings verified against current pricing (as of NOW_ET)

Safety constraints:

• No automatic resource deletion: All recommendations require human approval
• Production safeguards: Flag production resources; require elevated approval for changes
• Audit trail: Log all API calls and recommendations with timestamps

Auditability:

• Cite source for all pricing data (AWS Pricing API, Azure Rate Card, GCP Pricing Calculator)
• Include confidence scores for probabilistic recommendations (rightsizing, anomalies)
• Record baseline metrics used for comparisons

Determinism:

• Same inputs + same cost data → same recommendations
• Cost anomaly thresholds configurable (default: 20% deviation, $100 minimum)

Resources

Official cloud provider documentation:

• AWS Cost Optimization Hub: https://docs.aws.amazon.com/cost-optimization-hub/
• AWS Trusted Advisor: https://aws.amazon.com/premiumsupport/technology/trusted-advisor/
• AWS Cost Explorer: https://aws.amazon.com/aws-cost-management/aws-cost-explorer/
• Azure Cost Management: https://learn.microsoft.com/azure/cost-management-billing/
• Azure Advisor: https://learn.microsoft.com/azure/advisor/advisor-cost-recommendations
• GCP Recommender: https://cloud.google.com/recommender/docs
• GCP Cloud Billing: https://cloud.google.com/billing/docs

FinOps Foundation resources:

• FinOps Framework: https://www.finops.org/framework/
• FinOps Principles (6 core tenets): https://www.finops.org/framework/principles/
• FinOps Maturity Model: https://www.finops.org/framework/maturity-model/
• FinOps Personas: https://www.finops.org/framework/personas/

Third-party cost optimization guides:

• Cloud cost optimization best practices 2025: https://www.cloudzero.com/blog/aws-cost-management-best-practices/ (accessed 2025-10-25T22:42:30-04:00)
• Azure cost optimization tactics: https://cast.ai/blog/azure-cost-optimization/ (accessed 2025-10-25T22:42:30-04:00)
• GCP cost optimization tools: https://www.cloudzero.com/blog/gcp-cost-optimization-tools/ (accessed 2025-10-25T22:42:30-04:00)

Related skills:

• cloud-aws-architect: For architecture-level cost optimization during design phase
• devops-pipeline-architect: For CI/CD cost optimization (ephemeral environments)
• cloud-native-deployment-orchestrator: For Kubernetes cost optimization (cluster rightsizing)