cloud-cost-optimization-analyzer

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Analyze and optimize cloud costs across AWS, Azure, GCP with rightsizing, reserved instances, waste detection, and FinOps best practices.

williamzujkowski By williamzujkowski schedule Updated 10/26/2025
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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):

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

    • Group by service/resource type
    • Calculate total spend and trend (% change from previous period)

  2. Quick waste scan (top offenders only)

    • Stopped instances still attached to storage
    • Unattached EBS volumes / Azure managed disks / GCP persistent disks
    • Unused Elastic IPs / Public IPs / Static external IPs
    • Load balancers with zero traffic (last 7 days)

  3. Commitment coverage check

    • Calculate % of compute spend covered by reserved instances / savings plans / committed use discounts
    • If <60% coverage → flag as optimization opportunity

  4. Output quick wins (3 highest impact items)

    • Example: "Delete 15 unattached EBS volumes → save $450/month"
    • Example: "Purchase EC2 Savings Plan (3-year) → save $2,400/month"
    • 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

    • Fetch CloudWatch / Azure Monitor / GCP Monitoring metrics (CPU, memory, network utilization)
    • Identify resources with <20% utilization over 90th percentile
    • Recommend downsizing (example: m5.2xlarge → m5.xlarge saves $150/month)
    • Calculate savings per resource: (current_price - recommended_price) * hours_per_month

  2. Reserved instance / Savings plan optimization

    • Analyze historical usage patterns (30d minimum, 90d preferred)
    • Identify stable workloads eligible for commitments
    • Calculate break-even point: upfront_cost / monthly_savings
    • Recommend commitment term (1-year vs 3-year) based on usage stability
    • AWS: Compare Compute Savings Plans vs EC2 RIs vs convertible RIs
    • Azure: Compare Reserved VM Instances vs Azure Hybrid Benefit
    • GCP: Compare Committed Use Discounts (CUD) vs Sustained Use Discounts (SUD)

  3. Storage optimization

    • Identify candidates for lifecycle policies (hot → cool → archive)
    • AWS S3: Recommend Intelligent-Tiering or S3 Glacier transitions
    • Azure Blob: Recommend tiering to Cool or Archive
    • GCP Cloud Storage: Recommend Nearline or Coldline classes
    • Calculate savings: (current_storage_cost - optimized_cost) * TB_stored

  4. Network cost optimization

    • Identify cross-region / cross-AZ traffic (expensive)
    • Recommend VPC endpoints / Private Link to avoid NAT gateway costs
    • Flag public internet egress (most expensive path)

  5. Cost anomaly detection

    • Calculate baseline spend (average + 2 std deviations)
    • Flag spikes >20% above baseline
    • Attribute anomaly to specific service/resource

  6. 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):

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
    • Effort scale: Low (delete unused) < Medium (rightsize) < High (migrate architecture)
  2. Quick wins: Zero-downtime changes (stop unused, delete orphaned) rank highest
  3. 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:

FinOps Foundation resources:

Third-party cost optimization guides:

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)
Install via CLI
npx skills add https://github.com/williamzujkowski/cognitive-toolworks --skill cloud-cost-optimization-analyzer
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