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Identify waste, rightsizing opportunities, and reserved instance savings.\n\n## What This Skill Does\n\nWhen given cloud spend data (billing exports, cost explorer screenshots, or manual input), this skill:\n\n1. **Categorizes spend** across 8 cost domains (compute, storage, networking, databases, AI/ML, observability, security, licensing)\n2. **Identifies waste patterns** using 12 common anti-patterns\n3. **Calculates savings** with specific dollar amounts per optimization\n4. **Prioritizes actions** by effort vs. impact (quick wins → strategic moves)\n5. **Generates executive summary** with 90-day roadmap\n\n## Cost Domains & Benchmarks (2026)\n\n### 1. Compute (typically 40-55% of total)\n- **Idle instances**: >30% idle = waste. Benchmark: <10% idle capacity\n- **Rightsizing**: 60% of instances are oversized by 1+ size category\n- **Spot/preemptible**: Batch workloads not on spot = 60-80% overpay\n- **Reserved/savings plans**: On-demand for steady-state = 30-50% overpay\n- **Container density**: <40% CPU utilization on nodes = poor bin-packing\n\n### 2. Storage (typically 10-20%)\n- **Tiering**: Data not accessed in 90 days still on hot storage = 60-80% overpay\n- **Snapshot sprawl**: Orphaned snapshots older than 30 days\n- **Duplicate data**: Cross-region replication without business justification\n- **Object lifecycle**: No lifecycle policies = guaranteed bloat\n\n### 3. Networking (typically 8-15%)\n- **Cross-AZ traffic**: Unnecessary data transfer between zones ($0.01-0.02/GB)\n- **NAT gateway abuse**: High-throughput through NAT vs. VPC endpoints\n- **CDN miss rate**: >20% miss rate = CDN config issue\n- **Egress optimization**: No committed use discounts on egress\n\n### 4. Databases (typically 10-20%)\n- **Over-provisioned RDS/Cloud SQL**: Multi-AZ for dev/staging environments\n- **Read replica sprawl**: Replicas with <5% query load\n- **DynamoDB/Cosmos over-provisioning**: Provisioned capacity 3x+ actual usage\n- **License waste**: Commercial DB when open-source works\n\n### 5. AI/ML Infrastructure (growing — 5-25%)\n- **GPU idle time**: Training instances running 24/7 for 4hr/day workloads\n- **Inference over-provisioning**: GPU instances for CPU-viable inference\n- **Model storage**: Old model versions consuming storage\n- **API costs**: Frontier model API calls without caching layer\n\n### 6. Observability (typically 3-8%)\n- **Log ingestion bloat**: Debug logs in production, duplicate log streams\n- **Metric cardinality**: High-cardinality custom metrics ($$$)\n- **Trace sampling**: 100% trace sampling when 10% suffices\n- **Retention overkill**: 13-month retention for non-compliance data\n\n### 7. Security (typically 2-5%)\n- **WAF rule bloat**: Managed rule groups not actively tuned\n- **Key management**: KMS keys for non-sensitive data\n- **Compliance scanning**: Overlapping tools doing same checks\n\n### 8. Licensing (typically 5-15%)\n- **Shelfware**: Paid seats not logged in 60+ days\n- **Duplicate tools**: Multiple tools solving same problem\n- **Enterprise tiers**: Enterprise features unused, paying enterprise price\n\n## 12 Waste Anti-Patterns\n\n| # | Pattern | Typical Waste | Fix Effort |\n|---|---------|--------------|------------|\n| 1 | Zombie resources (stopped but attached) | 5-15% of bill | Low |\n| 2 | Over-provisioned instances | 15-30% compute | Medium |\n| 3 | No reserved capacity strategy | 25-40% compute | Medium |\n| 4 | Hot storage hoarding | 40-70% storage | Low |\n| 5 | Cross-AZ data transfer abuse | 10-30% network | Medium |\n| 6 | Dev/staging mirrors production | 20-40% of envs | Low |\n| 7 | Orphaned snapshots/AMIs | 3-8% storage | Low |\n| 8 | Log ingestion without sampling | 30-60% observability | Low |\n| 9 | GPU instances for CPU workloads | 70-85% compute | Medium |\n| 10 | No spot/preemptible for batch | 60-80% batch | Medium |\n| 11 | Shelfware licenses | 20-40% licensing | Low |\n| 12 | No tagging = no accountability | Unmeasurable | High |\n\n## Savings Estimation Framework\n\nFor each finding, calculate:\n```\nAnnual Savings = (Current Cost - Optimized Cost) × 12\nImplementation Cost = Engineering Hours × Loaded Rate\nROI = (Annual Savings - Implementation Cost) / Implementation Cost\nPayback Period = Implementation Cost / (Annual Savings / 12)\n```\n\n### Typical Savings by Company Size\n| Company Size | Monthly Cloud Spend | Typical Waste % | Annual Savings |\n|-------------|-------------------|----------------|---------------|\n| Startup (5-15) | $2K-$15K | 35-50% | $8K-$90K |\n| Growth (15-50) | $15K-$80K | 25-40% | $45K-$384K |\n| Mid-market (50-200) | $80K-$500K | 20-35% | $192K-$2.1M |\n| Enterprise (200+) | $500K-$5M+ | 15-25% | $900K-$15M+ |\n\n## Output Format\n\nGenerate a report with:\n1. **Executive Summary**: Total spend, waste identified, savings potential, top 3 quick wins\n2. **Domain Breakdown**: Spend per domain vs. benchmarks\n3. **Findings Table**: Each finding with current cost, optimized cost, savings, effort, priority\n4. **90-Day Roadmap**: Week 1-2 quick wins, Week 3-6 medium effort, Week 7-12 strategic\n5. **Governance Recommendations**: Tagging strategy, budget alerts, review cadence\n\n## Usage\n\nProvide your cloud billing data in any format:\n- AWS Cost Explorer export / Azure Cost Management / GCP Billing\n- Monthly bill summary\n- Architecture description with approximate sizing\n- Or just describe your stack and team size for estimates\n\nThe agent will analyze and produce the full optimization report.\n\n---\n\n## Want Industry-Specific Cloud Optimization?\n\nDifferent industries have different compliance, data residency, and workload patterns that change the optimization calculus entirely.\n\n**Get your industry context pack** — pre-built frameworks for Fintech, Healthcare, Legal, SaaS, Ecommerce, Construction, Real Estate, Recruitment, Manufacturing, and Professional Services.\n\n🛒 Browse packs: https://afrexai-cto.github.io/context-packs/\n🧮 Calculate your AI savings: https://afrexai-cto.github.io/ai-revenue-calculator/\n🤖 Set up your agent: https://afrexai-cto.github.io/agent-setup/\n\n**Bundle deals:**\n- Pick 3 packs: $97\n- All 10 packs: $197\n- Everything bundle: $247\n","readmeExcerpt":"Cloud Cost Optimization Audit Analyze cloud infrastructure spend across AWS, Azure, and GCP. 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