gh-anton-abyzov-specweave-plugins-specweave-cost-optimizer/skills/aws-cost-expert/SKILL.md

---
name: aws-cost-expert
description: Deep AWS cost optimization expertise covering EC2 Reserved Instances, Savings Plans, Spot Instances, Lambda cost optimization, S3 lifecycle policies, RDS Reserved Instances, Cost Explorer, AWS Budgets, Trusted Advisor, Compute Optimizer, Cost Anomaly Detection, and AWS-specific FinOps best practices. Activates for AWS costs, AWS pricing, EC2 costs, Lambda costs, S3 costs, RDS costs, AWS savings plans, AWS reserved instances, AWS spot instances, AWS cost explorer, AWS budgets, reduce AWS bill.
---

# AWS Cost Optimization Expert

Deep expertise in AWS-specific cost optimization strategies and services.

## AWS Cost Management Services

### 1. Cost Explorer
```bash
# Get monthly costs by service
aws ce get-cost-and-usage \
  --time-period Start=2025-01-01,End=2025-02-01 \
  --granularity MONTHLY \
  --metrics BlendedCost \
  --group-by Type=SERVICE

# Get EC2 costs by instance type
aws ce get-cost-and-usage \
  --time-period Start=2025-01-01,End=2025-02-01 \
  --granularity DAILY \
  --metrics UnblendedCost \
  --filter file://ec2-filter.json \
  --group-by Type=INSTANCE_TYPE
```

### 2. AWS Budgets
```yaml
Budget Configuration:
  - Monthly budget: $30,000
  - Alert at 80% ($24,000)
  - Alert at 90% ($27,000)
  - Alert at 100% ($30,000)
  - Alert at 110% ($33,000) - critical
  
Actions:
  - Stop non-production instances
  - Deny new resource creation
  - Email C-level executives
```

### 3. Compute Optimizer
```bash
# Get EC2 right-sizing recommendations
aws compute-optimizer get-ec2-instance-recommendations \
  --max-results 100

# Get Lambda function recommendations
aws compute-optimizer get-lambda-function-recommendations
```

### 4. Trusted Advisor
```bash
# Get cost optimization checks
aws support describe-trusted-advisor-checks \
  --language en \
  --query 'checks[?category==`cost_optimizing`]'

# Check results
aws support describe-trusted-advisor-check-result \
  --check-id <check-id>
```

## EC2 Cost Optimization

### Savings Plans vs Reserved Instances
```typescript
interface Comparison {
  option: string;
  flexibility: string;
  discount: string;
  commitment: string;
  bestFor: string;
}

const options: Comparison[] = [
  {
    option: 'On-Demand',
    flexibility: 'Maximum',
    discount: '0%',
    commitment: 'None',
    bestFor: 'Unpredictable workloads',
  },
  {
    option: 'Spot Instances',
    flexibility: 'Medium',
    discount: '50-90%',
    commitment: 'None',
    bestFor: 'Fault-tolerant batch workloads',
  },
  {
    option: 'Compute Savings Plans',
    flexibility: 'High (any instance, any region)',
    discount: '30-70%',
    commitment: '1 or 3 years',
    bestFor: 'Flexible compute usage',
  },
  {
    option: 'EC2 Instance Savings Plans',
    flexibility: 'Medium (same instance family, same region)',
    discount: '35-72%',
    commitment: '1 or 3 years',
    bestFor: 'Consistent instance family usage',
  },
  {
    option: 'Reserved Instances',
    flexibility: 'Low (specific instance type)',
    discount: '40-75%',
    commitment: '1 or 3 years',
    bestFor: 'Predictable, steady-state workloads',
  },
];
```

### Graviton Instances (ARM)
```yaml
Benefits:
  - 20% better price/performance vs x86
  - 40% better price/performance for many workloads
  - Lower power consumption

Migration:
  - t4g (general purpose, burstable)
  - m6g (balanced)
  - c6g (compute optimized)
  - r6g (memory optimized)

Compatibility:
  - Most Linux distributions
  - Container workloads (Docker, ECS, EKS)
  - Not for: Windows, x86-only software
```

## Lambda Cost Optimization

### Power Tuning
```typescript
// Use AWS Lambda Power Tuning tool
// https://github.com/alexcasalboni/aws-lambda-power-tuning

interface PowerTuningResult {
  optimalMemory: number;
  currentCost: number;
  optimalCost: number;
  savings: number;
}

// Example: Image processing function
const result: PowerTuningResult = {
  optimalMemory: 2048, // MB
  currentCost: 0.0000133, // per invocation at 1024MB
  optimalCost: 0.0000119, // per invocation at 2048MB
  savings: 10.5, // % (faster execution despite higher memory cost)
};
```

### Lambda Cost Optimization Checklist
```yaml
Memory Optimization:
  - ✅ Run power tuning for all production functions
  - ✅ Monitor cold start vs warm execution cost
  - ✅ Consider provisioned concurrency for latency-sensitive APIs
  
Architecture:
  - ✅ Avoid VPC Lambda unless necessary (saves NAT costs)
  - ✅ Use Lambda Layers for shared dependencies
  - ✅ Enable Lambda SnapStart for Java functions (faster cold starts)
  
Invocation:
  - ✅ Batch process vs streaming (fewer invocations)
  - ✅ Async invocation where possible
  - ✅ Use Step Functions for orchestration (not nested Lambdas)
```

## S3 Cost Optimization

### Intelligent-Tiering
```yaml
Automatic Cost Optimization:
  - Frequent Access tier (default)
  - Infrequent Access tier (30 days no access)
  - Archive Instant Access (90 days)
  - Archive Access (90-730 days, optional)
  - Deep Archive Access (180-730 days, optional)
  
Monitoring fee: $0.0025 per 1000 objects
Cost: Worth it for > 128KB objects with unpredictable access

Best for:
  - Unknown access patterns
  - Data lakes
  - Long-term storage with occasional access
```

### Lifecycle Policy Example
```json
{
  "Rules": [
    {
      "Id": "Optimize application logs",
      "Status": "Enabled",
      "Filter": { "Prefix": "logs/app/" },
      "Transitions": [
        { "Days": 30, "StorageClass": "STANDARD_IA" },
        { "Days": 90, "StorageClass": "GLACIER_IR" },
        { "Days": 365, "StorageClass": "DEEP_ARCHIVE" }
      ],
      "Expiration": { "Days": 2555 }
    },
    {
      "Id": "Delete incomplete multipart uploads",
      "Status": "Enabled",
      "AbortIncompleteMultipartUpload": {
        "DaysAfterInitiation": 7
      }
    }
  ]
}
```

## RDS Cost Optimization

### Reserved Instance vs Aurora Serverless
```typescript
interface DBCostComparison {
  option: string;
  monthlyCost: number;
  usagePattern: string;
  pros: string[];
  cons: string[];
}

const comparison: DBCostComparison[] = [
  {
    option: 'On-Demand (db.t3.medium)',
    monthlyCost: 50,
    usagePattern: 'Variable, testing',
    pros: ['No commitment', 'Easy to change'],
    cons: ['Highest cost'],
  },
  {
    option: 'Reserved Instance 1yr (db.t3.medium)',
    monthlyCost: 32,
    usagePattern: 'Steady-state, 24/7',
    pros: ['36% savings', 'Predictable cost'],
    cons: ['1-year commitment', 'Capacity reserved'],
  },
  {
    option: 'Aurora Serverless v2',
    monthlyCost: 15,
    usagePattern: 'Intermittent, dev/test',
    pros: ['Auto-scaling', 'Pay per ACU-second', '70% savings for low usage'],
    cons: ['Cold start latency', 'Not for steady 24/7'],
  },
];
```

### RDS Storage Optimization
```yaml
Storage Types:
  gp2 (General Purpose SSD):
    - $0.115/GB/month
    - 3 IOPS per GB (min 100, max 16,000)
    - Burstable to 3,000 IOPS
    
  gp3 (Newer General Purpose SSD):
    - $0.08/GB/month (30% cheaper!)
    - 3,000 IOPS baseline (free)
    - 125 MB/s throughput (free)
    - Additional IOPS: $0.005 per IOPS/month
    - Additional throughput: $0.04 per MB/s/month
    
  io1/io2 (Provisioned IOPS):
    - $0.125/GB + $0.065 per IOPS
    - For high-performance databases

Migration: gp2 → gp3 saves 30% with no performance impact
```

## DynamoDB Cost Optimization

### On-Demand vs Provisioned
```typescript
// Decision matrix
function chooseBillingMode(usage: UsagePattern): string {
  const { requestsPerDay, peakTPS, averageTPS, predictability } = usage;
  
  // On-demand if:
  // - Unpredictable traffic
  // - Spiky workloads
  // - New applications
  // - < 20% peak utilization
  
  if (predictability < 0.5 || (peakTPS / averageTPS) > 2) {
    return 'On-Demand';
  }
  
  // Provisioned if:
  // - Predictable traffic
  // - Steady-state workloads
  // - High utilization (> 20%)
  
  if (predictability > 0.7 && (peakTPS / averageTPS) < 2) {
    return 'Provisioned (with auto-scaling)';
  }
  
  return 'On-Demand (then migrate to Provisioned after 3 months)';
}
```

### Reserved Capacity
```yaml
Savings: 53-76% discount
Commitment: 1 year
Minimum: 100 WCU or RCU

Cost Comparison (100 WCU):
  - On-Demand: $1.25 per 1M writes = $3,600/month (100 writes/sec)
  - Provisioned: 100 WCU * $0.00065/hour * 730 = $47.45/month
  - Reserved: $47.45 * 0.47 = $22.30/month

Best for: Predictable write-heavy workloads
```

## Cost Anomaly Detection

### Setup
```bash
# Create anomaly monitor
aws ce create-anomaly-monitor \
  --anomaly-monitor Name=ProductionMonitor,MonitorType=DIMENSIONAL,MonitorDimension=SERVICE

# Create anomaly subscription
aws ce create-anomaly-subscription \
  --anomaly-subscription Name=ProductionAlerts,MonitorArnList=arn:aws:ce::123456789012:anomalymonitor/abc123,Subscribers=[{Address=team@example.com,Type=EMAIL}],Threshold=100
```

### Anomaly Patterns
```yaml
Common Anomalies:
  - Unexpected EC2 instance launches (compromised credentials)
  - Data transfer spikes (DDoS, misconfigured app)
  - Lambda invocation explosion (infinite loops)
  - S3 GET request flood (hotlinked content)
  - RDS storage growth (missing retention policies)

Alert Thresholds:
  - Service cost: > 50% increase from baseline
  - Daily spend: > 20% above 7-day average
  - Total cost: > 10% above monthly forecast
```

## Tagging Strategy for Cost Allocation

### Tag Policy
```yaml
Required Tags (enforced via AWS Config):
  Environment: [prod, staging, dev, test]
  Team: [platform, api, frontend, data]
  Project: [alpha, beta, gamma]
  CostCenter: [engineering, product, sales]
  Owner: [email@example.com]

Auto-Tagging:
  - Use AWS Organizations tag policies
  - Terraform: default_tags in provider
  - CloudFormation: Tags parameter
  - Lambda: Environment variables → tags
```

### Cost Allocation Tags
```bash
# Activate cost allocation tags
aws ce update-cost-allocation-tags-status \
  --cost-allocation-tags-status TagKey=Environment,Status=Active TagKey=Team,Status=Active

# View costs by tag
aws ce get-cost-and-usage \
  --time-period Start=2025-01-01,End=2025-02-01 \
  --granularity MONTHLY \
  --metrics BlendedCost \
  --group-by Type=TAG,Key=Environment
```

## AWS-Specific Best Practices

### Multi-Account Strategy
```yaml
Organization Structure:
  - Management account (billing only)
  - Production account (prod workloads)
  - Staging account (pre-prod)
  - Development account (dev/test)
  - Shared Services account (logging, monitoring)

Benefits:
  - Consolidated billing (volume discounts)
  - Reserved Instance sharing across accounts
  - Savings Plans apply organization-wide
  - Isolated blast radius
  - Clear cost attribution
```

### AWS Free Tier Monitoring
```bash
# Set up budget for free tier limits
aws budgets create-budget \
  --account-id 123456789012 \
  --budget file://free-tier-budget.json \
  --notifications-with-subscribers file://free-tier-alerts.json
```

Optimize AWS costs like a cloud financial engineer!