cost-optimization

wshobson ｜ 雲端與基礎設施 ｜ 原文 ｜ MIT

透過 rightsizing、標籤、預留執行個體和支出分析，降低 AWS/Azure/GCP/OCI 成本

---
name: cost-optimization
description: 透過資源 rightsizing、標籤策略、預留執行個體和支出分析，最佳化 AWS、Azure、GCP 和 OCI 的雲端成本。在降低雲端支出、分析基礎設施成本，或實施成本治理政策時使用。
---

# 雲端成本最佳化

最佳化 AWS、Azure、GCP 和 OCI 雲端成本的策略與模式。

## 目的

實施有系統的成本最佳化策略，在維持效能和可靠性的同時降低雲端支出。

## 何時使用

- 降低雲端支出
- 資源 right-sizing
- 實施成本治理
- 最佳化多雲成本
- 符合預算限制

## 成本最佳化框架

### 1. 可見性

- 實作成本分配標籤
- 使用雲端成本管理工具
- 設定預算警示
- 建立成本 dashboard

### 2. Right-Sizing

- 分析資源使用率
- 縮減過度配置的資源
- 使用 auto-scaling
- 移除閒置資源

### 3. 定價模型

- 使用預留容量
- 善用 spot/preemptible 執行個體
- 實施 savings plan
- 使用承諾使用折扣

### 4. 架構最佳化

- 使用託管服務
- 實作快取
- 最佳化資料傳輸
- 使用 lifecycle policy

## AWS 成本最佳化

### Reserved Instances

```
Savings: 30-72% vs On-Demand
Term: 1 or 3 years
Payment: All/Partial/No upfront
Flexibility: Standard or Convertible
```

### Savings Plans

```
Compute Savings Plans: 66% savings
EC2 Instance Savings Plans: 72% savings
Applies to: EC2, Fargate, Lambda
Flexible across: Instance families, regions, OS
```

### Spot Instances

```
Savings: Up to 90% vs On-Demand
Best for: Batch jobs, CI/CD, stateless workloads
Risk: 2-minute interruption notice
Strategy: Mix with On-Demand for resilience
```

### S3 成本最佳化

```hcl
resource "aws_s3_bucket_lifecycle_configuration" "example" {
  bucket = aws_s3_bucket.example.id

  rule {
    id     = "transition-to-ia"
    status = "Enabled"

    transition {
      days          = 30
      storage_class = "STANDARD_IA"
    }

    transition {
      days          = 90
      storage_class = "GLACIER"
    }

    expiration {
      days = 365
    }
  }
}
```

## Azure 成本最佳化

### Reserved VM Instances

- 1 年或 3 年期
- 最高 72% 的節省
- 彈性的規格
- 可以交換

### Azure Hybrid Benefit

- 使用既有的 Windows Server 授權
- 搭配 RI 最高節省 80%
- 適用於 Windows 和 SQL Server

### Azure Advisor 建議

- Right-size VM
- 刪除未使用的資源
- 使用預留容量
- 最佳化儲存

## GCP 成本最佳化

### Committed Use Discounts

- 1 年或 3 年的承諾
- 最高 57% 的節省
- 適用於 vCPU 和記憶體
- 以資源或以支出為基礎

### Sustained Use Discounts

- 自動折扣
- 執行中的執行個體最高 30%
- 不需要承諾
- 適用於 Compute Engine、GKE

### Preemptible VMs

- 最高 80% 的節省
- 最長執行 24 小時
- 最適合批次工作負載

## OCI 成本最佳化

### Flexible Shapes

- 獨立擴縮 OCPU 和記憶體
- 讓執行個體規格符合工作負載的需求
- 減少固定 VM 規格造成的容量浪費

### 承諾與預算

- 對可預測的支出使用年度承諾
- 設定 compartment 層級、帶警示的預算
- 用 OCI Cost Analysis 追蹤每月的預測

### Preemptible 容量

- 對批次和短暫的工作負載使用 preemptible 執行個體
- 保留能容忍中斷的 autoscaling group
- 對關鍵服務，混用標準容量

## 標籤策略

### AWS 標籤

```hcl
locals {
  common_tags = {
    Environment = "production"
    Project     = "my-project"
    CostCenter  = "engineering"
    Owner       = "team@example.com"
    ManagedBy   = "terraform"
  }
}

resource "aws_instance" "example" {
  ami           = "ami-12345678"
  instance_type = "t3.medium"

  tags = merge(
    local.common_tags,
    {
      Name = "web-server"
    }
  )
}
```

**參考：** 見 `references/tagging-standards.md`

## 成本監控

### 預算警示

```hcl
# AWS Budget
resource "aws_budgets_budget" "monthly" {
  name              = "monthly-budget"
  budget_type       = "COST"
  limit_amount      = "1000"
  limit_unit        = "USD"
  time_period_start = "2024-01-01_00:00"
  time_unit         = "MONTHLY"

  notification {
    comparison_operator        = "GREATER_THAN"
    threshold                  = 80
    threshold_type            = "PERCENTAGE"
    notification_type         = "ACTUAL"
    subscriber_email_addresses = ["team@example.com"]
  }
}
```

### 成本異常偵測

- AWS Cost Anomaly Detection
- Azure Cost Management 警示
- GCP Budget 警示
- OCI Budgets 和 Cost Analysis

## 架構模式

### Pattern 1：Serverless 優先

- 事件驅動的工作用 Lambda/Functions
- 只為執行時間付費
- 內含 auto-scaling
- 沒有閒置成本

### Pattern 2：Right-Sized 的資料庫

```
Development: t3.small RDS
Staging: t3.large RDS
Production: r6g.2xlarge RDS with read replicas
```

### Pattern 3：多層儲存

```
Hot data: S3 Standard
Warm data: S3 Standard-IA (30 days)
Cold data: S3 Glacier (90 days)
Archive: S3 Deep Archive (365 days)
```

### Pattern 4：Auto-Scaling

```hcl
resource "aws_autoscaling_policy" "scale_up" {
  name                   = "scale-up"
  scaling_adjustment     = 2
  adjustment_type        = "ChangeInCapacity"
  cooldown              = 300
  autoscaling_group_name = aws_autoscaling_group.main.name
}

resource "aws_cloudwatch_metric_alarm" "cpu_high" {
  alarm_name          = "cpu-high"
  comparison_operator = "GreaterThanThreshold"
  evaluation_periods  = "2"
  metric_name         = "CPUUtilization"
  namespace           = "AWS/EC2"
  period              = "60"
  statistic           = "Average"
  threshold           = "80"
  alarm_actions       = [aws_autoscaling_policy.scale_up.arn]
}
```

## 成本最佳化檢查清單

- [ ] 實作成本分配標籤
- [ ] 刪除未使用的資源（EBS、EIP、snapshot）
- [ ] 依使用率 right-size 執行個體
- [ ] 對穩定的工作負載使用預留容量
- [ ] 實作 auto-scaling
- [ ] 最佳化儲存類別
- [ ] 使用 lifecycle policy
- [ ] 啟用成本異常偵測
- [ ] 設定預算警示
- [ ] 每週檢視成本
- [ ] 使用 spot/preemptible 執行個體
- [ ] 最佳化資料傳輸成本
- [ ] 實作快取層
- [ ] 使用託管服務
- [ ] 持續監控並最佳化

## 工具

- **AWS：** Cost Explorer、Cost Anomaly Detection、Compute Optimizer
- **Azure：** Cost Management、Advisor
- **GCP：** Cost Management、Recommender
- **OCI：** Cost Analysis、Budgets、Cloud Advisor
- **多雲：** CloudHealth、Cloudability、Kubecost


## 相關 Skill

- `terraform-module-library`：用於資源佈建
- `multi-cloud-architecture`：用於雲端選擇

---
name: cost-optimization
description: Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.
---

# Cloud Cost Optimization

Strategies and patterns for optimizing cloud costs across AWS, Azure, GCP, and OCI.

## Purpose

Implement systematic cost optimization strategies to reduce cloud spending while maintaining performance and reliability.

## When to Use

- Reduce cloud spending
- Right-size resources
- Implement cost governance
- Optimize multi-cloud costs
- Meet budget constraints

## Cost Optimization Framework

### 1. Visibility

- Implement cost allocation tags
- Use cloud cost management tools
- Set up budget alerts
- Create cost dashboards

### 2. Right-Sizing

- Analyze resource utilization
- Downsize over-provisioned resources
- Use auto-scaling
- Remove idle resources

### 3. Pricing Models

- Use reserved capacity
- Leverage spot/preemptible instances
- Implement savings plans
- Use committed use discounts

### 4. Architecture Optimization

- Use managed services
- Implement caching
- Optimize data transfer
- Use lifecycle policies

## AWS Cost Optimization

### Reserved Instances

```
Savings: 30-72% vs On-Demand
Term: 1 or 3 years
Payment: All/Partial/No upfront
Flexibility: Standard or Convertible
```

### Savings Plans

```
Compute Savings Plans: 66% savings
EC2 Instance Savings Plans: 72% savings
Applies to: EC2, Fargate, Lambda
Flexible across: Instance families, regions, OS
```

### Spot Instances

```
Savings: Up to 90% vs On-Demand
Best for: Batch jobs, CI/CD, stateless workloads
Risk: 2-minute interruption notice
Strategy: Mix with On-Demand for resilience
```

### S3 Cost Optimization

```hcl
resource "aws_s3_bucket_lifecycle_configuration" "example" {
  bucket = aws_s3_bucket.example.id

  rule {
    id     = "transition-to-ia"
    status = "Enabled"

    transition {
      days          = 30
      storage_class = "STANDARD_IA"
    }

    transition {
      days          = 90
      storage_class = "GLACIER"
    }

    expiration {
      days = 365
    }
  }
}
```

## Azure Cost Optimization

### Reserved VM Instances

- 1 or 3 year terms
- Up to 72% savings
- Flexible sizing
- Exchangeable

### Azure Hybrid Benefit

- Use existing Windows Server licenses
- Up to 80% savings with RI
- Available for Windows and SQL Server

### Azure Advisor Recommendations

- Right-size VMs
- Delete unused resources
- Use reserved capacity
- Optimize storage

## GCP Cost Optimization

### Committed Use Discounts

- 1 or 3 year commitment
- Up to 57% savings
- Applies to vCPUs and memory
- Resource-based or spend-based

### Sustained Use Discounts

- Automatic discounts
- Up to 30% for running instances
- No commitment required
- Applies to Compute Engine, GKE

### Preemptible VMs

- Up to 80% savings
- 24-hour maximum runtime
- Best for batch workloads

## OCI Cost Optimization

### Flexible Shapes

- Scale OCPUs and memory independently
- Match instance sizing to workload demand
- Reduce wasted capacity from fixed VM shapes

### Commitments and Budgets

- Use annual commitments for predictable spend
- Set compartment-level budgets with alerts
- Track monthly forecasts with OCI Cost Analysis

### Preemptible Capacity

- Use preemptible instances for batch and ephemeral workloads
- Keep interruption-tolerant autoscaling groups
- Mix with standard capacity for critical services

## Tagging Strategy

### AWS Tagging

```hcl
locals {
  common_tags = {
    Environment = "production"
    Project     = "my-project"
    CostCenter  = "engineering"
    Owner       = "team@example.com"
    ManagedBy   = "terraform"
  }
}

resource "aws_instance" "example" {
  ami           = "ami-12345678"
  instance_type = "t3.medium"

  tags = merge(
    local.common_tags,
    {
      Name = "web-server"
    }
  )
}
```

**Reference:** See `references/tagging-standards.md`

## Cost Monitoring

### Budget Alerts

```hcl
# AWS Budget
resource "aws_budgets_budget" "monthly" {
  name              = "monthly-budget"
  budget_type       = "COST"
  limit_amount      = "1000"
  limit_unit        = "USD"
  time_period_start = "2024-01-01_00:00"
  time_unit         = "MONTHLY"

  notification {
    comparison_operator        = "GREATER_THAN"
    threshold                  = 80
    threshold_type            = "PERCENTAGE"
    notification_type         = "ACTUAL"
    subscriber_email_addresses = ["team@example.com"]
  }
}
```

### Cost Anomaly Detection

- AWS Cost Anomaly Detection
- Azure Cost Management alerts
- GCP Budget alerts
- OCI Budgets and Cost Analysis

## Architecture Patterns

### Pattern 1: Serverless First

- Use Lambda/Functions for event-driven
- Pay only for execution time
- Auto-scaling included
- No idle costs

### Pattern 2: Right-Sized Databases

```
Development: t3.small RDS
Staging: t3.large RDS
Production: r6g.2xlarge RDS with read replicas
```

### Pattern 3: Multi-Tier Storage

```
Hot data: S3 Standard
Warm data: S3 Standard-IA (30 days)
Cold data: S3 Glacier (90 days)
Archive: S3 Deep Archive (365 days)
```

### Pattern 4: Auto-Scaling

```hcl
resource "aws_autoscaling_policy" "scale_up" {
  name                   = "scale-up"
  scaling_adjustment     = 2
  adjustment_type        = "ChangeInCapacity"
  cooldown              = 300
  autoscaling_group_name = aws_autoscaling_group.main.name
}

resource "aws_cloudwatch_metric_alarm" "cpu_high" {
  alarm_name          = "cpu-high"
  comparison_operator = "GreaterThanThreshold"
  evaluation_periods  = "2"
  metric_name         = "CPUUtilization"
  namespace           = "AWS/EC2"
  period              = "60"
  statistic           = "Average"
  threshold           = "80"
  alarm_actions       = [aws_autoscaling_policy.scale_up.arn]
}
```

## Cost Optimization Checklist

- [ ] Implement cost allocation tags
- [ ] Delete unused resources (EBS, EIPs, snapshots)
- [ ] Right-size instances based on utilization
- [ ] Use reserved capacity for steady workloads
- [ ] Implement auto-scaling
- [ ] Optimize storage classes
- [ ] Use lifecycle policies
- [ ] Enable cost anomaly detection
- [ ] Set budget alerts
- [ ] Review costs weekly
- [ ] Use spot/preemptible instances
- [ ] Optimize data transfer costs
- [ ] Implement caching layers
- [ ] Use managed services
- [ ] Monitor and optimize continuously

## Tools

- **AWS:** Cost Explorer, Cost Anomaly Detection, Compute Optimizer
- **Azure:** Cost Management, Advisor
- **GCP:** Cost Management, Recommender
- **OCI:** Cost Analysis, Budgets, Cloud Advisor
- **Multi-cloud:** CloudHealth, Cloudability, Kubecost


## Related Skills

- `terraform-module-library` - For resource provisioning
- `multi-cloud-architecture` - For cloud selection