典型的な無駄パターン：
❌ 開発環境が24時間稼働
❌ 使われていないEBSボリューム
❌ 古いスナップショットの蓄積
❌ 過剰なインスタンスサイズ
❌ 不要なロードバランサー

非効率なアーキテクチャ例：
❌ 単一の大きなインスタンス
❌ 適切でないインスタンスタイプ
❌ リザーブドインスタンスの未活用
❌ スポットインスタンスの未活用
❌ 不適切なストレージクラス

管理不備による問題：
❌ コスト監視の仕組みがない
❌ 責任者・予算が不明確
❌ 定期的な見直しがない
❌ アラート設定の不備
❌ タグ付けの不統一

コスト内訳（最適化前）：
- EC2インスタンス: 25万円（50%）
- RDS: 12万円（24%）
- EBS: 8万円（16%）
- その他: 5万円（10%）
問題点：
- 開発環境が24時間稼働: +15万円/月
- 過剰なインスタンスサイズ: +8万円/月
- 不要なスナップショット: +3万円/月
- 非効率なRDS設定: +4万円/月

IaCの利点：
✅ 全リソースの可視化
✅ 設定の標準化
✅ 変更履歴の追跡
✅ 環境間の統一
✅ 自動化による人的ミス削減

動的管理の効果：
✅ 時間ベースの自動スケーリング
✅ 需要に応じたリソース調整
✅ 不要リソースの自動削除
✅ 環境別の最適化設定
✅ コスト予算の自動制御

# variables.tf
variable "environment_schedule" {
  description = "Environment scheduling configuration"
  type = object({
    start_time = string
    stop_time  = string
    timezone   = string
    weekdays   = list(string)
  })
  default = {
    start_time = "09:00"
    stop_time  = "18:00"
    timezone   = "Asia/Tokyo"
    weekdays   = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"]
  }
}
# Lambda function for scheduling
resource "aws_lambda_function" "instance_scheduler" {
  filename         = "instance_scheduler.zip"
  function_name    = "instance-scheduler"
  role            = aws_iam_role.lambda_role.arn
  handler         = "index.handler"
  runtime         = "python3.9"
  timeout         = 60
  environment {
    variables = {
      START_TIME = var.environment_schedule.start_time
      STOP_TIME  = var.environment_schedule.stop_time
      TIMEZONE   = var.environment_schedule.timezone
    }
  }
}
# CloudWatch Events for scheduling
resource "aws_cloudwatch_event_rule" "start_instances" {
  name                = "start-instances"
  description         = "Start instances at specified time"
  schedule_expression = "cron(0 ${split(":", var.environment_schedule.start_time)[1]} ${split(":", var.environment_schedule.start_time)[0]} ? * MON-FRI *)"
}
resource "aws_cloudwatch_event_rule" "stop_instances" {
  name                = "stop-instances"
  description         = "Stop instances at specified time"
  schedule_expression = "cron(0 ${split(":", var.environment_schedule.stop_time)[1]} ${split(":", var.environment_schedule.stop_time)[0]} ? * MON-FRI *)"
}
# Event targets
resource "aws_cloudwatch_event_target" "start_lambda" {
  rule      = aws_cloudwatch_event_rule.start_instances.name
  target_id = "StartInstancesTarget"
  arn       = aws_lambda_function.instance_scheduler.arn
  input = jsonencode({
    action = "start"
  })
}
resource "aws_cloudwatch_event_target" "stop_lambda" {
  rule      = aws_cloudwatch_event_rule.stop_instances.name
  target_id = "StopInstancesTarget"
  arn       = aws_lambda_function.instance_scheduler.arn
  input = jsonencode({
    action = "stop"
  })
}

# instance_scheduler.py
import boto3
import json
import os
from datetime import datetime
def handler(event, context):
ec2 = boto3.client('ec2')
action = event['action']
# 環境タグでフィルタリング
filters = [
{'Name': 'tag:Environment', 'Values': ['development', 'staging']},
{'Name': 'tag:AutoSchedule', 'Values': ['true']}
]
if action == 'start':
# 停止中のインスタンスを起動
response = ec2.describe_instances(
Filters=filters + [{'Name': 'instance-state-name', 'Values': ['stopped']}]
)
instance_ids = []
for reservation in response['Reservations']:
for instance in reservation['Instances']:
instance_ids.append(instance['InstanceId'])
if instance_ids:
ec2.start_instances(InstanceIds=instance_ids)
print(f"Started instances: {instance_ids}")
elif action == 'stop':
# 実行中のインスタンスを停止
response = ec2.describe_instances(
Filters=filters + [{'Name': 'instance-state-name', 'Values': ['running']}]
)
instance_ids = []
for reservation in response['Reservations']:
for instance in reservation['Instances']:
instance_ids.append(instance['InstanceId'])
if instance_ids:
ec2.stop_instances(InstanceIds=instance_ids)
print(f"Stopped instances: {instance_ids}")
return {
'statusCode': 200,
'body': json.dumps(f'Successfully executed {action} action')
}

# Auto Scaling Group with cost-optimized configuration
resource "aws_autoscaling_group" "web_asg" {
  name                = "web-asg"
  vpc_zone_identifier = var.private_subnet_ids
  target_group_arns   = [aws_lb_target_group.web.arn]
  health_check_type   = "ELB"
  health_check_grace_period = 300
  # コスト最適化のための設定
  min_size         = var.environment == "production" ? 2 : 1
  max_size         = var.environment == "production" ? 10 : 3
  desired_capacity = var.environment == "production" ? 2 : 1
  # Mixed instances policy for cost optimization
  mixed_instances_policy {
    launch_template {
      launch_template_specification {
        launch_template_id = aws_launch_template.web.id
        version           = "$Latest"
      }
      # Override with cost-effective instance types
      override {
        instance_type     = "t3.micro"
        weighted_capacity = "1"
      }
      override {
        instance_type     = "t3.small"
        weighted_capacity = "2"
      }
      override {
        instance_type     = "t3.medium"
        weighted_capacity = "4"
      }
    }
    instances_distribution {
      on_demand_base_capacity                  = var.environment == "production" ? 1 : 0
      on_demand_percentage_above_base_capacity = var.environment == "production" ? 25 : 0
      spot_allocation_strategy                 = "diversified"
    }
  }
  # Time-based scaling
  tag {
    key                 = "Name"
    value               = "web-server"
    propagate_at_launch = true
  }
  tag {
    key                 = "Environment"
    value               = var.environment
    propagate_at_launch = true
  }
  tag {
    key                 = "CostCenter"
    value               = var.cost_center
    propagate_at_launch = true
  }
}
# Scaling policies
resource "aws_autoscaling_policy" "scale_up" {
  name                   = "scale-up"
  scaling_adjustment     = 1
  adjustment_type        = "ChangeInCapacity"
  cooldown              = 300
  autoscaling_group_name = aws_autoscaling_group.web_asg.name
}
resource "aws_autoscaling_policy" "scale_down" {
  name                   = "scale-down"
  scaling_adjustment     = -1
  adjustment_type        = "ChangeInCapacity"
  cooldown              = 300
  autoscaling_group_name = aws_autoscaling_group.web_asg.name
}
# CloudWatch alarms for scaling
resource "aws_cloudwatch_metric_alarm" "cpu_high" {
  alarm_name          = "cpu-utilization-high"
  comparison_operator = "GreaterThanThreshold"
  evaluation_periods  = "2"
  metric_name         = "CPUUtilization"
  namespace           = "AWS/EC2"
  period              = "120"
  statistic           = "Average"
  threshold           = "70"
  alarm_description   = "This metric monitors ec2 cpu utilization"
  alarm_actions       = [aws_autoscaling_policy.scale_up.arn]
  dimensions = {
    AutoScalingGroupName = aws_autoscaling_group.web_asg.name
  }
}
resource "aws_cloudwatch_metric_alarm" "cpu_low" {
  alarm_name          = "cpu-utilization-low"
  comparison_operator = "LessThanThreshold"
  evaluation_periods  = "2"
  metric_name         = "CPUUtilization"
  namespace           = "AWS/EC2"
  period              = "120"
  statistic           = "Average"
  threshold           = "30"
  alarm_description   = "This metric monitors ec2 cpu utilization"
  alarm_actions       = [aws_autoscaling_policy.scale_down.arn]
  dimensions = {
    AutoScalingGroupName = aws_autoscaling_group.web_asg.name
  }
}

# Cost-optimized EBS volumes
resource "aws_ebs_volume" "app_data" {
  availability_zone = var.availability_zone
  size             = var.volume_size
  type             = var.environment == "production" ? "gp3" : "gp2"
  # gp3 specific settings for cost optimization
  throughput = var.environment == "production" ? 125 : null
  iops      = var.environment == "production" ? 3000 : null
  encrypted = true
  tags = {
    Name        = "app-data-volume"
    Environment = var.environment
    CostCenter  = var.cost_center
    Backup      = "required"
  }
}
# Automated snapshot management
resource "aws_dlm_lifecycle_policy" "ebs_snapshot_policy" {
  description        = "EBS snapshot lifecycle policy"
  execution_role_arn = aws_iam_role.dlm_lifecycle_role.arn
  state             = "ENABLED"
  policy_details {
    resource_types   = ["VOLUME"]
    target_tags = {
      Backup = "required"
    }
    schedule {
      name = "daily-snapshots"
      create_rule {
        interval      = 24
        interval_unit = "HOURS"
        times         = ["03:00"]
      }
      retain_rule {
        count = var.environment == "production" ? 30 : 7
      }
      tags_to_add = {
        SnapshotCreator = "DLM"
        Environment     = var.environment
      }
      copy_tags = true
    }
  }
}
# Lambda function for unused EBS volume cleanup
resource "aws_lambda_function" "ebs_cleanup" {
  filename         = "ebs_cleanup.zip"
  function_name    = "ebs-cleanup"
  role            = aws_iam_role.lambda_role.arn
  handler         = "index.handler"
  runtime         = "python3.9"
  timeout         = 300
  environment {
    variables = {
      DRY_RUN = var.environment == "production" ? "true" : "false"
    }
  }
}
# Scheduled cleanup execution
resource "aws_cloudwatch_event_rule" "ebs_cleanup_schedule" {
  name                = "ebs-cleanup-schedule"
  description         = "Weekly EBS cleanup"
  schedule_expression = "cron(0 2 ? * SUN *)"
}
resource "aws_cloudwatch_event_target" "ebs_cleanup_target" {
  rule      = aws_cloudwatch_event_rule.ebs_cleanup_schedule.name
  target_id = "EBSCleanupTarget"
  arn       = aws_lambda_function.ebs_cleanup.arn
}

# Cost-optimized RDS configuration
resource "aws_db_instance" "main" {
  identifier = "${var.project_name}-${var.environment}-db"
  # Instance configuration based on environment
  engine         = "mysql"
  engine_version = "8.0"
  instance_class = var.environment == "production" ? "db.t3.medium" : "db.t3.micro"
  allocated_storage     = var.environment == "production" ? 100 : 20
  max_allocated_storage = var.environment == "production" ? 1000 : 100
  storage_type         = "gp2"
  storage_encrypted    = true
  # Cost optimization settings
  multi_az               = var.environment == "production" ? true : false
  backup_retention_period = var.environment == "production" ? 7 : 1
  backup_window          = "03:00-04:00"
  maintenance_window     = "sun:04:00-sun:05:00"
  # Performance Insights (production only)
  performance_insights_enabled = var.environment == "production" ? true : false
  # Automated minor version upgrade
  auto_minor_version_upgrade = true
  # Deletion protection
  deletion_protection = var.environment == "production" ? true : false
  # Skip final snapshot for non-production
  skip_final_snapshot = var.environment != "production"
  final_snapshot_identifier = var.environment == "production" ? "${var.project_name}-final-snapshot-${formatdate("YYYY-MM-DD-hhmm", timestamp())}" : null
  tags = {
    Name        = "${var.project_name}-${var.environment}-db"
    Environment = var.environment
    CostCenter  = var.cost_center
  }
}
# RDS Proxy for connection pooling (production only)
resource "aws_db_proxy" "main" {
  count = var.environment == "production" ? 1 : 0
  name                   = "${var.project_name}-db-proxy"
  engine_family         = "MYSQL"
  auth {
    auth_scheme = "SECRETS"
    secret_arn  = aws_secretsmanager_secret.db_credentials.arn
  }
  role_arn               = aws_iam_role.db_proxy_role[0].arn
  vpc_subnet_ids         = var.private_subnet_ids
  target {
    db_instance_identifier = aws_db_instance.main.id
  }
  tags = {
    Name        = "${var.project_name}-db-proxy"
    Environment = var.environment
  }
}

# AWS Budgets for cost monitoring
resource "aws_budgets_budget" "monthly_cost" {
  name         = "${var.project_name}-monthly-budget"
  budget_type  = "COST"
  limit_amount = var.monthly_budget_limit
  limit_unit   = "USD"
  time_unit    = "MONTHLY"
  time_period_start = "2025-01-01_00:00"
  cost_filters {
    tag {
      key    = "Project"
      values = [var.project_name]
    }
  }
  notification {
    comparison_operator        = "GREATER_THAN"
    threshold                 = 80
    threshold_type            = "PERCENTAGE"
    notification_type         = "ACTUAL"
    subscriber_email_addresses = var.budget_alert_emails
  }
  notification {
    comparison_operator        = "GREATER_THAN"
    threshold                 = 100
    threshold_type            = "PERCENTAGE"
    notification_type          = "FORECASTED"
    subscriber_email_addresses = var.budget_alert_emails
  }
}
# CloudWatch dashboard for cost monitoring
resource "aws_cloudwatch_dashboard" "cost_monitoring" {
  dashboard_name = "${var.project_name}-cost-monitoring"
  dashboard_body = jsonencode({
    widgets = [
      {
        type   = "metric"
        x      = 0
        y      = 0
        width  = 12
        height = 6
        properties = {
          metrics = [
            ["AWS/Billing", "EstimatedCharges", "Currency", "USD"]
          ]
          view    = "timeSeries"
          stacked = false
          region  = "us-east-1"
          title   = "Estimated Monthly Charges"
          period  = 86400
        }
      }
    ]
  })
}

月額コスト: 50万円
主な問題:
- 開発環境24時間稼働: 15万円/月
- 過剰なインスタンスサイズ: 8万円/月
- 不要なスナップショット: 3万円/月
- 非効率なRDS設定: 4万円/月

1. 自動スケジューリング導入
   - 開発環境の夜間・週末停止
   - 削減効果: 12万円/月
2. インスタンスサイズ最適化
   - 使用率分析によるRight Sizing
   - 削減効果: 6万円/月
3. ストレージ最適化
   - 不要スナップショット削除
   - EBSタイプ最適化
   - 削減効果: 2万円/月
4. RDS最適化
   - インスタンスクラス調整
   - バックアップ期間短縮
   - 削減効果: 2万円/月

月額コスト: 35万円（30%削減）
年間削減額: 180万円
投資対効果: 500%（導入コスト36万円）

月額コスト: 120万円
主な問題:
- リザーブドインスタンス未活用
- スポットインスタンス未活用
- 過剰なマルチAZ構成
- 非効率なロードバランサー構成

1. リザーブドインスタンス導入
   - 1年契約での大幅割引
   - 削減効果: 25万円/月
2. スポットインスタンス活用
   - バッチ処理での活用
   - 削減効果: 8万円/月
3. アーキテクチャ最適化
   - 不要なマルチAZ削減
   - ロードバランサー統合
   - 削減効果: 12万円/月

月額コスト: 75万円（38%削減）
年間削減額: 540万円
投資対効果: 600%（導入コスト90万円）

コスト最適化エンジニアの年収相場：
- 初級（経験1-2年）: 700-900万円
- 中級（経験3-5年）: 900-1,300万円
- 上級（経験5年以上）: 1,300-2,000万円
フリーランス・コンサルティング単価：
- 初級: 月額80-100万円
- 中級: 月額100-150万円
- 上級: 月額150-250万円

最高単価パターン：
IaC + コスト最適化 + FinOps + 経営視点
→ 年収1,800-2,500万円
高単価パターン：
Terraform + AWS + コスト分析 + 自動化
→ 年収1,200-1,800万円
安定単価パターン：
IaC + クラウド + 運用最適化
→ 年収900-1,300万円

Phase 1: 基礎知識習得（1-2ヶ月）
- クラウドコストの仕組み理解
- 基本的な最適化手法
- IaCツールの習得
Phase 2: 実践経験積み上げ（3-6ヶ月）
- 実際のプロジェクトでの最適化
- 効果測定・分析
- 自動化システム構築
Phase 3: 高度な戦略立案（6ヶ月以上）
- FinOpsの実践
- 経営層への提案
- 組織全体の最適化戦略