Vercel Commerce AI 플랫폼 가이드 - Private Cloud 팀을 위한 완전 활용법

⏱️ 예상 읽기 시간: 25분

서론

Vercel Commerce는 Next.js App Router 기반의 고성능 서버 렌더링 이커머스 애플리케이션입니다. React Server Components, Server Actions, Suspense 등 최신 기술을 활용하여 뛰어난 성능과 개발자 경험을 제공합니다.

Private Cloud AI 플랫폼 팀에게는 단순한 이커머스 플랫폼을 넘어 AI 서비스 마켓플레이스, 내부 도구 플랫폼, 개인화된 고객 서비스를 구축할 수 있는 강력한 기반이 됩니다.

주요 특징

MIT 라이선스: 상업적 활용 완전 자유
13.2k stars: 검증된 오픈소스 프로젝트
다양한 Provider 지원: Shopify, BigCommerce, Medusa, Saleor 등
최신 Next.js 기술: App Router, Server Components, Server Actions
고성능 최적화: SEO, 이미지 최적화, 캐싱 전략

License 분석 및 상업적 활용

# MIT License 확인
curl -s https://raw.githubusercontent.com/vercel/commerce/main/license.md

MIT 라이선스 핵심 사항:

✅ 상업적 사용 완전 자유
✅ 수정/배포 자유
✅ Private 사용 자유
✅ Patent 사용 권한
⚠️ 라이선스 고지 의무

Private Cloud 회사의 AI 플랫폼 팀이 제약 없이 활용 가능합니다.

Private Cloud AI 플랫폼 팀 활용 시나리오

1. AI 서비스 마켓플레이스 구축

목적: 내부 AI 모델과 서비스를 카탈로그화하여 조직 내 공유

// lib/ai-services/types.ts
export interface AIService {
  id: string;
  name: string;
  description: string;
  category: 'llm' | 'vision' | 'speech' | 'nlp' | 'prediction';
  provider: 'openai' | 'anthropic' | 'internal' | 'huggingface';
  pricing: {
    model: 'token' | 'request' | 'subscription';
    price: number;
    unit: string;
  };
  capabilities: string[];
  documentation: string;
  endpoint: string;
  authentication: 'api-key' | 'oauth' | 'internal';
  status: 'active' | 'beta' | 'deprecated';
}

// components/ai-services/service-card.tsx
import { AIService } from '@/lib/ai-services/types';

interface ServiceCardProps {
  service: AIService;
  onSubscribe: (serviceId: string) => void;
}

export function ServiceCard({ service, onSubscribe }: ServiceCardProps) {
  const getCategoryColor = (category: AIService['category']) => {
    const colors = {
      llm: 'bg-blue-100 text-blue-800',
      vision: 'bg-green-100 text-green-800',
      speech: 'bg-purple-100 text-purple-800',
      nlp: 'bg-yellow-100 text-yellow-800',
      prediction: 'bg-red-100 text-red-800'
    };
    return colors[category];
  };

  return (
    <div className="border rounded-lg p-6 hover:shadow-lg transition-shadow">
      <div className="flex justify-between items-start mb-4">
        <h3 className="text-xl font-semibold">{service.name}</h3>
        <span className={`px-3 py-1 rounded-full text-sm ${getCategoryColor(service.category)}`}>
          {service.category.toUpperCase()}
        </span>
      </div>
      
      <p className="text-gray-600 mb-4">{service.description}</p>
      
      <div className="space-y-2 mb-4">
        <div className="flex justify-between">
          <span className="text-sm text-gray-500">Provider:</span>
          <span className="text-sm font-medium">{service.provider}</span>
        </div>
        <div className="flex justify-between">
          <span className="text-sm text-gray-500">Pricing:</span>
          <span className="text-sm font-medium">
            ${service.pricing.price} per {service.pricing.unit}
          </span>
        </div>
        <div className="flex justify-between">
          <span className="text-sm text-gray-500">Status:</span>
          <span className={`text-sm font-medium ${
            service.status === 'active' ? 'text-green-600' : 
            service.status === 'beta' ? 'text-yellow-600' : 'text-red-600'
          }`}>
            {service.status.toUpperCase()}
          </span>
        </div>
      </div>
      
      <div className="mb-4">
        <h4 className="text-sm font-medium mb-2">Capabilities:</h4>
        <div className="flex flex-wrap gap-2">
          {service.capabilities.map((capability, index) => (
            <span 
              key={index}
              className="px-2 py-1 bg-gray-100 text-gray-700 text-xs rounded"
            >
              {capability}
            </span>
          ))}
        </div>
      </div>
      
      <div className="flex gap-3">
        <button
          onClick={() => onSubscribe(service.id)}
          disabled={service.status === 'deprecated'}
          className="flex-1 bg-blue-600 text-white px-4 py-2 rounded hover:bg-blue-700 disabled:bg-gray-400 disabled:cursor-not-allowed"
        >
          {service.status === 'deprecated' ? 'Deprecated' : 'Subscribe'}
        </button>
        <a
          href={service.documentation}
          target="_blank"
          rel="noopener noreferrer"
          className="px-4 py-2 border border-gray-300 rounded hover:bg-gray-50"
        >
          Docs
        </a>
      </div>
    </div>
  );
}

2. 내부 AI 도구 플랫폼

목적: 팀별 AI 도구와 스크립트를 공유하고 관리

// lib/internal-tools/tool-manager.ts
export interface InternalTool {
  id: string;
  name: string;
  description: string;
  category: 'data-processing' | 'model-training' | 'deployment' | 'monitoring';
  team: string;
  author: string;
  version: string;
  repository: string;
  installation: {
    type: 'npm' | 'pip' | 'docker' | 'script';
    command: string;
  };
  dependencies: string[];
  usage: {
    example: string;
    parameters: Record<string, any>;
  };
  tags: string[];
  downloads: number;
  rating: number;
  createdAt: Date;
  updatedAt: Date;
}

// components/tools/tool-marketplace.tsx
'use client';

import { useState, useEffect } from 'react';
import { InternalTool } from '@/lib/internal-tools/tool-manager';
import { ToolCard } from './tool-card';
import { ToolFilter } from './tool-filter';

export function ToolMarketplace() {
  const [tools, setTools] = useState<InternalTool[]>([]);
  const [filteredTools, setFilteredTools] = useState<InternalTool[]>([]);
  const [filters, setFilters] = useState({
    category: '',
    team: '',
    tags: [] as string[]
  });
  const [searchTerm, setSearchTerm] = useState('');

  useEffect(() => {
    fetchTools();
  }, []);

  useEffect(() => {
    applyFilters();
  }, [tools, filters, searchTerm]);

  const fetchTools = async () => {
    try {
      const response = await fetch('/api/internal-tools');
      const data = await response.json();
      setTools(data);
    } catch (error) {
      console.error('Failed to fetch tools:', error);
    }
  };

  const applyFilters = () => {
    let filtered = tools;

    // Search filter
    if (searchTerm) {
      filtered = filtered.filter(tool =>
        tool.name.toLowerCase().includes(searchTerm.toLowerCase()) ||
        tool.description.toLowerCase().includes(searchTerm.toLowerCase()) ||
        tool.tags.some(tag => tag.toLowerCase().includes(searchTerm.toLowerCase()))
      );
    }

    // Category filter
    if (filters.category) {
      filtered = filtered.filter(tool => tool.category === filters.category);
    }

    // Team filter
    if (filters.team) {
      filtered = filtered.filter(tool => tool.team === filters.team);
    }

    // Tags filter
    if (filters.tags.length > 0) {
      filtered = filtered.filter(tool =>
        filters.tags.every(tag => tool.tags.includes(tag))
      );
    }

    setFilteredTools(filtered);
  };

  const handleInstall = async (tool: InternalTool) => {
    // Track usage analytics
    await fetch('/api/analytics/tool-install', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
        toolId: tool.id,
        userId: 'current-user-id',
        timestamp: new Date().toISOString()
      })
    });

    // Show installation instructions
    alert(`Install command: ${tool.installation.command}`);
  };

  return (
    <div className="container mx-auto px-4 py-8">
      <div className="mb-8">
        <h1 className="text-3xl font-bold mb-4">Internal AI Tools</h1>
        <div className="flex gap-4 mb-6">
          <input
            type="text"
            placeholder="Search tools..."
            value={searchTerm}
            onChange={(e) => setSearchTerm(e.target.value)}
            className="flex-1 px-4 py-2 border rounded-lg"
          />
          <button
            onClick={() => window.location.href = '/tools/submit'}
            className="px-6 py-2 bg-blue-600 text-white rounded-lg hover:bg-blue-700"
          >
            Submit Tool
          </button>
        </div>
        <ToolFilter filters={filters} onFiltersChange={setFilters} />
      </div>

      <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-6">
        {filteredTools.map((tool) => (
          <ToolCard
            key={tool.id}
            tool={tool}
            onInstall={handleInstall}
          />
        ))}
      </div>

      {filteredTools.length === 0 && (
        <div className="text-center py-12">
          <p className="text-gray-500">No tools found matching your criteria.</p>
        </div>
      )}
    </div>
  );
}

3. AI 기반 개인화 쇼핑 경험

목적: AI 추천 시스템을 통한 개인화된 상품 추천

// lib/ai/recommendation-engine.ts
export interface UserProfile {
  userId: string;
  preferences: {
    categories: string[];
    priceRange: { min: number; max: number };
    brands: string[];
  };
  behavior: {
    viewHistory: string[];
    purchaseHistory: string[];
    searchQueries: string[];
  };
  demographics: {
    age?: number;
    location?: string;
    profession?: string;
  };
}

export interface RecommendationRequest {
  userId: string;
  context: 'homepage' | 'product-page' | 'cart' | 'checkout';
  currentProductId?: string;
  limit: number;
}

export class AIRecommendationEngine {
  private apiKey: string;
  private baseUrl: string;

  constructor(apiKey: string, baseUrl: string) {
    this.apiKey = apiKey;
    this.baseUrl = baseUrl;
  }

  async generateRecommendations(request: RecommendationRequest): Promise<string[]> {
    const userProfile = await this.getUserProfile(request.userId);
    
    const prompt = this.buildRecommendationPrompt(userProfile, request);
    
    const response = await fetch(`${this.baseUrl}/v1/chat/completions`, {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${this.apiKey}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        model: 'gpt-4',
        messages: [
          {
            role: 'system',
            content: 'You are an AI recommendation engine for an e-commerce platform. Generate product recommendations based on user profiles and context.'
          },
          {
            role: 'user',
            content: prompt
          }
        ],
        functions: [
          {
            name: 'recommend_products',
            description: 'Recommend products based on user profile and context',
            parameters: {
              type: 'object',
              properties: {
                productIds: {
                  type: 'array',
                  items: { type: 'string' },
                  description: 'Array of recommended product IDs'
                },
                reasoning: {
                  type: 'string',
                  description: 'Explanation for the recommendations'
                }
              },
              required: ['productIds', 'reasoning']
            }
          }
        ],
        function_call: { name: 'recommend_products' }
      })
    });

    const data = await response.json();
    const functionCall = data.choices[0].message.function_call;
    const result = JSON.parse(functionCall.arguments);
    
    return result.productIds;
  }

  private buildRecommendationPrompt(profile: UserProfile, request: RecommendationRequest): string {
    return `
User Profile:
- Preferred Categories: ${profile.preferences.categories.join(', ')}
- Price Range: $${profile.preferences.priceRange.min} - $${profile.preferences.priceRange.max}
- Favorite Brands: ${profile.preferences.brands.join(', ')}
- Recent Views: ${profile.behavior.viewHistory.slice(-10).join(', ')}
- Purchase History: ${profile.behavior.purchaseHistory.slice(-5).join(', ')}

Context: ${request.context}
${request.currentProductId ? `Current Product: ${request.currentProductId}` : ''}

Please recommend ${request.limit} products that would be most relevant to this user.
    `;
  }

  private async getUserProfile(userId: string): Promise<UserProfile> {
    // Fetch user profile from database
    const response = await fetch(`/api/users/${userId}/profile`);
    return response.json();
  }
}

// components/recommendations/ai-product-recommendations.tsx
'use client';

import { useState, useEffect } from 'react';
import { AIRecommendationEngine } from '@/lib/ai/recommendation-engine';
import { ProductCard } from '../product/product-card';

interface AIProductRecommendationsProps {
  userId: string;
  context: 'homepage' | 'product-page' | 'cart' | 'checkout';
  currentProductId?: string;
  title?: string;
}

export function AIProductRecommendations({
  userId,
  context,
  currentProductId,
  title = 'Recommended for You'
}: AIProductRecommendationsProps) {
  const [recommendations, setRecommendations] = useState([]);
  const [loading, setLoading] = useState(true);
  const [error, setError] = useState<string | null>(null);

  useEffect(() => {
    loadRecommendations();
  }, [userId, context, currentProductId]);

  const loadRecommendations = async () => {
    try {
      setLoading(true);
      setError(null);

      const response = await fetch('/api/recommendations', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
          userId,
          context,
          currentProductId,
          limit: 8
        })
      });

      if (!response.ok) {
        throw new Error('Failed to load recommendations');
      }

      const data = await response.json();
      setRecommendations(data.products);
    } catch (err) {
      setError(err instanceof Error ? err.message : 'An error occurred');
    } finally {
      setLoading(false);
    }
  };

  if (loading) {
    return (
      <div className="py-8">
        <h2 className="text-2xl font-bold mb-6">{title}</h2>
        <div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-4 gap-6">
          {[...Array(8)].map((_, index) => (
            <div key={index} className="animate-pulse">
              <div className="bg-gray-200 aspect-square rounded-lg mb-4"></div>
              <div className="h-4 bg-gray-200 rounded mb-2"></div>
              <div className="h-4 bg-gray-200 rounded w-2/3"></div>
            </div>
          ))}
        </div>
      </div>
    );
  }

  if (error) {
    return (
      <div className="py-8">
        <h2 className="text-2xl font-bold mb-6">{title}</h2>
        <div className="bg-red-50 border border-red-200 rounded-lg p-4">
          <p className="text-red-600">Failed to load recommendations: {error}</p>
          <button
            onClick={loadRecommendations}
            className="mt-2 px-4 py-2 bg-red-600 text-white rounded hover:bg-red-700"
          >
            Retry
          </button>
        </div>
      </div>
    );
  }

  if (recommendations.length === 0) {
    return null;
  }

  return (
    <div className="py-8">
      <div className="flex items-center justify-between mb-6">
        <h2 className="text-2xl font-bold">{title}</h2>
        <span className="text-sm text-gray-500 bg-gray-100 px-3 py-1 rounded-full">
          AI Powered
        </span>
      </div>
      <div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-4 gap-6">
        {recommendations.map((product: any) => (
          <ProductCard key={product.id} product={product} />
        ))}
      </div>
    </div>
  );
}

macOS 개발 환경 설정

필수 사전 요구사항

# Node.js 설치 확인
node --version  # v18.17.0 이상 필요
npm --version   # v9.0.0 이상 필요

# pnpm 설치 (권장 패키지 매니저)
npm install -g pnpm

# Vercel CLI 설치
npm install -g vercel

# Git 설정 확인
git --version

프로젝트 설정 및 실행

#!/bin/bash
# scripts/setup-vercel-commerce.sh

set -e

PROJECT_NAME="ai-platform-commerce"
SHOPIFY_STORE_DOMAIN="your-store.myshopify.com"
SHOPIFY_STOREFRONT_ACCESS_TOKEN="your-token"

echo "🚀 Vercel Commerce AI Platform Setup"
echo "=================================="

# 1. 프로젝트 클론
echo "📦 Cloning Vercel Commerce..."
git clone https://github.com/vercel/commerce.git $PROJECT_NAME
cd $PROJECT_NAME

# 2. 종속성 설치
echo "📋 Installing dependencies..."
pnpm install

# 3. 환경 변수 설정
echo "🔧 Setting up environment variables..."
cp .env.example .env.local

# 환경 변수 업데이트
cat > .env.local << EOF
# Shopify Integration
SHOPIFY_STORE_DOMAIN=$SHOPIFY_STORE_DOMAIN
SHOPIFY_STOREFRONT_ACCESS_TOKEN=$SHOPIFY_STOREFRONT_ACCESS_TOKEN

# AI Services Configuration
OPENAI_API_KEY=your-openai-api-key
ANTHROPIC_API_KEY=your-anthropic-api-key

# Internal Analytics
ANALYTICS_DATABASE_URL=postgresql://user:password@localhost:5432/commerce_analytics

# Authentication
NEXTAUTH_SECRET=your-nextauth-secret
NEXTAUTH_URL=http://localhost:3000

# Redis for Caching
REDIS_URL=redis://localhost:6379
EOF

# 4. 개발 서버 시작
echo "🏃 Starting development server..."
pnpm dev &
DEV_PID=$!

# 5. 브라우저에서 열기
sleep 5
echo "🌐 Opening browser..."
open http://localhost:3000

echo "✅ Setup complete! Press Ctrl+C to stop the server."
wait $DEV_PID

실행 결과

# 스크립트 실행
chmod +x scripts/setup-vercel-commerce.sh
./scripts/setup-vercel-commerce.sh

실행 결과:

🚀 Vercel Commerce AI Platform Setup
==================================
📦 Cloning Vercel Commerce...
Cloning into 'ai-platform-commerce'...
remote: Enumerating objects: 2847, done.
remote: Counting objects: 100% (487/487), done.
remote: Compressing objects: 100% (298/298), done.
remote: Total 2847 (delta 253), reused 356 (delta 189), pack-reused 2360
Receiving objects: 100% (2847/2847), 4.23 MiB | 2.85 MiB/s, done.
Resolving deltas: 100% (1456/1456), done.

📋 Installing dependencies...
Progress: resolved 847, reused 0, downloaded 0, added 847, done

🔧 Setting up environment variables...
🏃 Starting development server...
   ▲ Next.js 14.0.3
   - Local:        http://localhost:3000
   - Environment:  development

 ✓ Ready in 3.2s
🌐 Opening browser...
✅ Setup complete! Press Ctrl+C to stop the server.

AI 플랫폼 커스터마이징

1. AI 서비스 API 통합

// lib/ai-platform/service-integration.ts
export class AIPlatformIntegration {
  private services: Map<string, any> = new Map();

  async initializeServices() {
    // OpenAI 통합
    this.services.set('openai', {
      client: new OpenAI({ apiKey: process.env.OPENAI_API_KEY }),
      models: ['gpt-4', 'gpt-3.5-turbo', 'dall-e-3'],
      pricing: { 'gpt-4': 0.03, 'gpt-3.5-turbo': 0.002 }
    });

    // Anthropic 통합
    this.services.set('anthropic', {
      client: new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY }),
      models: ['claude-3-opus', 'claude-3-sonnet'],
      pricing: { 'claude-3-opus': 0.015, 'claude-3-sonnet': 0.003 }
    });

    // 내부 AI 서비스 통합
    this.services.set('internal', {
      endpoint: process.env.INTERNAL_AI_ENDPOINT,
      models: await this.fetchInternalModels(),
      pricing: { 'custom-llm': 0.001 }
    });
  }

  async processAIRequest(serviceId: string, modelId: string, prompt: string) {
    const service = this.services.get(serviceId);
    if (!service) throw new Error(`Service ${serviceId} not found`);

    const usage = await this.executeRequest(service, modelId, prompt);
    await this.trackUsage(serviceId, modelId, usage);
    
    return usage;
  }

  private async fetchInternalModels() {
    // 내부 모델 목록 가져오기
    return ['custom-llm-v1', 'domain-specific-model'];
  }

  private async executeRequest(service: any, modelId: string, prompt: string) {
    // 실제 AI 서비스 호출 로직
    const startTime = Date.now();
    
    // 서비스별 API 호출
    const response = await service.client.chat.completions.create({
      model: modelId,
      messages: [{ role: 'user', content: prompt }]
    });

    return {
      response: response.choices[0].message.content,
      tokens: response.usage.total_tokens,
      duration: Date.now() - startTime,
      cost: this.calculateCost(service, modelId, response.usage.total_tokens)
    };
  }

  private calculateCost(service: any, modelId: string, tokens: number): number {
    const pricePerToken = service.pricing[modelId] || 0;
    return (tokens / 1000) * pricePerToken;
  }

  private async trackUsage(serviceId: string, modelId: string, usage: any) {
    // 사용량 데이터베이스에 저장
    await fetch('/api/analytics/ai-usage', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
        serviceId,
        modelId,
        tokens: usage.tokens,
        cost: usage.cost,
        duration: usage.duration,
        timestamp: new Date().toISOString()
      })
    });
  }
}

2. 실시간 분석 대시보드

// components/analytics/ai-usage-dashboard.tsx
'use client';

import { useState, useEffect } from 'react';
import { Line, Bar, Doughnut } from 'react-chartjs-2';
import {
  Chart as ChartJS,
  CategoryScale,
  LinearScale,
  PointElement,
  LineElement,
  BarElement,
  ArcElement,
  Title,
  Tooltip,
  Legend
} from 'chart.js';

ChartJS.register(
  CategoryScale,
  LinearScale,
  PointElement,
  LineElement,
  BarElement,
  ArcElement,
  Title,
  Tooltip,
  Legend
);

interface UsageMetrics {
  totalCosts: number;
  totalTokens: number;
  totalRequests: number;
  avgResponseTime: number;
  topModels: Array<{ model: string; usage: number }>;
  costTrend: Array<{ date: string; cost: number }>;
  serviceDistribution: Array<{ service: string; percentage: number }>;
}

export function AIUsageDashboard() {
  const [metrics, setMetrics] = useState<UsageMetrics | null>(null);
  const [loading, setLoading] = useState(true);
  const [timeRange, setTimeRange] = useState('7d');

  useEffect(() => {
    fetchMetrics();
  }, [timeRange]);

  const fetchMetrics = async () => {
    try {
      setLoading(true);
      const response = await fetch(`/api/analytics/metrics?range=${timeRange}`);
      const data = await response.json();
      setMetrics(data);
    } catch (error) {
      console.error('Failed to fetch metrics:', error);
    } finally {
      setLoading(false);
    }
  };

  if (loading || !metrics) {
    return (
      <div className="p-6">
        <div className="animate-pulse space-y-4">
          <div className="h-8 bg-gray-200 rounded w-1/3"></div>
          <div className="grid grid-cols-1 md:grid-cols-4 gap-4">
            {[...Array(4)].map((_, i) => (
              <div key={i} className="h-24 bg-gray-200 rounded"></div>
            ))}
          </div>
          <div className="h-64 bg-gray-200 rounded"></div>
        </div>
      </div>
    );
  }

  const costTrendData = {
    labels: metrics.costTrend.map(item => item.date),
    datasets: [{
      label: 'Daily Cost ($)',
      data: metrics.costTrend.map(item => item.cost),
      borderColor: 'rgb(59, 130, 246)',
      backgroundColor: 'rgba(59, 130, 246, 0.1)',
      tension: 0.1
    }]
  };

  const serviceDistributionData = {
    labels: metrics.serviceDistribution.map(item => item.service),
    datasets: [{
      data: metrics.serviceDistribution.map(item => item.percentage),
      backgroundColor: [
        'rgba(59, 130, 246, 0.8)',
        'rgba(16, 185, 129, 0.8)',
        'rgba(245, 158, 11, 0.8)',
        'rgba(239, 68, 68, 0.8)'
      ],
      borderWidth: 0
    }]
  };

  return (
    <div className="p-6 space-y-6">
      <div className="flex justify-between items-center">
        <h1 className="text-3xl font-bold">AI Usage Analytics</h1>
        <select
          value={timeRange}
          onChange={(e) => setTimeRange(e.target.value)}
          className="px-4 py-2 border rounded-lg"
        >
          <option value="1d">Last 24 hours</option>
          <option value="7d">Last 7 days</option>
          <option value="30d">Last 30 days</option>
          <option value="90d">Last 90 days</option>
        </select>
      </div>

      {/* Key Metrics */}
      <div className="grid grid-cols-1 md:grid-cols-4 gap-4">
        <div className="bg-white p-6 rounded-lg shadow border">
          <h3 className="text-lg font-semibold text-gray-600">Total Cost</h3>
          <p className="text-3xl font-bold text-blue-600">
            ${metrics.totalCosts.toFixed(2)}
          </p>
        </div>
        <div className="bg-white p-6 rounded-lg shadow border">
          <h3 className="text-lg font-semibold text-gray-600">Total Tokens</h3>
          <p className="text-3xl font-bold text-green-600">
            {(metrics.totalTokens / 1000000).toFixed(1)}M
          </p>
        </div>
        <div className="bg-white p-6 rounded-lg shadow border">
          <h3 className="text-lg font-semibold text-gray-600">Total Requests</h3>
          <p className="text-3xl font-bold text-yellow-600">
            {metrics.totalRequests.toLocaleString()}
          </p>
        </div>
        <div className="bg-white p-6 rounded-lg shadow border">
          <h3 className="text-lg font-semibold text-gray-600">Avg Response Time</h3>
          <p className="text-3xl font-bold text-red-600">
            {metrics.avgResponseTime.toFixed(0)}ms
          </p>
        </div>
      </div>

      {/* Charts */}
      <div className="grid grid-cols-1 lg:grid-cols-2 gap-6">
        <div className="bg-white p-6 rounded-lg shadow border">
          <h3 className="text-xl font-semibold mb-4">Cost Trend</h3>
          <Line data={costTrendData} options={{ responsive: true }} />
        </div>

        <div className="bg-white p-6 rounded-lg shadow border">
          <h3 className="text-xl font-semibold mb-4">Service Distribution</h3>
          <Doughnut 
            data={serviceDistributionData} 
            options={{
              responsive: true,
              plugins: {
                legend: {
                  position: 'bottom'
                }
              }
            }}
          />
        </div>
      </div>

      {/* Top Models */}
      <div className="bg-white p-6 rounded-lg shadow border">
        <h3 className="text-xl font-semibold mb-4">Top Models by Usage</h3>
        <div className="space-y-3">
          {metrics.topModels.map((model, index) => (
            <div key={model.model} className="flex items-center justify-between">
              <div className="flex items-center space-x-3">
                <span className="w-6 h-6 bg-blue-600 text-white rounded-full text-sm flex items-center justify-center">
                  {index + 1}
                </span>
                <span className="font-medium">{model.model}</span>
              </div>
              <div className="flex items-center space-x-4">
                <div className="w-32 bg-gray-200 rounded-full h-2">
                  <div 
                    className="bg-blue-600 h-2 rounded-full"
                    style={{ width: `${(model.usage / metrics.topModels[0].usage) * 100}%` }}
                  ></div>
                </div>
                <span className="text-sm text-gray-600 w-16 text-right">
                  {model.usage.toLocaleString()}
                </span>
              </div>
            </div>
          ))}
        </div>
      </div>
    </div>
  );
}

프로덕션 배포 전략

1. Vercel 배포 설정

// vercel.json
{
  "version": 2,
  "name": "ai-platform-commerce",
  "builds": [
    {
      "src": "package.json",
      "use": "@vercel/next"
    }
  ],
  "routes": [
    {
      "src": "/(.*)",
      "dest": "/$1"
    }
  ],
  "env": {
    "SHOPIFY_STORE_DOMAIN": "@shopify_store_domain",
    "SHOPIFY_STOREFRONT_ACCESS_TOKEN": "@shopify_storefront_access_token",
    "OPENAI_API_KEY": "@openai_api_key",
    "ANTHROPIC_API_KEY": "@anthropic_api_key",
    "NEXTAUTH_SECRET": "@nextauth_secret",
    "ANALYTICS_DATABASE_URL": "@analytics_database_url"
  },
  "functions": {
    "app/api/*/route.ts": {
      "maxDuration": 30
    }
  }
}

// next.config.ts
import type { NextConfig } from 'next';

const nextConfig: NextConfig = {
  experimental: {
    serverActions: {
      allowedOrigins: ['localhost:3000', 'ai-platform-commerce.vercel.app']
    }
  },
  images: {
    domains: [
      'cdn.shopify.com',
      'images.unsplash.com',
      'your-internal-cdn.com'
    ],
    formats: ['image/webp', 'image/avif']
  },
  async headers() {
    return [
      {
        source: '/api/:path*',
        headers: [
          { key: 'Access-Control-Allow-Origin', value: '*' },
          { key: 'Access-Control-Allow-Methods', value: 'GET, POST, PUT, DELETE, OPTIONS' },
          { key: 'Access-Control-Allow-Headers', value: 'Content-Type, Authorization' }
        ]
      }
    ];
  },
  async redirects() {
    return [
      {
        source: '/admin',
        destination: '/admin/dashboard',
        permanent: true
      }
    ];
  }
};

export default nextConfig;

2. 데이터베이스 마이그레이션

-- migrations/001_initial_ai_platform_schema.sql
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";

-- AI Services Table
CREATE TABLE ai_services (
    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
    name VARCHAR(255) NOT NULL,
    description TEXT,
    category VARCHAR(50) NOT NULL,
    provider VARCHAR(100) NOT NULL,
    endpoint TEXT,
    api_key_required BOOLEAN DEFAULT true,
    pricing_model VARCHAR(50),
    price_per_unit DECIMAL(10,4),
    status VARCHAR(20) DEFAULT 'active',
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- User AI Usage Table
CREATE TABLE ai_usage (
    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
    user_id VARCHAR(255) NOT NULL,
    service_id UUID REFERENCES ai_services(id),
    model_name VARCHAR(100),
    tokens_used INTEGER,
    cost DECIMAL(10,4),
    response_time_ms INTEGER,
    request_data JSONB,
    response_data JSONB,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Internal Tools Table
CREATE TABLE internal_tools (
    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
    name VARCHAR(255) NOT NULL,
    description TEXT,
    category VARCHAR(50),
    team VARCHAR(100),
    author VARCHAR(255),
    version VARCHAR(50),
    repository TEXT,
    installation_type VARCHAR(20),
    installation_command TEXT,
    dependencies JSONB,
    usage_example TEXT,
    tags TEXT[],
    downloads INTEGER DEFAULT 0,
    rating DECIMAL(3,2) DEFAULT 0,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Analytics Tables
CREATE TABLE user_interactions (
    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
    user_id VARCHAR(255),
    session_id VARCHAR(255),
    event_type VARCHAR(100),
    event_data JSONB,
    page_url TEXT,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Indexes for Performance
CREATE INDEX idx_ai_usage_user_id ON ai_usage(user_id);
CREATE INDEX idx_ai_usage_created_at ON ai_usage(created_at);
CREATE INDEX idx_user_interactions_user_id ON user_interactions(user_id);
CREATE INDEX idx_user_interactions_event_type ON user_interactions(event_type);
CREATE INDEX idx_internal_tools_category ON internal_tools(category);
CREATE INDEX idx_internal_tools_team ON internal_tools(team);

-- Update Triggers
CREATE OR REPLACE FUNCTION update_updated_at_column()
RETURNS TRIGGER AS $$
BEGIN
    NEW.updated_at = CURRENT_TIMESTAMP;
    RETURN NEW;
END;
$$ language 'plpgsql';

CREATE TRIGGER update_ai_services_updated_at BEFORE UPDATE ON ai_services FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();
CREATE TRIGGER update_internal_tools_updated_at BEFORE UPDATE ON internal_tools FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();

모니터링 및 성능 최적화

1. 실시간 모니터링 설정

// lib/monitoring/performance-monitor.ts
export class PerformanceMonitor {
  private metrics: Map<string, any> = new Map();
  
  async trackAPIPerformance(endpoint: string, startTime: number, endTime: number, success: boolean) {
    const duration = endTime - startTime;
    const metric = this.metrics.get(endpoint) || {
      totalRequests: 0,
      successfulRequests: 0,
      totalDuration: 0,
      averageDuration: 0,
      errorRate: 0
    };

    metric.totalRequests++;
    if (success) metric.successfulRequests++;
    metric.totalDuration += duration;
    metric.averageDuration = metric.totalDuration / metric.totalRequests;
    metric.errorRate = ((metric.totalRequests - metric.successfulRequests) / metric.totalRequests) * 100;

    this.metrics.set(endpoint, metric);

    // Send to monitoring service
    await this.sendMetrics(endpoint, metric);
  }

  async trackAIServiceUsage(serviceId: string, modelId: string, tokens: number, cost: number, duration: number) {
    const usage = {
      serviceId,
      modelId,
      tokens,
      cost,
      duration,
      timestamp: new Date().toISOString()
    };

    // Store in database
    await fetch('/api/analytics/ai-usage', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify(usage)
    });

    // Send to real-time monitoring
    await this.sendAIMetrics(usage);
  }

  private async sendMetrics(endpoint: string, metric: any) {
    // Send to monitoring service (e.g., DataDog, New Relic)
    if (process.env.MONITORING_WEBHOOK) {
      await fetch(process.env.MONITORING_WEBHOOK, {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
          type: 'api_performance',
          endpoint,
          metric,
          timestamp: new Date().toISOString()
        })
      });
    }
  }

  private async sendAIMetrics(usage: any) {
    // Send AI usage metrics
    if (process.env.AI_MONITORING_WEBHOOK) {
      await fetch(process.env.AI_MONITORING_WEBHOOK, {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
          type: 'ai_usage',
          usage,
          timestamp: new Date().toISOString()
        })
      });
    }
  }

  getMetrics(): Record<string, any> {
    return Object.fromEntries(this.metrics);
  }
}

// Global performance monitor instance
export const performanceMonitor = new PerformanceMonitor();

2. 캐싱 전략

// lib/cache/redis-cache.ts
import Redis from 'ioredis';

export class RedisCache {
  private client: Redis;

  constructor() {
    this.client = new Redis(process.env.REDIS_URL || 'redis://localhost:6379');
  }

  async get(key: string): Promise<any> {
    try {
      const value = await this.client.get(key);
      return value ? JSON.parse(value) : null;
    } catch (error) {
      console.error('Redis GET error:', error);
      return null;
    }
  }

  async set(key: string, value: any, ttl: number = 3600): Promise<void> {
    try {
      await this.client.setex(key, ttl, JSON.stringify(value));
    } catch (error) {
      console.error('Redis SET error:', error);
    }
  }

  async del(key: string): Promise<void> {
    try {
      await this.client.del(key);
    } catch (error) {
      console.error('Redis DEL error:', error);
    }
  }

  async cacheAIResponse(userId: string, prompt: string, response: any, ttl: number = 1800): Promise<void> {
    const key = `ai_response:${userId}:${this.hashPrompt(prompt)}`;
    await this.set(key, response, ttl);
  }

  async getCachedAIResponse(userId: string, prompt: string): Promise<any> {
    const key = `ai_response:${userId}:${this.hashPrompt(prompt)}`;
    return await this.get(key);
  }

  private hashPrompt(prompt: string): string {
    // Simple hash function for demo
    return Buffer.from(prompt).toString('base64').substring(0, 32);
  }
}

// lib/cache/cache-middleware.ts
import { NextRequest, NextResponse } from 'next/server';
import { RedisCache } from './redis-cache';

const cache = new RedisCache();

export function withCache(handler: Function, ttl: number = 3600) {
  return async (request: NextRequest) => {
    const cacheKey = `api:${request.url}:${request.method}`;
    
    if (request.method === 'GET') {
      const cachedResponse = await cache.get(cacheKey);
      if (cachedResponse) {
        return NextResponse.json(cachedResponse);
      }
    }

    const response = await handler(request);
    
    if (request.method === 'GET' && response.ok) {
      const responseData = await response.json();
      await cache.set(cacheKey, responseData, ttl);
      return NextResponse.json(responseData);
    }

    return response;
  };
}

개발자 도구 및 Aliases

zshrc 설정

# ~/.zshrc에 추가할 Vercel Commerce AI Platform 개발 aliases

# 프로젝트 디렉토리 설정
export VERCEL_COMMERCE_DIR="$HOME/projects/ai-platform-commerce"
export AI_SERVICES_CONFIG="$VERCEL_COMMERCE_DIR/config/ai-services.json"

# 기본 개발 명령어
alias vcdev="cd $VERCEL_COMMERCE_DIR && pnpm dev"
alias vcbuild="cd $VERCEL_COMMERCE_DIR && pnpm build"
alias vctest="cd $VERCEL_COMMERCE_DIR && pnpm test"
alias vclint="cd $VERCEL_COMMERCE_DIR && pnpm lint"
alias vctype="cd $VERCEL_COMMERCE_DIR && pnpm type-check"

# Vercel 배포 관련
alias vcdeploy="cd $VERCEL_COMMERCE_DIR && vercel --prod"
alias vcpreview="cd $VERCEL_COMMERCE_DIR && vercel"
alias vcstatus="cd $VERCEL_COMMERCE_DIR && vercel ls"
alias vclogs="cd $VERCEL_COMMERCE_DIR && vercel logs"

# 데이터베이스 관리
alias vcdb-migrate="cd $VERCEL_COMMERCE_DIR && npm run db:migrate"
alias vcdb-seed="cd $VERCEL_COMMERCE_DIR && npm run db:seed"
alias vcdb-reset="cd $VERCEL_COMMERCE_DIR && npm run db:reset"
alias vcdb-studio="cd $VERCEL_COMMERCE_DIR && npm run db:studio"

# AI 서비스 관리
alias ai-services-list="cat $AI_SERVICES_CONFIG | jq '.services[].name'"
alias ai-usage-today="cd $VERCEL_COMMERCE_DIR && node scripts/ai-usage-report.js --date=today"
alias ai-costs-month="cd $VERCEL_COMMERCE_DIR && node scripts/ai-costs-report.js --period=month"

# 개발 유틸리티
alias vc-logs-error="cd $VERCEL_COMMERCE_DIR && tail -f .next/trace | grep ERROR"
alias vc-logs-ai="cd $VERCEL_COMMERCE_DIR && tail -f logs/ai-services.log"
alias vc-performance="cd $VERCEL_COMMERCE_DIR && node scripts/performance-check.js"

# 코드 생성 및 템플릿
alias vc-component="cd $VERCEL_COMMERCE_DIR && node scripts/generate-component.js"
alias vc-api-route="cd $VERCEL_COMMERCE_DIR && node scripts/generate-api-route.js"
alias vc-ai-service="cd $VERCEL_COMMERCE_DIR && node scripts/generate-ai-service.js"

# 테스트 및 QA
alias vc-test-ai="cd $VERCEL_COMMERCE_DIR && npm run test:ai-services"
alias vc-test-e2e="cd $VERCEL_COMMERCE_DIR && npm run test:e2e"
alias vc-lighthouse="cd $VERCEL_COMMERCE_DIR && npm run audit:lighthouse"
alias vc-security="cd $VERCEL_COMMERCE_DIR && npm audit"

# 모니터링 및 분석
alias vc-metrics="cd $VERCEL_COMMERCE_DIR && node scripts/fetch-metrics.js"
alias vc-alerts="cd $VERCEL_COMMERCE_DIR && node scripts/check-alerts.js"
alias vc-health="curl -s http://localhost:3000/api/health | jq"

# 환경 관리
alias vc-env-check="cd $VERCEL_COMMERCE_DIR && node scripts/check-env.js"
alias vc-env-sync="cd $VERCEL_COMMERCE_DIR && vercel env pull"
alias vc-secrets-rotate="cd $VERCEL_COMMERCE_DIR && node scripts/rotate-secrets.js"

# 유틸리티 함수
vc-new-feature() {
    if [ -z "$1" ]; then
        echo "Usage: vc-new-feature <feature-name>"
        return 1
    fi
    
    cd $VERCEL_COMMERCE_DIR
    git checkout -b "feature/$1"
    mkdir -p "components/$1"
    mkdir -p "app/api/$1"
    echo "Created feature branch and directories for: $1"
}

vc-ai-test() {
    if [ -z "$1" ]; then
        echo "Usage: vc-ai-test <service-name>"
        return 1
    fi
    
    cd $VERCEL_COMMERCE_DIR
    echo "Testing AI service: $1"
    curl -X POST http://localhost:3000/api/ai/test \
        -H "Content-Type: application/json" \
        -d "{\"service\":\"$1\",\"prompt\":\"Hello, world!\"}" | jq
}

vc-deploy-preview() {
    cd $VERCEL_COMMERCE_DIR
    echo "Creating preview deployment..."
    URL=$(vercel --yes | grep -o 'https://.*\.vercel\.app')
    echo "Preview URL: $URL"
    echo $URL | pbcopy
    echo "URL copied to clipboard!"
}

vc-performance-check() {
    cd $VERCEL_COMMERCE_DIR
    echo "Running performance checks..."
    echo "1. Bundle analysis:"
    npm run analyze
    echo "2. Lighthouse audit:"
    npm run lighthouse
    echo "3. Core Web Vitals:"
    curl -s "http://localhost:3000/api/metrics/web-vitals" | jq
}

# 빠른 설정 함수
vc-setup() {
    echo "🚀 Setting up Vercel Commerce AI Platform development environment..."
    
    # Node.js 버전 확인
    node_version=$(node -v | cut -d'v' -f2)
    required_version="18.17.0"
    if [ "$(printf '%s\n' "$required_version" "$node_version" | sort -V | head -n1)" != "$required_version" ]; then
        echo "❌ Node.js $required_version or higher required. Current: $node_version"
        return 1
    fi
    
    # 필요한 글로벌 패키지 설치
    echo "📦 Installing global dependencies..."
    npm install -g pnpm vercel @next/bundle-analyzer
    
    # 프로젝트 클론 (없는 경우)
    if [ ! -d "$VERCEL_COMMERCE_DIR" ]; then
        echo "📥 Cloning Vercel Commerce..."
        git clone https://github.com/vercel/commerce.git $VERCEL_COMMERCE_DIR
    fi
    
    cd $VERCEL_COMMERCE_DIR
    
    # 의존성 설치
    echo "📋 Installing project dependencies..."
    pnpm install
    
    # 환경 변수 파일 생성
    if [ ! -f ".env.local" ]; then
        echo "🔧 Creating environment configuration..."
        cp .env.example .env.local
        echo "Please configure .env.local with your API keys and database URLs"
    fi
    
    echo "✅ Setup complete! Use 'vcdev' to start development server."
}

# 도움말 함수
vc-help() {
    echo "Vercel Commerce AI Platform Developer Commands:"
    echo ""
    echo "🚀 Development:"
    echo "  vcdev          - Start development server"
    echo "  vcbuild        - Build for production"
    echo "  vctest         - Run test suite"
    echo "  vclint         - Run linter"
    echo ""
    echo "🌐 Deployment:"
    echo "  vcdeploy       - Deploy to production"
    echo "  vcpreview      - Create preview deployment"
    echo "  vcstatus       - Check deployment status"
    echo ""
    echo "🤖 AI Services:"
    echo "  ai-services-list    - List configured AI services"
    echo "  ai-usage-today      - Show today's AI usage"
    echo "  vc-ai-test <name>   - Test AI service"
    echo ""
    echo "🛠️ Utilities:"
    echo "  vc-new-feature <name>  - Create new feature branch"
    echo "  vc-performance-check   - Run performance audits"
    echo "  vc-setup              - Initial environment setup"
    echo ""
    echo "📊 Monitoring:"
    echo "  vc-metrics     - Fetch application metrics"
    echo "  vc-health      - Check application health"
    echo "  vc-alerts      - Check system alerts"
}

실전 테스트 시나리오

1. AI 추천 시스템 테스트

// scripts/test-ai-recommendations.ts
import { AIRecommendationEngine } from '../lib/ai/recommendation-engine';

async function testRecommendationSystem() {
  console.log('🧪 Testing AI Recommendation System...');
  
  const engine = new AIRecommendationEngine(
    process.env.OPENAI_API_KEY!,
    'https://api.openai.com'
  );

  // 테스트 사용자 프로필
  const testUser = {
    userId: 'test-user-001',
    preferences: {
      categories: ['electronics', 'ai-tools'],
      priceRange: { min: 50, max: 500 },
      brands: ['apple', 'google', 'openai']
    },
    behavior: {
      viewHistory: ['product-1', 'product-2', 'product-3'],
      purchaseHistory: ['product-4', 'product-5'],
      searchQueries: ['ai assistant', 'smart home', 'automation']
    },
    demographics: {
      age: 32,
      location: 'Seoul',
      profession: 'Software Engineer'
    }
  };

  try {
    // 홈페이지 추천 테스트
    console.log('Testing homepage recommendations...');
    const homeRecommendations = await engine.generateRecommendations({
      userId: testUser.userId,
      context: 'homepage',
      limit: 6
    });
    console.log('✅ Homepage recommendations:', homeRecommendations);

    // 상품페이지 추천 테스트
    console.log('Testing product page recommendations...');
    const productRecommendations = await engine.generateRecommendations({
      userId: testUser.userId,
      context: 'product-page',
      currentProductId: 'ai-assistant-premium',
      limit: 4
    });
    console.log('✅ Product page recommendations:', productRecommendations);

    // 성능 측정
    const startTime = Date.now();
    await engine.generateRecommendations({
      userId: testUser.userId,
      context: 'cart',
      limit: 3
    });
    const duration = Date.now() - startTime;
    console.log(`⚡ Recommendation generation time: ${duration}ms`);

    console.log('🎉 All recommendation tests passed!');
  } catch (error) {
    console.error('❌ Recommendation test failed:', error);
    process.exit(1);
  }
}

if (require.main === module) {
  testRecommendationSystem();
}

2. 성능 벤치마크 테스트

#!/bin/bash
# scripts/performance-benchmark.sh

echo "🏃‍♂️ Running Vercel Commerce AI Platform Performance Benchmarks"
echo "============================================================="

# 개발 서버 시작
echo "Starting development server..."
pnpm dev &
DEV_PID=$!

# 서버 준비 대기
sleep 10

# Lighthouse 성능 테스트
echo "📊 Running Lighthouse performance audit..."
npx lighthouse http://localhost:3000 \
  --only-categories=performance,accessibility,best-practices,seo \
  --chrome-flags="--headless" \
  --output=json \
  --output-path=./reports/lighthouse-report.json

# Core Web Vitals 테스트
echo "⚡ Testing Core Web Vitals..."
curl -s http://localhost:3000/api/metrics/web-vitals | jq

# API 성능 테스트
echo "🔗 Testing API performance..."
for endpoint in "/api/products" "/api/ai/recommendations" "/api/analytics/metrics"; do
  echo "Testing $endpoint..."
  curl -w "@curl-format.txt" -o /dev/null -s "http://localhost:3000$endpoint"
done

# AI 서비스 응답 시간 테스트
echo "🤖 Testing AI service response times..."
for service in "openai" "anthropic" "internal"; do
  echo "Testing $service..."
  time curl -X POST http://localhost:3000/api/ai/test \
    -H "Content-Type: application/json" \
    -d "{\"service\":\"$service\",\"prompt\":\"Performance test\"}" > /dev/null 2>&1
done

# 메모리 사용량 확인
echo "💾 Checking memory usage..."
ps -o pid,ppid,pcpu,pmem,args -p $DEV_PID

# 개발 서버 종료
kill $DEV_PID

echo "✅ Performance benchmark complete! Check ./reports/lighthouse-report.json for detailed results."

실행 결과 예시

# 성능 벤치마크 실행
./scripts/performance-benchmark.sh