Day 8: Building Resilient AI-Powered Systems: The Architecture Patterns Netflix & GitHub Use
Day 8 of our 60-Day Hands-On AI Engineering Series
When Netflix recommends your next binge-watch or when GitHub Copilot suggests code completions, there's a sophisticated service architecture orchestrating these AI interactions. Unlike traditional CRUD operations, AI services introduce unique challenges: unpredictable response times, token limits, rate limiting, and the need for graceful degradation when AI services are unavailable.
The Reality Behind AI Service Integration
Most engineers approach AI integration like any other API call. This creates brittle systems that fail spectacularly under real-world conditions. AI services have unique characteristics:
Variable Latency: Response times range from 200ms to 30+ seconds
Content-Based Failures: Requests fail based on input content, not just system load
Rate Limiting Complexity: Multiple limit types (requests/minute, tokens/minute, concurrent requests)
Cost Implications: Each request has direct monetary cost
Core Concept: Separation of Concerns in AI Architecture
Think of AI service integration like a restaurant kitchen during rush hour. You wouldn't have the head chef directly taking orders from customers, handling payments, and cooking simultaneously. Instead, you create specialized roles: waitstaff for customer interaction, payment processors for transactions, and chefs for food preparation.
Similarly, AI service architecture demands clear separation between:
Request Orchestration: Managing incoming requests and routing
AI Integration Layer: Handling AI service communication
Business Logic: Processing AI responses and applying domain rules
Data Persistence: Storing results and maintaining state
