System Design: LLM Gateway Pattern

Описание: System Design: LLM Gateway Pattern

A comprehensive deep dive into the LLM Gateway pattern for enterprise AI systems. Covers why you should stop calling LLM providers directly from your backend services, the four core gateway components, production code for rate limiting and circuit breakers, and real-world architecture showing how requests flow through a centralized AI middleware layer.

===========
Timestamps:
===========
00:00 - Introduction: The Case for an LLM Gateway
00:29 - Challenge 1: Cascading Failures from Service Defects
00:48 - Challenge 2: Provider Lock-In and Migration Risk
01:02 - Challenge 3: Lack of Cost Attribution
01:16 - Solution: The Gateway Pattern Architecture
02:11 - Four Core Gateway Components Overview
02:48 - Component Deep Dive: Rate Limiting
03:09 - Component Deep Dive: Observability and Logging
03:33 - System Design Interview: Quota Enforcement at Scale
04:30 - Implementation: Rate Limiting with Redis and Lua Scripts
05:00 - Implementation Pitfall: Request-Based vs Token-Based Limiting
05:26 - Implementation: Circuit Breaker Pattern
06:37 - Architecture: Enterprise LLM Gateway System Diagram
07:48 - Architecture: Request Lifecycle with Quota Enforcement
08:44 - Summary and Key Takeaways

==================================
Key Concepts and Architecture Patterns:
==================================
The Problem with Direct LLM Calls
Fragmented Quotas: Direct calls create hidden costs scattered across microservices with no unified governance
Zero Attribution: No way to know which team or feature consumed your token allocation
Debugging Nightmare: Logs scattered across dozens of services make troubleshooting impossible
Provider Lock-in: Switching from OpenAI to Anthropic requires touching every microservice

Gateway Pattern Solution
Centralized Middleware: Single layer between all backend services and LLM providers
Provider Agnostic: Application services never know which LLM they're calling
Zero-Downtime Migration: Weighted routing and canary deployments for seamless provider switches

The Four Core Gateway Components
Authentication Layer: API key validation, scope enforcement, PII redaction before requests leave your infrastructure
Rate Limiting: Fixed window, sliding window, and token bucket algorithms with atomic Redis operations
Observability Stack: Centralized logging, cost dashboards, latency metrics, prompt analytics
Resilience Patterns: Fallback chains, circuit breakers, request queuing, graceful degradation

System Design Interview Question
Quota Enforcement: "Design a system that prevents one team from consuming entire token allocation"
Fair Scheduling: Priority classes (P0 interactive, P1 background, P2 batch) with burst allowances

Production Code Patterns
Sliding Window Rate Limiting: Redis sorted sets with Lua scripts for atomic operations under high concurrency
Token-Based Limiting: Why request-based limiting is a common mistake in LLM systems
Circuit Breaker States: Closed → Open → Half-Open lifecycle with timeout-based recovery

Enterprise Architecture
Horizontal Scaling: Stateless gateway instances behind load balancer
Request Lifecycle: Auth → Cache → Quota → LLM → Update Counters → Log Metrics
Cache-First Pattern: Check cache before quota check to save Redis ops and LLM costs

=========
About me:
=========
I'm Mukul Raina, a Senior Software Engineer and Tech Lead at Microsoft, with a Master's in Computer Science from the University of Oxford, UK

#AISystemDesign #LLMGateway #ProductionAI #RateLimiting #CircuitBreaker #AIArchitecture #SystemDesign #LLM #APIGateway #EnterpriseAI #AIEngineering #MLOps