Architecture Overview
An LLM proxy serves as an intermediary layer between client applications and language model providers, implementing a request-response pattern with enhanced functionality. The architecture must handle high throughput, low latency requirements, and complex routing logic while maintaining reliability and observability.
Modern LLM proxies follow a layered architecture pattern, with each layer responsible for specific cross-cutting concerns. This separation enables modular scaling, independent testing, and clear boundaries between functional areas. Understanding these layers is essential for designing, deploying, and troubleshooting production systems.
Layered Architecture Pattern
Core Components
API Gateway
Exposes OpenAI-compatible REST endpoints, handles HTTP request parsing, response formatting, and streaming support for real-time token delivery to clients.
Auth Module
Validates API keys, JWT tokens, or OAuth credentials. Implements RBAC for fine-grained access control and maintains audit logs of authentication events.
Cache Engine
Stores response data with exact-match and semantic similarity lookup capabilities. Manages TTL-based expiration and cache invalidation policies.
Router
Implements intelligent routing logic based on model capabilities, cost, latency, and availability. Handles failover between providers automatically.
Metrics Collector
Aggregates request counts, token usage, latency percentiles, and error rates. Exports to Prometheus, Datadog, or custom monitoring solutions.
Config Manager
Handles dynamic configuration updates without restart. Manages model catalogs, routing rules, and rate limit policies from various sources.
Request Flow
Understanding the complete request lifecycle is crucial for debugging performance issues and implementing custom functionality. Each request passes through multiple processing stages before reaching the LLM provider.
Request Processing Pipeline
async def handle_completion_request(request): # 1. Authentication user = await auth_module.validate(request.headers) if not user: raise UnauthorizedError() # 2. Rate limiting if not await rate_limiter.check(user.id): raise RateLimitError() # 3. Cache lookup cache_key = generate_cache_key(request.body) cached = await cache.get(cache_key) if cached: return cached # 4. Route to provider provider = router.select_provider(request.model) # 5. Execute request response = await provider.complete(request) # 6. Cache result await cache.set(cache_key, response, ttl=3600) return response
Caching Architecture
The caching layer is critical for performance and cost optimization. Modern LLM proxies implement multi-level caching with both exact-match and semantic similarity capabilities.
| Cache Type | Storage | Hit Rate | Latency |
|---|---|---|---|
| Exact Match | Redis / Memory | 20-40% | <5ms |
| Semantic Cache | Vector DB + Redis | 40-70% | 15-50ms |
| Embedding Cache | Redis | 60-80% | <10ms |
💡 Cache Key Design
Cache keys should include model identifier, prompt hash, temperature, and other parameters affecting output. For semantic caching, compute embeddings for similarity search while maintaining separate exact-match cache for identical requests.
Routing Logic
Intelligent routing maximizes value by directing requests to optimal providers based on multiple factors. The router evaluates cost, latency, availability, and capability requirements for each request.
Cost-Based Routing
Direct requests to the cheapest provider capable of handling the task. Maintain real-time cost tables and implement budget-aware routing for cost-sensitive applications.
Latency-Based Routing
Prioritize providers with lowest response times. Track latency percentiles per provider and model, routing time-sensitive requests to fastest options.
Failover Routing
Implement automatic failover when primary providers experience outages. Configure fallback chains with preferred backup providers for each model type.
Capability Routing
Route requests to providers based on feature requirements like vision, function calling, or specific context lengths. Match request capabilities with provider strengths.
Deployment Patterns
LLM proxies can be deployed in various configurations depending on scale, reliability requirements, and infrastructure constraints. Each pattern offers different trade-offs between complexity, cost, and performance.
apiVersion: apps/v1 kind: Deployment metadata: name: llm-proxy spec: replicas: 3 template: spec: containers: - name: proxy image: llm-proxy:latest ports: - containerPort: 8000 env: - name: REDIS_URL valueFrom: secretKeyRef: name: llm-secrets key: redis-url resources: requests: memory: "512Mi" cpu: "500m"
🔗 Architecture Resources
Continue exploring: Gateway vs Proxy Difference | Security Best Practices | Why Use LLM Proxy | Production Deployment