Patterns d'architecture LLM en production
The scaling vocabulary applied to LLM endpoints, API Gateway, rate limiting, embedding caching, message queues, circuit breakers, autoscaling, load balancing. The skeleton a senior system builds before handling 10,000 concurrent requests or surviving a vector DB that crashes mid-query.
Strengths
- Shared vocabulary with non-AI backend engineers, eases collaboration
- Each pattern addresses a precise, measurable failure mode (latency, cost, availability)
- Model-provider agnostic, applies to OpenAI, Claude, self-hosted models
Limitations
- Wide conceptual surface, you need to know at least 7-8 bricks for a healthy stack
- Likely over-engineering if you serve <100 requests/day, only introduce on real need
Best for
- Devs shipping an LLM agent or inference endpoint to prod for the first time
- Platform teams who need to govern multiple product teams using LLMs
- System design interview prep with an AI / inference focus