Patterns de scaling d'une API LLM
The set of architecture pieces that let a model-backed API absorb load without blowing up cost or falling over: a single entry gateway (authentication and rate limiting), a cache for identical embedding requests, message queues for asynchronous calls, circuit breakers to isolate a failing model, GPU autoscaling and load balancing across nodes. It is the classic architecture interview question: how do you hold ten thousand concurrent requests against a model API?
Strengths
- Provides shared vocabulary with platform teams, who already know these pieces outside AI
- Most pieces are classic infrastructure: nothing to reinvent, only to plug in the right place
Limitations
- Stacking the pieces too early adds failure points on traffic that does not justify them yet
Best for
- Framing the architecture of a service calling a model under real traffic