Encaisser le trafic sur une API LLM
When an API that calls an LLM goes from ten requests to ten thousand, it falls over if the architecture was not designed for it. Seven building blocks keep coming back: an API gateway (single entry point handling auth and rate limiting), rate limiting (protects the inference endpoints), caching (do not replay the same embedding request twice, that is burning money), message queues (handle LLM calls asynchronously, no instant reply), circuit breakers (one failing model call does not take down the whole pipeline), autoscaling (do not pay for idle GPU) and load balancing (spread traffic across the GPU nodes).
Strengths
- Each block has a single reason to exist: cost, failure, latency or load spike, never « just in case »
- Gives a vocabulary to talk scaling with infra without reinventing everything at each incident
- Caching and autoscaling alone often cut the inference bill by a visible factor
Limitations
- Wiring everything at once produces a heavy, opaque pipeline: each added block is one more failure point
- Advanced infra topic: without real load that actually hurts, these patterns stay theoretical and premature
Best for
- Devs shipping an LLM feature to production and watching traffic climb faster than expected
- Tech leads who must decide which scaling blocks are worth the cost right now