Routing LLM Inference in Production: From Engine Signals to Policy

Official Schedule Context

Production LLM apps need more than a fast model: they need an inference routing layer that can

choose where each request should run as engines, capacity, latency, and geography cost change. This

talk shares a generalized Inference Load Balancer (ILB) proxy/controller architecture. A low-latency

proxy applies routing weights and request-path signals, while a controller computes source-cluster-

to-engine weights from demand, capacity/performance profiles, replica state, and geography cost. We

will cover the practical debugging patterns AI engineers need: reading engine signals, explaining

why a request went to one backend instead of another, handling retries and load shedding, and

keeping routing behavior observable without exposing OpenAI-specific internals or non-public

metrics.

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