Inference Infrastructure

Inference infrastructure is everything that runs when a user sends a message to an AI product: the servers hosting the model weights, the systems routing requests, the caching layers, the load balancers, and the monitoring tools watching response times and error rates. Unlike training, which happens once (or periodically), inference runs continuously at scale as users interact with the product. The quality and capacity of inference infrastructure directly affects the user experience — response latency, reliability, and cost at scale all depend on how it’s designed. For behavior architects, inference infrastructure is context: constraints like maximum context length, latency requirements, and cost budgets shape which behavioral designs are actually feasible in production.