AWS launches prefix-aware routing for SageMaker Inference

Prefix caching already existed in LLM serving frameworks like vLLM and TensorRT-LLM, storing the computed key-value pairs for prompt beginnings seen before so repeated text is not reprocessed. The catch appeared only at scale: behind an endpoint spread across a fleet of machines, the same 3,000-token instruction block was landing on different instances, and none of them saw it often enough to build a reliable cache, leaving the caching feature present but largely ineffective.

Prefix-aware routing closes that gap at the routing layer rather than in the model container. The endpoint inspects the start of each payload and directs identical beginnings to the same instance, with two safeguards: overflow routing to a less busy instance once a concurrency limit is reached, and stable placement when instances are added or removed so caches are not invalidated on every scaling event. It joins two existing strategies, RANDOM and LEAST_OUTSTANDING_REQUESTS, and AWS measured it across 16 test configurations on 7 ml.p5.48xlarge instances with vLLM.

The benefit is uneven by workload, which is the practical reading for anyone weighing it. The longer the shared prefix, the larger the payoff, so long-context workloads with 8,000-token shared prefixes gained far more than short, variable ShareGPT-style conversations. Whether a team actually captures those gains is likely to hinge on following AWS's operational guidance: prefix caching must be enabled in the serving framework, and request serialization must stay consistent, because JSON whitespace and key ordering can change routing decisions and scatter requests that should have stayed together.