Orchestrating Large Language Models in Time-Constrained Marketplace Systems: A Cost and Latency Optimization Framework

Abstract

Digital marketplaces facilitating advertising transactions and financial product exchanges operate within microsecond-level temporal boundaries where processing determinations require rapid completion. Advanced neural language architectures provide sophisticated analytical capabilities for contextual interpretation, behavioral intent extraction, and risk pattern detection, yet impose considerable computational burdens and exhibit variable processing intervals. This article addresses integration strategies for neural language systems within temporally restricted operational cycles while preserving economic sustainability and system responsiveness. The coordination challenge spans multiple technical domains: selecting appropriate architectural scales, managing pre-computed contextual states, distributing processing time across pipeline components, and optimizing accelerator utilization between immediate-response and background operations. Drawing from recent progress in inference acceleration and production marketplace deployments, this article presents a governance-oriented coordination framework that positions neural language capabilities as regulated computational resources within managed decision architectures. The article combines system engineering principles for optimization and state management, financial models analyzing deployment economics, and domain expertise in auction theory and risk quantification.