Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving
Authors: name: Yasmin Moslem, name: Magdalena Kacmajor, name: Vasudevan Nedumpozhimana, name: Ammar Abbas, name: Solmaz Panahi, name: David Lynch, name: Zhuangzhuang Nie, name: Alexandros Agapitos, name: Aleksandar Milenovic, name: Hongmeng Song, name: Yucheng Shi, name: Yue Pan, name: Patricia Buffini, name: John D. Kelleher

Efficient deployment of large language models (LLMs) in production forces a trade-off between accuracy and cost. Operators often default to a single model that is either expensive for easy queries or insufficient for hard ones. To address this challenge, we propose a two-stage cascaded solution. Stage 1 clusters incoming queries and assigns each cluster to its most cost-effective model. The cost budget for this routing process is set by an interpretable hyperparameter, tuned offline. Stage 2 adds a quality estimation (QE) cascade; when an output from Stage 1 is judged low-quality, the query is escalated to a stronger model. This ensures only hard or low-confidence cases reach the expensive models. On the test datasets, the cascaded system retains 97-99% of the strongest model's accuracy while reducing Time Per Output Token (TPOT). It requires only task-correctness labels and adapts to changes in the model pool without manual reconfiguration.