Cost-Optimal LLM Routing with Limited User Feedback under User Satisfaction Guarantees

Abstract:Inference costs for large language model (LLM) applications are rapidly growing, driven by surging demand and rising infrastructure cost. Users expect high-quality responses, and in commercial settings this is formally codified in Service Level Agreements (SLAs), creating a fundamental tension between cost and quality. Recent progress on cost-aware LLM request routing has shown potential to resolve this tension, but existing approaches rely on complete feedback signals, offline training, extensive per-workload tuning, and most lack SLA guarantees or inference-time adaptivity. We introduce SLARouter, an online routing algorithm that learns a cost-optimal policy from the sparse, one-sided user feedback available in production systems. SLARouter provides theoretical guarantees for both cost optimality and strict SLA compliance. Experiments across a wide range of LLM benchmarks show that SLARouter satisfies SLA constraints without the need for per-benchmark tuning, reducing operating cost by up to 2.2x over existing baselines.

Preprint. Under review

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

I.2.0; H.3.3; I.2.7

arXiv:2606.19376 [cs.LG]

(or arXiv:2606.19376v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.19376 Focus to learn more arXiv-issued DOI via DataCite

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