The article examines the practice of routing requests to different large language models (LLMs) during a single session. It reports that switching models mid‑session can negate the expected cost savings from using cheaper inference options. The author conducted tests to illustrate how these switches impact overall expenses.
Enterprise Document Intelligence [Vol. 1 #9ter] - The pipeline from Article 9 calls a model at several steps to be sure it is right.
Model routing can reduce the cost of LLM applications by assigning simpler requests to cheaper models without sacrificing the quality of the response. However, most routers treat requests as independe...
The article compares seven open decision models with a generative large language model (LLM) in a hands‑on repository called Jev. It evaluates each model based on routing accuracy, latency, and output reliability. The comparison provides practical insights into how these models perform in real‑world scenarios.
I measured the actual GPU electricity for eight local models on one RTX 3090 — and the cheapest wasn't the smallest, nor the priciest the biggest. The post How Much Does It Actually Cost to Run a Local LLM?
Using mathematical optimization to solve a pickup-and-delivery problem with time windows. The post “Los Movimientos”: The Routing Problem That Nearly Broke My Spirit appeared first on Towards Data Science .
How a seemingly harmless move to a multi-agent architecture quietly tripled our LLM costs and what actually fixed it. The post The 3× Token Bill We Didn’t See Coming appeared first on Towards Data Science .
arXiv:2606. 19376v1 Announce Type: cross Abstract: Inference costs for large language model (LLM) applications are rapidly growing, driven by surging demand and rising infrastructure cost.
arXiv:2609.08189v1 Announce Type: new Abstract: Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, ho...
A team cut their AI inference bill by more than half. Three months later, customer satisfaction was dropping and the cost savings were tied to the quality loss.
The paper investigates why large‑language‑model (LLM) routers—systems that select the best model for each query—often fail to outperform a single best model. By evaluating 14 models on 294 questions across seven task types and three languages, the authors find that a simple static mapping of task type to model improves 21 of the 29 questions that routing could potentially solve, and that learned routers do not significantly exceed this performance. The study highlights that most routing gains stem from task‑type specialization rather than complex learned decision rules.
Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without sacrificing answer quality.
arXiv:2608.23023v2 Announce Type: new Abstract: An LLM router picks which model should answer each query. The appeal is that models fail on different questions. Whatever single model is best overall...