Analysis of LLM Performance on AWS Bedrock: Receipt-item Categorisation Case Study

Serving open-weight language models on premises forces a trade-off between accuracy and computational cost. Hosting models of different sizes lets easy queries go to a small, efficient model and hard ones to a large model. We present CRE-Router (Cluster, Route, Escalate), a two-stage method that decides which of these models answers each query. It clusters queries and routes each cluster to the model that best trades error against cost, under a budget on the time per output token (TPOT) or the end-to-end latency per request (E2EL). It then either accepts the answer or, when a quality estimator rejects it, escalates the query to the strong model. Routing decides before generation, so a hard query goes straight to the strong model; escalation decides after, so it can catch the errors routing cannot predict. It needs no annotation beyond task-correctness labels. We evaluate CRE-Router on TeleMath, TeleQnA and AIME, with pools of two to nine models served on A100 GPUs. CRE-Router is more accurate than the efficient models in its pool and comparable to the strong model it escalates to, at a lower cost per token. We also find that the choice of cost term decides which models serve the workload, that pricing each model per cluster shortens the wait per request, and that a change of hardware changes the best routing.

Businesses increasingly rely on large language models (LLMs) to automate simple repetitive tasks instead of developing custom machine learning models. LLMs require few, if any, training examples and can be utilized by users without expertise in model development. However, this comes at the cost of substantially higher resource and energy consumption compared to smaller models, which often achieve similar predictive performance for simple tasks. In this paper, we present our vision for just-in-time model replacement (JITR), where, upon identifying a recurring task in calls to an LLM, the model is replaced transparently with a cheaper alternative that performs well for this specific task. JITR retains the ease of use and low development effort of LLMs, while saving significant cost and energy. We discuss the main challenges in realizing our vision regarding the identification of recurring tasks and the creation of a custom model. Specifically, we argue that model search and transfer learning will play a crucial role in JITR to efficiently identify and fine-tune models for a recurring task. Using our JITR prototype Poodle, we reduce inference time by up to 7.5x compared to a self- hosted LLM and save more than $2,200 per 1M requests compared to a flagship hosted LLM, while achieving accuracy competitive with the LLM baseline.

Language models make it easy to build classifiers by simply prompting a model with the sample to classify. However, their predictions are neither deterministic nor uniformly reliable across samples. How can we combine predictions from different models to improve reliability? We view LLM classifiers as noisy measurement instruments of an underlying target label. Our goal is to maximize average classification accuracy at a given average query cost. To this end, we introduce circuits that organize LLM classifiers into an adaptive sequence of queries and decisions. We then characterize the optimal circuit, obtain the fundamental limits of achievable accuracy, and provide practical insights on realizing the optimal circuit. Across seven classification tasks spanning safety, health, political science, human preference, agent evaluation, decision-making and reasoning, our method improves the cost-accuracy frontier over routing and cascading baselines.