  Prompting Large Language Models (LLMs) performs impressively in zero- and
few-shot settings. Hence, small and medium-sized enterprises (SMEs) that cannot
afford the cost of creating large task-specific training datasets, but also the
cost of pretraining their own LLMs, are increasingly turning to third-party
services that allow them to prompt LLMs. However, such services currently
require a payment per call, which becomes a significant operating expense
(OpEx). Furthermore, customer inputs are often very similar over time, hence
SMEs end-up prompting LLMs with very similar instances. We propose a framework
that allows reducing the calls to LLMs by caching previous LLM responses and
using them to train a local inexpensive model on the SME side. The framework
includes criteria for deciding when to trust the local model or call the LLM,
and a methodology to tune the criteria and measure the tradeoff between
performance and cost. For experimental purposes, we instantiate our framework
with two LLMs, GPT-3.5 or GPT-4, and two inexpensive students, a k-NN
classifier or a Multi-Layer Perceptron, using two common business tasks, intent
recognition and sentiment analysis. Experimental results indicate that
significant OpEx savings can be obtained with only slightly lower performance.
