Model distillation is a technique aimed at increasing the computational efficiency of large language models (LLMs) by transferring knowledge from a larger, complex model (the "teacher") to a smaller, more efficient model (the "student"), ultimately achieving similar performance with reduced computational resources and costs. This approach involves creating a dataset based on the teacher model's outputs and fine-tuning the student model to mimic these outputs, which is facilitated by techniques like temperature scaling. While model distillation offers benefits such as reduced latency and operational costs and enhanced scalability, it also poses challenges like potential accuracy loss, dataset creation complexity, and technical intricacies in fine-tuning. OpenAI provides a structured process for model distillation, but it faces limitations in model selection, evaluation restrictions, and technical user interface complexity. Alternatives like Humanloop offer more flexible and collaborative platforms for managing evaluations and prompt management, allowing enterprises to adopt best practices when deploying LLMs.