As businesses scramble to integrate Large Language Models (LLMs) into their workflows, a pertinent question arises: Is fine-tuning worth the investment? Fine-tuning offers domain-specific precision and improved user satisfaction, but requires tremendous upfront data, computational power, and skilled personnel. This presentation analyzes the economics of fine-tuning’s intricacies, when it is a good investment, when retrieval-augmented generation (RAG) is a more suitable option, and how companies can balance their need for custom solutions against cost considerations.