One post tagged with "lora"

Most engineers underestimate fine-tuning costs by a factor of three to five. The training run is the smallest part of the bill. Data curation, failed experiments, deployment infrastructure, and ongoing model maintenance are where budgets actually go. Teams that skip this math end up months into a fine-tuning project before realizing that a well-engineered prompt with few-shot examples would have solved the problem in a week.

This post walks through the complete economics — what fine-tuning actually costs across its full lifecycle, when LoRA and PEFT make the math work, and a decision framework for choosing between fine-tuning and prompt engineering based on real production numbers.