The Fine-Tuning Trap: What the Math Doesn't Tell You About Custom AI Models

Fine-tuning large language models can improve performance, but it often adds significant cost and operational burden.

Hidden expenses such as data labeling, infrastructure complexity, and model maintenance can quickly reduce ROI.

For many use cases, RAG and prompt engineering may deliver cheaper and simpler results than fine-tuning.

Teams should weigh performance gains against long-term maintenance and total system complexity before customizing a model.