Fine-tuning an open weight model typically delivers somewhere around 80 to 90 percent of the value of training a model from scratch for around 5 to 10 percent of the cost, and parameter-efficient techniques like LoRA and QLoRA now let teams fine-tune models with 65 billion or more parameters on a single GPU. The real question for most teams is not whether fine-tuning is cheaper, it is whether the three-year total cost of ownership, typically 30 to 40 percent of the equivalent API spend at the same volume, actually beats staying on a frontier API. This session walks through how to make that call properly, not just on cost, but on maintenance burden, model drift, and what happens when the base model you fine-tuned gets deprecated out from under you. Key takeaways: - The real cost range for fine-tuning today, from a few thousand dollars for a parameter-efficient job to well into six figures for a heavily customized model - When the three-year total cost of ownership actually favors fine-tuning over staying on a frontier API, and when it does not - What ongoing maintenance a fine-tuned model actually requires that a hosted API does not - How to plan for base model deprecation before you are forced to re-tune on a deadline If you are staring down a build versus buy decision on model customization, this session gives you the real numbers to run it with.