Fine-tuning starts with a model that already knows a lot and then adapts it using additional task-specific data. This is usually cheaper and faster than training from scratch.
The main tradeoff is control versus simplicity. Fine-tuning can improve performance on a narrow problem, but it also introduces data quality, evaluation, and maintenance concerns.
