Full fine-tuning updates the model’s weights and needs real data and real compute. Most production work does not need it: a lightweight adapter such as a LoRA, or a trained identity inside a platform, gets ninety per cent of the benefit for a fraction of the cost and can be swapped out per project.
The reason to fine-tune properly is a house style that has to hold across thousands of assets and outlive any one tool. The reason not to is that a fine-tune is a maintenance commitment, and a stale one is worse than none.
Should I fine-tune a model for my brand?
Only after a LoRA or a trained identity has failed to hold the style at the volume you need. Start light, measure the failure rate, escalate if the numbers justify it.
What is the difference between fine-tuning and prompting?
Prompting steers a general model at run time. Fine-tuning changes what the model is, so the behaviour persists without being asked for each time.