Can fine-tuning smaller models cut AI agent costs?
LLMs or SLMs usually has three practical goals. It can help a model handle knowledge from a specific field, keep answers in a fixed style or format, or lower costs by using an SLM instead of a larger model.
The data used for is a major part of the decision. data may be enough in some cases, while paid may be needed when quality, coverage, or control matters more.
Key points
- can add domain knowledge, enforce style, or reduce cost.
- SLMs may be cheaper to run than larger LLMs for narrow repeated tasks.
- The choice between data and paid affects both cost and quality.
- benefit most when the task is frequent, narrow, and has a stable output format.