The first version of most AI features runs on the biggest model available. It works, so nobody questions it. Then usage grows, the bill arrives, and someone asks whether a model one-tenth the size could do the same job.
For a narrow task, the answer is often yes.
What distillation means
Distillation trains a small model to imitate a large one on your task. The large model answers thousands of your real examples. A small model then learns from those answers. What you get is a model that does one thing well, runs on modest hardware and answers quickly.
Where it works
Tasks with a clear shape do well: sorting messages into categories, pulling fields from documents, rewriting text in a fixed style, answering from a known set of policies. The task does not change much, the right answer can be checked, and the volume is high enough that cost matters.
Where it does not
Broad, open-ended work, such as a general assistant that must handle anything a user types, still needs a large model. Tasks that need fresh knowledge are better served by retrieval than by training new facts into a small model.
How to test it before you commit
- Build a test set from real cases and keep it out of training.
- Score the large model on it. That is your baseline.
- Train the small model and score it on the same set.
- Compare quality, cost per request and speed, side by side.
- Decide with the table in front of you.
If the small model falls short on a few hard cases, route only those to the large model. Many teams end up with a small model for the bulk and a large one for the exceptions.
The takeaway
Choosing a model is a cost decision as much as a quality one. Measure first, then pay for only as much model as the task needs.
See how we approach this under custom LLM fine-tuning, and compare costs with the LLM cost calculator.
Work with us on this
- Custom LLM Training and Fine-Tuning: Domain-specific language models fine-tuned or distilled on your data, measured against your tasks, and delivered on weights you own.