---
title: "Fine-tuning"
canonical: "https://router.xark.io/glossary/fine-tuning"
description: "Fine-tuning is further training of an already-trained model on your own examples so that it adapts to a task, a format or a voice."
section: "glossary"
updated: "2026-09-02"
source: "https://router.xark.io/glossary/fine-tuning.md"
---

# Fine-tuning

*Also known as: finetuning, LoRA, supervised fine-tuning, custom model.*

Fine-tuning is further training of an already-trained model on your own examples so that it adapts to a task, a format or a voice, producing a new set of weights that then has to be served somewhere.

Full fine-tuning updates every parameter and produces a complete new checkpoint. Parameter-efficient methods, of which LoRA is the common one, train a small adapter alongside frozen weights instead — far cheaper to train, small enough to store many of, and swappable at serve time. Almost everything described commercially as fine-tuning today is the second kind.

The training run is usually the smallest of the three costs. Building and cleaning a dataset that is actually representative is the largest, and serving the result is the one that never stops: a fine-tuned model is your model, and it does not share capacity with anyone else's traffic.

It earns its place on a narrow, stable, high-volume task — particularly where a small fine-tuned model can replace a large prompted one, or where an output format has to be exact. It does not earn its place on anything a better prompt or a retrieval step would fix, because every change to the task re-runs the whole cycle.

This service does not offer fine-tuning. Several providers on the comparison pages do, and if training and serving on one platform is what you need, that is a real reason to use one of them instead.

## What it costs you

Fine-tuning trades a variable cost for a fixed one. Prompting costs tokens on every request forever; fine-tuning costs a dataset, a training run and then a serving arrangement that is charged whether or not traffic arrives. That arithmetic only works above a volume threshold, and the threshold moves every time per-token prices fall — which has been the direction of travel throughout the open-weight era. Most teams that consider fine-tuning to save money would save more, sooner, by shortening a system prompt.

## Related terms

- [Distillation](https://router.xark.io/glossary/distillation.md) — Distillation is the training of a small model to reproduce the outputs of a larger one, so that the smaller student performs close to the teacher across a particular range of work while costing a fraction as much to serve.
- [Model weights](https://router.xark.io/glossary/model-weights.md) — Model weights are the numeric parameters a training run produces — the entire learned content of a model — and a published checkpoint of them, together with the code and configuration needed to run it, is enough to reproduce that model's behaviour anywhere the hardware exists.
- [Open-weight model](https://router.xark.io/glossary/open-weight-model.md) — An open-weight model is one whose trained parameters are published under a licence permitting others to download, run and serve them, which is what makes it possible for anyone other than the original lab to offer that model as an API.

## See also

- [How we compare to Together AI, who do offer it](https://router.xark.io/compare/together-ai)
- [How we compare to Fireworks AI](https://router.xark.io/compare/fireworks)