Comparison
Fine-tuning vs training your own model
The difference between fine-tuning, post-training, and training from scratch, and which one you actually need.
In short
Fine-tuning lightly adapts an existing model to a task; post-training deeply specialises a base model on your domain while preserving its general ability; training from scratch builds a model from zero. For most enterprises, post-training, what Locai does with Forget-Me-Not™, gives domain expertise and ownership without the cost of training from scratch.
The three approaches, defined
- Fine-tuning: A light adaptation of an existing model to a narrow task or style; quick, but shallow and prone to forgetting.
- Post-training: Deeper specialisation of a strong base model on your domain, language, and workflows, preserving general reasoning.
- Training from scratch: Building a model from zero, maximum control, but very high cost and time.
What Locai does, and why
Naive fine-tuning causes catastrophic forgetting: the model gains your task but loses general ability. Locai's Forget-Me-Not™ framework post-trains a strong base on your data while preserving its general capabilities, producing a domain expert that still reasons well, and can keep learning continually. You own the resulting weights.
Fine-tuning vs post-training vs from scratch
| Post-training (Locai) | Fine-tuning | From scratch | |
|---|---|---|---|
| Depth of specialisation | Deep | Shallow | Deep |
| Preserves general ability | Yes (Forget-Me-Not) | Often degrades | Yes |
| Cost | Moderate | Low | Very high |
| Continual learning | Yes | Limited | Yes |
| You own the result | Yes | Often not (hosted) | Yes |
What this looks like with Locai
Where the comparison points toward owning your model, this is what it looks like delivered in practice.
Locai Labs believes organisations should own their intelligence. Renting access to a general-purpose model that lives on someone else's servers is fine for low-stakes work; for the AI that touches your data, your customers and your decisions, the model itself should be yours. That is the bet behind everything we build.
It is also a bet that an expert model beats a generalist on the work that actually matters to your business. A smaller model trained on your data, your language, your workflows and your edge cases routinely outperforms much larger generalists on the tasks you care about, and it does so on infrastructure you control. The goal is not the biggest model; the goal is the right model for your business.
And it is deployed sovereignly: an owned model that runs inside your perimeter, on-prem via Locai One, in your private cloud tenant, in a UK sovereign cloud, or fully air-gapped, depending on your residency and security requirements. Your prompts, your documents and your outputs stay inside your environment, under UK jurisdiction, with a data path designed to fit GDPR and the procurement standards regulated organisations are held to.
Frequently asked questions
What's the difference between fine-tuning and training?
Fine-tuning lightly adapts an existing model; training (from scratch) builds one from zero. Post-training sits between, deeply specialising a base model while keeping its general ability.
Is fine-tuning enough?
For narrow tasks, sometimes. For deep domain expertise without losing general reasoning, post-training is stronger, and it avoids the catastrophic forgetting that fine-tuning can cause.
What is post-training?
Specialising a strong base model on your domain, language, and workflows. Locai uses Forget-Me-Not to do this while preserving general capability.
Do I own a fine-tuned model?
Often not, fine-tuning on a hosted API usually keeps the result on the vendor's platform. With Locai's post-training you own the weights outright.
Book a sovereign AI briefing
A 30-minute session on owning your model: deployment options, the data path, and a clear cost range for your use case.