Private model / fine-tuning
Fine-tuning an open model
on your own data
Private model / fine-tuning is for a software company. Two to six weeks. We run it for you, in your own cloud account: evaluate first, fine-tune an open model only if the hosted model falls short, and deliver an endpoint and a comparison report. It can start on its own or after an AI production sprint.
The facts
At a glance.
- Who it is for
- A software company whose hosted model may fall short.
- Length
- Typically two to six weeks, depending on data and scope.
- What you get
- A written decision. If we fine-tune, an endpoint and a comparison report. We run it for you, in your own cloud account.
- What happens after
- It can start on its own or after an AI production sprint. We fine-tune only if the hosted model falls short.
- Pricing
- Contact us for pricing.
One task
Tell us where the hosted model falls short
A rule about where the data may live, a bill that grows with volume, or a narrow task you run over and over. We say whether Private model / fine-tuning should start on its own or after an AI production sprint.
The fit
When a private model fits.
Four reasons to look at Private model / fine-tuning. If none of them is true, we recommend you stay on the hosted model.
We run it for you
We run it for you, in your own cloud account. Self-hosting is available on request, on Amazon Web Services, Microsoft Azure, Google Cloud, GleSYS or in your own on-premises environment. It runs where your data is allowed to be.
Cost at high volume
The same narrow task, called often enough that the bill is the question. The report compares cost with the model you use now. We do not quote a saving before that comparison exists.
A narrow, repetitive task
The examples look like each other. A fine-tune is for that kind of work. A broad assistant is a different job, and it stays on the hosted model.
A domain the hosted model handles poorly
Your terms, formats or language, where the evaluation shows it gets things wrong.
How it works
The decision comes before any fine-tune
We train only after an evaluation on your own examples says the hosted model is not good enough.
Start with an evaluation
On its own, or after an AI production sprint. The evaluation step ends with a written decision you can walk away with.
Read the evaluations
We run the hosted model on your own test examples (golden evaluation set). The question is whether it falls short on the task you actually have. That call is written down.
Fine-tune only if it falls short
If the evaluations show the hosted model is not good enough, we fine-tune an open model on your own examples. If it is good enough, we recommend you stay on it, and we do not train.
We run it for you
In your own cloud account. You get an endpoint, and a simple report that compares it with your current model on accuracy, cost and speed.
Evaluations
first.
We fine-tune when the hosted model falls short on your own test examples.
What you leave with
A decision, and a model only if the decision says so.
It ends with a written decision. The endpoint and the report follow only when we fine-tune.
The decision, in writing
Fine-tune, or stay on the hosted model. The evaluations are the reason.
An endpoint
If we fine-tune, you can call the model where it is hosted. If we do not, there is no new endpoint.
A simple comparison
If we fine-tune, a simple report sets the new model next to your current one on accuracy, cost and speed.
A decision, not a default
Why the fine-tune is a decision, not a default.
A private model is the right tool for some tasks. It is the wrong default for most of them.
We run it for you
In your own cloud account. Self-hosting is available on request. The model stays where the data has to stay.
Evaluations first
We do not fine-tune because a private model sounds safer. We fine-tune when the hosted model falls short on your own test examples (golden evaluation set).
Your examples
The fine-tune uses the examples you give us for this model. We do not train on your data for anything else.
Where it sits
On its own, or after a sprint.
It can start on its own or after an AI production sprint. Care, a coding setup, a person in the team, or a platform takeover are the other shapes.
AI production sprint
A fixed build: one system in four to six weeks. Private model / fine-tuning can follow it if you need a private model.
Read more →RetainerManaged AI operations
Monthly care once a system is live. A private model does not replace that agreement.
Read more →Engineering teamsAI coding rollout
When the work is your engineering team's coding agents, not a model for one task.
Read more →ConsultingForward-deployed engineers
A senior in your team, for the AI feature and the product engineering around it.
Read more →Full-servicePlatform Partner
When the system lives on an app, portal or connected product that nobody owns yet.
Read more →AIAll AI packages
Sprint, Managed AI operations, AI coding rollout and Private model / fine-tuning, and how we choose between them.
Read more →Related insights
From a hosted model to one you can trust.
How we think about shipping one system and knowing whether a change is actually better.
AI pilot to production: a practical playbook for Nordic product teams
Most AI pilots never become product value. Here is a field-tested playbook for taking an AI initiative from demo to stable production impact.
Read insight →AI Product Development · 6 min readAgent readiness before you scale AI
Agentic workflows fail fast when data access, ownership, and logging are unclear. A short readiness sprint surfaces the gaps before production.
Read insight →Questions
Common questions.
Short answers before you treat a private model as the plan.
Where does the model run?
We run it for you, in your own cloud account. Self-hosting is available on request, on Amazon Web Services, Microsoft Azure, Google Cloud, GleSYS or in your own on-premises environment. We do not move the examples off that environment to train somewhere else.
Is Private model / fine-tuning its own project?
Yes. It can start on its own or after an AI production sprint. On its own it starts with an evaluation; after a sprint it can use that sprint's evaluations. Either way, nothing is trained until the evaluation says so.
What does it cost?
Contact us for pricing. It can start on its own or after an AI production sprint.
Do you always fine-tune?
No. We fine-tune only when the evaluations show the hosted model falls short. Otherwise we recommend you stay on the hosted model, and we do not train.
What do we get?
A written decision either way. If we fine-tune, you also get an endpoint and a simple report comparing the new model with your current one on accuracy, cost and speed.
Do you train on our data?
Only on the examples you give us for this model, and only if the evaluations say we should fine-tune. We do not train on your data for anything else, and we do not train at all if the hosted model is good enough.
Ready to scope it?
Bring the task and the examples
We will tell you straight if the hosted model is enough, if Private model / fine-tuning should start on its own or after an AI production sprint, or if we are the wrong people.
Ready to talk?
Let's talk about
what you're building
Or reach us directly at hello@aidoni.se
