Compare

Owned, rented, local.

Who owns the model, and does it keep learning? One table, three answers.

Compared	Rented AI	Local AI	Owned AI, Eigna
Where it lives	In someone else’s building	On your own machine	Where you decide: your machines or your region
Whose model	The vendor’s	A stock open model, the same for everyone	Yours, with a signed Model Deed
Your data	Sent to the provider	On the device	On your machines with Local, in the region you choose with Hybrid and Cloud
Learns your work	No. Where it learns from use, it trains the vendor’s model	No. It remembers context	Yes, from approved work only
After go-live	Changes on the provider’s schedule	Stays as shipped	Keeps learning, released only when better
The bill as use grows	Per seat or per token, rising with use	Flat, capped by the machine	Setup, then a fixed running cost. No token bill
Who runs it	The provider	You	We deliver, set it up and run it
Local fit	One global default	One general model	Your language, laws and terms

Our reading of public material, checked 24 October 2026.

Where renting is still right

Cost as use grows.

Rented AI bills every token, so heavy users pay more the more they succeed. An owned model runs at a fixed cost until the machine is full.

For light use of the cheapest rented models, renting stays cheaper. We make no cost claim against it.
For email, office work and open questions, the best general model is rented to everyone. Keep it.
The case for owning is the work that defines you: quality on your task, control of every version and a cost you can plan.

Pricing

UseMonthly cost

Rented: billed by use
Owned: a fixed fee per site or GPU

Illustrative shape, no figures on the axes.

Eigna and the alternatives

Every part is for sale. The combination is not.

Open weights, tuning platforms, machines on site and boxes at home are all for sale today. A model you own, that keeps learning your approved work and comes delivered and run, is not.

OwnedThe weights, the data and the experts belong to you.Also offered by Mistral Forge, Prem, Arcee, Fireworks and Ghost.

Keeps learningRetrained on approved work. Live only when it beats the current version.Sold as a tool for ML teams, or for the vendor’s own model.

Delivered and runLocal or cloud, on your hardware or ours. No ML team to hire.Mistral does it, for the largest firms.

EignaOwned, keeps learning, delivered and run. Together, only here.

Compared	Rented assistants	Tuning platforms	Local AI	Owned, Eigna
For example	ChatGPT, Claude, Gemini, Copilot	Fireworks, Together, Cohere, Mistral	Ollama, AI PCs, Ghost	Eigna
Whose weights	The provider’s	Yours, if tuned on open weights	A stock open model	Yours, with a Model Deed
Learns your work	No, context only	Once per job, run by your ML team	No, memory only	From approved work, gated
Gate and rollback	On the provider’s schedule	You build it	None	Release gate, record, rollback in 60 seconds (designed)
Where it runs	Their cloud	Their cloud, some on site	On the device	Local, Hybrid or Cloud
Cost as use grows	Rises with use	Rises with use	Flat, capped by the box	Flat per site or GPU
General work	Best available	Same as the base	Below the frontier	Below the frontier: keep a rented model for it

Choose a rented assistantFor broad office work, open questions and light use. Minutes to start, the best general model, and the lowest cost when use is light.
Choose a tuning platformWhen you have an ML team that wants to run each training job, its tests and its releases itself.
Choose local AIFor a general model on one machine, at home or on a desk. It remembers context; it does not learn your work.

Our reading of public material, checked 24 October 2026. Vendors change their offers often; tell us if a cell is out of date.

Questions and answers

All questions

Is owned AI cheaper than rented AI?

For heavy, steady use of the mid and top tiers, a fixed running cost wins. For light use of the cheapest rented models, renting stays cheaper, and we make no cost claim against it. The stronger point is a cost you can plan.

Tokens keep getting cheaper. Does that change the case?

Unit prices fall, and bills keep rising as use grows: the price per token fell to 0.42 times while the bill more than doubled (Bain, September 2026). For the rest, the case is quality on your task and control of every version.

Open models will make this a commodity, won’t they?

We hope so. Better open models lower our cost, and we re-base on the best one, through the gate. What does not become a commodity is your data, your sealed test set, your release record and our Formula for your field.

Start

See which column your work belongs in.

The fit check takes two minutes and recommends a run mode. Or bring your questions to a founder.

See if it’s a fit Book a call