Comparison
Locai vs OpenAI & Anthropic for regulated enterprises
Rented general-purpose models versus an owned, domain-trained model, for organisations where data control is non-negotiable.
By James Drayson
In short
OpenAI and Anthropic offer the broadest general capability through rented APIs. Locai offers a model you own, deployed inside your perimeter and trained on your domain. For regulated enterprises, the deciding factor is ownership and privacy, not just raw generality, and a smaller expert model built on your data routinely outperforms a much larger generalist on the work that matters.
Where the big AI labs lead
OpenAI and Anthropic build exceptional general-purpose models with the widest breadth of capability. For consumer products and low-sensitivity general tasks, they're outstanding, and honestly hard to beat on sheer generality.
Where Locai leads
For regulated enterprises the priorities are different: keeping data in, owning the asset, and excelling on your specific domain. Locai delivers all three. The principle behind the work is straightforward: a smaller expert model trained on your data, your language, your workflows and your edge cases routinely outperforms a much larger generalist on the work you actually care about, while staying fully owned by you.
When each fits
- Choose a cloud API: for general, non-sensitive tasks, prototyping, and broad consumer use.
- Choose Locai: when data can't leave your perimeter, when you need to own the model, or when domain accuracy and auditability matter most.
Locai vs the big AI labs for regulated enterprises
| Locai (own) | OpenAI / Anthropic (rent) | |
|---|---|---|
| Own the weights & IP | Yes | No |
| Data stays in perimeter | Yes | No, sent to provider |
| Domain-trained | Yes, on your data | General-purpose |
| Air-gapped deployment | Yes | No |
| General breadth | Strong, domain-focused | Broadest |
| Domain quality | Expert on your data | Generalist across the web |
| Cost model | Fixed, owned | Per-token |
What this looks like with Locai
Where the comparison points toward running AI on your own hardware, this is what that looks like delivered as a product.
Locai Labs builds Locai One, an on-prem AI appliance. It is one machine that arrives with everything already in it: the hardware, our open-weight Locai Juno models, and Locai OS, the operating system that serves the models and handles users, access and monitoring. You plug it into a mains socket and your network, and your team is working in about 15 minutes. No cloud account, no per-token bill, and nothing leaving the building.
The reason a data-centre-class model fits in a box on your floor is SPACE, our compression algorithm. Instead of asking how much of a model can be cut while keeping it broadly similar, SPACE asks what the model needs to be good at, preserves the subnetworks behind those capabilities and strips back the rest. The result is a smaller specialist rather than a shrunken generalist, tuned to the exact hardware it ships on.
Locai One starts at £29,950 for a team, and Locai One Pro at £49,950 for an organisation, bought once and owned outright. Both run air-cooled on standard mains power and work fully air-gapped. Any compatible open-weight model runs alongside Juno, and if you need a model trained on your own proprietary data we can post-train one and deploy it on the same machine.
Frequently asked questions
Is Locai as capable as OpenAI or Anthropic?
On general breadth, the big AI labs lead. On your domain, a smaller expert model built on your data routinely outperforms a much larger generalist on the work you actually care about, and that is the model you own.
Can I use both?
Yes. Many enterprises use a cloud API for general, low-sensitivity tasks and an owned Locai model for sensitive, core, or regulated workloads.
What happens to my data with each?
With Locai, data stays inside your perimeter; with OpenAI or Anthropic, inputs are sent to the provider under their terms.
Why would a regulated enterprise choose Locai?
Ownership, in-perimeter privacy, domain training, air-gapped deployment, and auditability, the things rented cloud APIs can't provide.
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.