Explainer
On-premise AI for enterprise
Running AI inside your own data centre or on dedicated hardware, full control, predictable cost, and no data leaving your perimeter.
In short
On-premise AI runs large language models inside your own data centre or on dedicated hardware you control, rather than calling a model over the internet. It gives enterprises full data control, predictable fixed cost, and the option to own the model outright.
Benefits of going on-premise
- Data stays in: Every inference happens inside your perimeter, so sensitive data never leaves.
- Fixed, predictable cost: An owned appliance replaces an open-ended per-token bill that grows with usage.
- Performance & latency: Local inference is fast and predictable, with no dependence on an external service.
- Ownership: Run a model you hold the weights to, and improve it continually on your data.
Making on-premise turnkey
On-premise AI used to mean building a serving stack and MLOps practice from scratch. Locai One removes that: it is a fixed-cost on-prem appliance that bundles a sovereign model, an application layer (chat, API, usage platform), and serving, so you get on-prem control without the integration burden. Hardware can run from a single GPU server up to multi-GPU nodes depending on model size.
What this looks like with Locai
An AI computer is only as useful as what comes inside it, the model, the application layer, and the deployment story.
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 hardware do I need for on-premise AI?
It depends on model size, a 35B model runs on a single A100 80GB server, while larger models use multi-GPU nodes. Locai advises on hardware or supplies it as part of Locai One.
Is on-premise AI more expensive than the cloud?
There's an upfront investment, but for sustained enterprise usage the fixed cost of an owned appliance is typically far lower than recurring per-token API fees, and you keep the asset.
Can on-premise models stay current?
Yes. With continual learning the model is retrained on your evolving data on a schedule, so it keeps improving rather than freezing at deployment.
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.