Comparison
Sovereign AI vs AWS
AWS is strong on infrastructure sovereignty. But the real test is whether you own the model, not just where it runs.
In short
AWS is excellent for infrastructure sovereignty, regions, residency, and its European Sovereign Cloud, but with Bedrock the model itself is still rented from a third party. Locai is sovereign at the model layer: you own the weights, the IP, and the update cadence, not just the data centre.
Infrastructure sovereignty vs model sovereignty
AWS genuinely solves a hard problem: keeping your data and compute in the right jurisdiction with strong controls. For infrastructure sovereignty it is a credible, mature option, and Locai models can run inside your AWS tenant.
But infrastructure is only one layer. With a hosted model service the model you actually use is owned by someone else, frozen between releases, and changeable or withdrawable under you. Running a rented model in a sovereign region does not make the model sovereign.
Model sovereignty is the missing layer: holding the weights, controlling the training data, and deciding the update cadence. That is what Locai adds, and it is the layer that determines whether the capability is truly yours.
Own with Locai vs rent the model via AWS
| Own with Locai | Rent the model via AWS | |
|---|---|---|
| Infrastructure residency | Your choice, incl. inside AWS | Strong (Sovereign Cloud) |
| Own the model weights | Yes | No, rented via the model service |
| Model can be deprecated | Never, it's yours | Yes, by the model provider |
| Trained on your domain | Yes, post-trained on your data | General-purpose foundation models |
| Improves continually | Yes | Frozen between releases |
| Cost model | Fixed, owned asset | Per-token / per-call |
| Full weight & data audit | Yes | No |
| Air-gapped option | Yes | No |
What this looks like with Locai
What sovereign AI actually looks like in production is the part most marketing skips, so here is the short version.
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
Can I run Locai inside my existing AWS environment?
Yes. Locai models can be deployed inside your own AWS tenant, so you keep your cloud relationship and residency while gaining model ownership on top.
Isn't AWS already 'sovereign'?
AWS provides strong infrastructure sovereignty. The gap is model sovereignty: with a hosted model service you still don't own the weights or control the update cadence. Locai closes that gap.
Does this mean I should leave AWS?
No. This isn't AWS versus Locai at the infrastructure layer, you can keep AWS and add model ownership with Locai. The point is to be sovereign at the model layer, not just the data-centre layer.
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.