Sovereign AI

Frontier LLM APIs are the fastest way to start — and, past a certain scale, the most expensive, least predictable, and least private way to run in production. Sovereign AI is the alternative: open-weight models (Llama, Mistral, Qwen and their kin) deployed on infrastructure you own, tuned and evaluated so they hold up on your actual work. It exists for three reasons, and most teams come to us for a mix of them.

Predictable cost and compute

On a frontier API you rent intelligence by the token, on someone else’s price list, which can and does change. Self-hosting turns that variable, per-call meter into a fixed, capacity-based cost you can forecast — the same GPUs, the same latency, the same bill whether you run a thousand calls or a million. For steady, high-volume workloads that’s usually cheaper past the crossover point, and always more predictable.

Your data never leaves your servers

If you handle regulated, confidential, or contractually-restricted data — health records, financial detail, legal documents, anything under HIPAA, SOC 2, or GDPR — sending it to a third-party API is often a non-starter. A sovereign deployment keeps every prompt and response inside your own VPC, data centre, or air-gapped environment. The model comes to your data; your data doesn’t go to a vendor.

Model behavior that doesn’t change under you

Frontier models update silently. A prompt that worked last month can quietly return something different this month, because the model underneath moved. When you host an open-weight model you pin the exact version — behavior is reproducible, and it only changes when you decide to change it. For anything you’ve evaluated and certified, that stability is worth as much as the privacy.

How we migrate you off a frontier model

The risk in switching is quality: will the open model be good enough? We answer that with evidence, not optimism. We build an evaluation harness that scores your current frontier output on your real tasks, then benchmark candidate open models against it until we find one that matches — right-sizing to the smallest model that clears the bar, because a 70B model you don’t need is just a bigger GPU bill. Then we deploy, wire it into your stack, and monitor for drift. You switch when the numbers say quality holds, not before.

When self-hosting is right — and when it isn’t

We’ll tell you honestly. Self-hosting wins when you have steady volume, sensitive data, or a need for stability. It’s the wrong call when you’re still prototyping, your volume is low and spiky, or you genuinely need the absolute frontier of capability for a hard reasoning task — there, a frontier API is cheaper and smarter, and we’ll say so. The honest answer is often a hybrid: sovereign models for the bulk, private, high-volume work, a frontier API for the rare hard case. This is the concrete delivery of a principle we write about in what AI projects really cost: solve the problem first, then right-size for cost. It pairs naturally with ML model deployment and private RAG on your own data.