Self-hosted models,
when that's what the job needs.
Llama is Meta's open-weight model family — we deploy it self-hosted when a client needs the model to never leave their own infrastructure, or when per-token API costs at volume make a hosted model the wrong economics.
Serving UK and EU clients: GDPR, EU AI Act, and data residency are covered on our Trust & Safety page.
Open weights,
your infrastructure.
Llama models are open-weight, which means they can be fine-tuned and run entirely inside a client's own infrastructure or VPC — no data leaving to a third-party API. That matters for data residency requirements, strict compliance environments, and high-volume workloads where hosted API costs would otherwise dominate the budget.
The tradeoff is that you take on hosting and serving — which is why this is a deliberate choice per project, not a default.
Hosted the same way
we'd host anything production.
Self-hosting an open model doesn't mean treating it casually.
Using tools like Unsloth or Axolotl for efficient fine-tuning on domain-specific data.
Deployed with the same monitoring, cost tracking, and uptime discipline as any hosted model, via our MLOps work.
The model and the data stay inside the client's own environment — the whole point of choosing this path.
Where this fits
Open-weight deployment is one option in a broader LLM integration and MLOps practice.
Before you
book a call.
The questions we get asked most about self-hosted Llama deployment — answered straight, no sales pitch.
Why self-host Llama instead of using a hosted API model?
Primarily data residency and cost at volume — self-hosting means data never leaves your infrastructure, and per-request cost stops being a per-token API charge once you own the serving infrastructure.
Is a self-hosted open model as capable as Claude or GPT-class models?
It depends on the task. For many well-scoped, domain-specific jobs — especially after fine-tuning — it's a strong fit. For the hardest reasoning tasks, a frontier hosted model is often still the better call. We'll tell you honestly which applies.
What's involved in deploying this?
Model selection, optional fine-tuning on your data, and production-grade serving infrastructure — monitoring, scaling, and cost tracking, the same discipline as any hosted service.
Tell us what
you're trying to build.
Book a 30-minute call — we'll tell you honestly whether a self-hosted open model fits your requirements, or whether a hosted API is the simpler path.