Private LLM Deployment
A private LLM is a language model whose weights run on infrastructure you control, so your prompts and documents never leave your perimeter. This page covers when that is worth doing, what it takes, and when it is not.
When self-hosting is the right call
There are four reasons that hold up. If none of them describes you, an API is probably the better answer, and we would rather say that than sell you a GPU cluster.
A rule prohibits the disclosure
HIPAA, GLBA, ITAR, FERPA, CJIS, a client MSA or a bar confidentiality duty. Sending data to a third-party model is a disclosure whether or not a human reads it. This is the most common reason, by a wide margin.
The token bill has overtaken the hardware
Per-token pricing is excellent at pilot volume and punishing at production volume. Above a certain sustained throughput, owning the hardware is simply cheaper, and the crossover point is lower than most teams assume.
You need guarantees an API cannot give
Fixed latency, guaranteed availability, and a model that does not change underneath you. Providers deprecate and silently update models; a self-hosted weight file does not move unless you move it.
You are building on data you cannot re-create
If your advantage is a proprietary corpus, sending it out to be embedded or tuned by someone else deserves more scrutiny than it usually gets.
What the stack looks like
Model
Open-weight models in the Llama, Mistral, Qwen and Phi families. On document, extraction and summarization work the gap to frontier models is now small enough that the privacy trade is cheap. On complex reasoning it is not, and we will tell you which side your use case sits on.
Serving
vLLM or TGI for throughput, Ollama where simplicity matters more than concurrency. The choice is driven by your concurrency profile, not by preference.
Retrieval
A vector store alongside your existing systems, with access control enforced at retrieval time. In practice most of the engineering effort lands here rather than on the model.
Hardware
Sized to the model, quantisation and concurrency you actually need. Over-specifying GPUs is the most common way these projects waste money before they have proven anything.
The honest trade-off
Self-hosting buys you data sovereignty, cost predictability and version stability. It costs you capability at the frontier, and it costs you operational overhead that a hosted API does not have. Somebody has to patch it, monitor it, and plan capacity for it.
That trade is clearly worth it when a regulation makes the alternative unavailable, and clearly not worth it for a small team doing general-purpose drafting on non-sensitive material. Most decisions sit between those poles and turn on volume and data sensitivity rather than on principle.
The related question of whether to fine-tune the model or rely on retrieval is separate and usually more consequential. We cover it in RAG versus fine-tuning.
Go deeper
Self-hosted LLM: the technical picture
Model selection, serving stack, quantisation, throughput and what actually determines your hardware bill.
On-premise LLM: the business case
How the cost model compares to API spend, what the crossover depends on, and how to build the case internally.
On-prem deployment service
How Senteras delivers a private LLM deployment, from sizing through handover.
HIPAA-compliant AI
The healthcare case for keeping the model inside the perimeter, and where the BAA question goes away.
Common questions
Is a self-hosted open-weight model good enough?
For document extraction, summarization, classification, retrieval-grounded question answering and drafting, yes, comfortably. For frontier reasoning, agentic multi-step planning and code generation at the highest level, frontier API models still lead. Most enterprise workloads are in the first group, which is why this works.
What hardware do we need?
It depends on model size, quantisation and how many concurrent users you have. A quantised model in the 8B to 14B range serves a small team on a single professional GPU. A 70B-class model at meaningful concurrency needs multiple data-center GPUs. We size against your measured workload rather than a rule of thumb, because the rules of thumb are consistently wrong in both directions.
What does it cost to run?
The honest answer is that hardware amortisation and power dominate, and both are predictable in a way per-token pricing is not. What matters for your decision is the crossover point against your current or projected API spend, which we work out during scoping rather than asserting up front.
Can we start in the cloud and move on-premise later?
Yes, and it is often the right sequence. Build against an open-weight model in your own cloud tenancy first, prove the use case, then move the weights on-premise if the economics or the compliance position justify it. Because the model is the same, the migration is an infrastructure task rather than a rebuild.
Who maintains it?
Either your team, with us handing over, or us on a support arrangement. Self-hosting is a real operational commitment: model updates, monitoring, capacity. Anyone who tells you it is set-and-forget has not run one.
Work out whether self-hosting fits your case
We will tell you where your crossover point sits and whether the compliance argument holds, before anyone talks about hardware.
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