What We Build

Private LLM Deployment & Self-Hosted AI Models

Private LLM deployment is the practice of running open-weight language models on infrastructure the organisation controls — on-premise or in its own cloud tenancy — so that prompts, documents and outputs never leave its security boundary.

Two separate pressures push organisations towards running their own models, and they arrive on different timelines. The first is data sovereignty: some corpora simply cannot be sent to a third-party endpoint, whatever the contract says, because a regulator, a customer agreement or an internal policy forbids it. That constraint is present from day one and is not negotiable.

The second is economics, and it arrives with scale. Per-token pricing is excellent while volumes are low and becomes the dominant line item once a system is genuinely used across an organisation. At high, steady volume the arithmetic of a dedicated model on owned or reserved hardware changes substantially.

Neither pressure means self-hosting is automatically right. It introduces operational responsibility that an API does not, and below a certain volume it costs more, not less. Part of our job is telling you when the answer is to stay on a commercial endpoint.

What we build

A private deployment is a system, not a model download. The serving layer, the evaluation harness and the update path matter as much as the weights.

Model selection benchmarked on your tasks and your data, rather than on public leaderboards
Fine-tuning and preference tuning where a task genuinely benefits from it, and an honest recommendation against it where prompting and retrieval would do the job
Optimised serving with quantisation, batching and KV-cache management, sized to your real concurrency rather than a theoretical peak
Deployment into your VPC, your data centre, or an air-gapped environment where required
Autoscaling and failover, including graceful degradation to a smaller model under load
An evaluation harness that travels with the model, so a version upgrade is a measured change rather than a leap of faith

The cost question, honestly

Savings from self-hosting come from replacing per-token pricing with amortised compute, and they only materialise at sustained volume with reasonable utilisation. A model sitting idle on reserved GPUs is more expensive than an API, not less. The variables that decide the answer are your token volume, how spiky it is, the model size the task actually needs, and whether you can buy reserved capacity.

We model this properly during discovery, with your numbers, and present the crossover point. Sometimes the conclusion is a hybrid — a small self-hosted model handling the high-volume routine work, with a frontier API reserved for the hard minority of queries. That is frequently the best answer and it is rarely the one a vendor selling infrastructure will propose.

Staying current

The main objection to self-hosting is that open-weight models move fast and a deployed model risks becoming dated. The answer is to treat the model as a replaceable component: a stable interface in front of it, an evaluation suite that can score a candidate replacement against the incumbent on your actual tasks, and a rollback path. Upgrading then becomes a routine, evidenced operation.

02Where It Applies

Typical applications of Private LLM Deployment.

Workloads under data residency or sovereignty obligations

Document processing where contents cannot leave the organisation

High-volume classification and extraction where per-token pricing dominates cost

Domain-specific models tuned on internal terminology and document conventions

Air-gapped environments with no external network path

Latency-sensitive applications needing inference close to the data

03Questions

Private LLM Deployment, answered.

The questions that come up on almost every first call about this capability.

Are open-weight models good enough for serious work?

For most enterprise tasks, yes — and the gap has narrowed considerably. Extraction, classification, summarisation and retrieval-grounded answering are well within reach of current open-weight models, particularly when the task is narrow and the model is tuned for it. Frontier models retain an edge on open-ended reasoning over long, complex context. The right way to settle it is benchmarking both on your tasks, which is a discovery activity rather than a matter of opinion.

What infrastructure does this actually require?

Less than most people assume. A quantised mid-sized model serving a department's workload runs on a single modern GPU; a large model serving an entire organisation at high concurrency is a different proposition. Sizing falls out of your concurrency, latency target and chosen model, and it is established during architecture. Deployment into your existing cloud tenancy is the common case, which means no hardware procurement at all.

Who operates it once it is live?

Either party, agreed up front. Some clients take operations in-house with their platform team and we hand over runbooks, dashboards and the evaluation harness. Others keep us on for monitoring, tuning and model upgrades. What we do not do is deploy a system and leave without the documentation and instrumentation needed to run it.

Does fine-tuning risk the model memorising and leaking sensitive data?

It is a real consideration and it is handled at the data preparation stage — training sets are constructed deliberately, sensitive fields are excluded or masked, and the resulting model is tested for memorisation before deployment. It is also a good reason not to fine-tune reflexively: for most enterprise knowledge tasks, retrieval grounds the model in your data at answer time without ever writing that data into the weights.

Is the 60-80% cost reduction realistic?

It is achievable at sustained high volume with good utilisation, and it is not a universal promise. The figure comes from replacing per-token frontier pricing with amortised compute on a right-sized open-weight model; at low or highly spiky volume the saving shrinks or reverses. We model your specific case during discovery and give you the crossover point rather than the headline.

04Sectors