On premise AI runs open models on hardware you control, for work whose data stays inside your own network. Legal files under privilege, clinical records, defence work, anything under a contract that names where the data may sit.
It is a genuine engineering choice with measurable trade-offs, and we measure them before you commit.
On Premise AI: When It Earns Its Keep
A contract or regulator names the location. The common case. Where the clause says the data stays on your infrastructure, this is what satisfies it.
The data is somebody else’s secret. Client material held under professional privilege, or under an NDA drafted long before anybody thought about language models.
The volume makes per-token pricing expensive. At high steady throughput, a model you host costs a fraction of the same work billed per call, and the crossover arrives sooner than most people assume.
Where a hosted model in the right region is the better engineering decision, we will say so and build that instead.
What It Actually Involves
A model sized to the hardware you have or can buy. The open models worth running span a wide range, and most useful work runs on far less hardware than the headlines suggest.
Quantisation, with the trade measured. Smaller precision is faster, cheaper and slightly worse. How much worse is a question with an answer, specific to your task.
Serving properly. Batching, concurrency, a queue that degrades sensibly under load, and monitoring that reports saturation before failure rather than after it. Throughput gets tested against your real peak, not against an idle box.
A cost model built from measurement. Hardware, power and engineer time against the hosted bill for the same volume, produced before you commit. That figure is usually what decides the project, so it arrives first rather than last.
What Open Models Are Genuinely Good At
Extraction, classification, summarisation and retrieval over your own documents, which covers most commercial work.
For those tasks the difference against a frontier hosted model is narrow, and the residency requirement wins comfortably. We will show you the measured number for your own task rather than argue the general case, because your corpus is the only one that matters here.
That measurement is what AI evaluation is for, and on premise AI decisions should be made on it rather than on a benchmark run against somebody else’s problem.
What Support Looks Like
Business hours, a named engineer, and a runbook covering the routine work: model updates, monitoring thresholds and capacity planning.
The weights, the serving configuration, the evaluation set and the runbook are yours, so the system can be operated by your own team. Tell us what the data is and what rule applies to it.
Related Services
The hardware and deployment underneath this is cloud and infrastructure, including the private and hybrid cases.
Retrieval over documents that stay inside is RAG development. Whether a smaller model is good enough for your task is AI evaluation, settled before hardware is bought.