Private & on-premise AI
For data that genuinely cannot leave your infrastructure: open-weight models running on hardware you control, with an honest account of the trade-offs.
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When this is genuinely required
Less often than it is asked for, and we will say so. Commercial providers now offer regional deployment, contractual commitments not to train on your data, and certifications that satisfy most auditors. For a great many companies that is sufficient and considerably cheaper. Self-hosting is genuinely required where a regulator or a contract prohibits data leaving your infrastructure, where you are processing material under legal privilege or classified handling, where an air-gapped environment is mandated, or where volume is so high that the API bill exceeds the cost of hardware. Those are real cases, and they are the minority.
What deployment involves
Open-weight models such as the Llama, Mistral and Qwen families now run well on a single well-specified GPU server for most business workloads. We size the hardware against your actual concurrency and context requirements rather than to a benchmark, because the two rarely match. That includes the parts people forget: quantisation to fit the model into available memory without losing usable quality, an inference server that handles concurrent requests properly, monitoring, and a plan for updating the model as better open-weight releases arrive, which they do every few months.
The honest trade-offs
Open-weight models have closed much of the gap with the frontier commercial ones, and for classification, extraction, summarisation and most retrieval tasks the difference is often not noticeable. For the hardest reasoning, the frontier models remain ahead. The costs are real: capital expenditure on hardware, someone to operate it, and being responsible for your own capacity when demand spikes. A hybrid usually wins: sensitive work on your own infrastructure, everything else on a commercial API. We build so that routing is a policy decision rather than an architectural one.
What you get
Data never leaves
For regulatory, contractual or classification requirements that a hosted API cannot satisfy.
Told when you do not need it
Regional hosting with no-training commitments satisfies most auditors, and costs considerably less.
Hardware sized on your workload
Against your real concurrency and context length, not against a benchmark that resembles nobody's usage.
Hybrid routing
Sensitive work local, everything else on a commercial API, with routing as a policy rather than a rebuild.
Air-gapped deployment
Fully disconnected environments where that is genuinely mandated, including model update procedures.
A plan for model updates
Better open-weight models arrive every few months. Upgrading should be routine, not a project.
Technologies
Frequently asked questions
Are open-weight models good enough?
For classification, extraction, summarisation and most retrieval tasks, generally yes, and the difference is often not noticeable to users. For the hardest multi-step reasoning, frontier commercial models remain ahead. We evaluate against your actual cases rather than published benchmarks, which correlate poorly with specific business tasks.
What hardware do we need?
For most business workloads, a single server with one or two current-generation GPUs handles a mid-sized company comfortably. The variables are concurrent users and how much context each request carries. We size it against your real numbers before anyone buys anything.
Is it cheaper than an API?
Only at high volume. There is real capital expenditure and real operational effort, against a per-token cost that keeps falling. Below a certain throughput the API wins clearly on cost, and if cost is your only driver we will tell you that self-hosting is the wrong answer.
Find out whether you actually need to self-host
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Location
Serbia