Nothing
leaves.
A one-sentence data statement that a reviewer can verify. For material that is confidential by default, this is frequently the whole reason the deployment is permitted.
Your hardware.
Your version.
Your problem.
On-premise AI gives you a data boundary, a frozen model version and predictable latency. It also gives you everything that comes with running infrastructure.
READ THE DEFINITION ↓BOUNDARY · VERSION · OPERATIONS
01 / DEFINITION
The short answer first.What is on-premise AI?
On-premise AI is the deployment of AI models on hardware an organisation owns and operates, inside its own network. Prompts, documents and outputs never leave that environment, and the model version is fixed until the organisation chooses to change it.
On-premise is a subset of private AI. Private inference can also run in a dedicated cloud tenancy; on-premise specifically means physical infrastructure under the organisation's control, which is the strongest available statement about where data sits and the largest operational commitment.
02 / FOUR REAL ADVANTAGES
What hosted inference cannot offer.Nothing
It changes
when you say.
A pinned artefact means a result obtained today reproduces in two years. Hosted endpoints can change behaviour with no notice and no changelog you control.
No round trip,
no shared queue.
Local inference is predictable under your own load rather than under someone else's, which matters more than raw speed for interactive use.
A fourth advantage is cost shape at sustained volume. Per-token pricing scales linearly with use; owned hardware has a fixed capacity and a marginal cost close to zero once purchased. For a supervision model running over every document an organisation produces, that difference compounds — and it is a different argument from the privacy one, so it should be made separately.
03 / WHAT YOU TAKE ON
The honest side of the ledger.| Responsibility | What it actually involves |
|---|---|
| Capacity planning | Sizing for peak concurrency and context length, not average load |
| Hardware lifecycle | Procurement lead times, failure replacement, eventual refresh |
| Driver and firmware compatibility | A real source of outages; version matrices matter |
| Patching in an isolated network | A defined import route for updates, with provenance |
| Monitoring and on-call | Including cache pressure, not just utilisation and uptime |
| Model upgrades | Re-running fine-tuning and the full evaluation for each new base |
The last row is the one most often omitted from a business case. A private model is a maintained artefact: capturing an improvement from a newer base family means repeating the training and the held-out evaluation. Organisations that budget for the second version treat this as routine; those that do not end up with a frozen system nobody is willing to change. See private AI infrastructure.
04 / WHEN NOT TO
Cases where hosted inference is the better answer.The task needs frontier reasoning.
Some work genuinely requires the largest available models. Where the data permits transmission, hosted inference is the right choice for it.
Volume is low and sporadic.
Owned capacity sitting idle is worse value than per-token pricing. The economics favour hosted inference well below steady utilisation.
There is no infrastructure function.
An organisation with no capacity to run hardware will run it badly, and a badly run private deployment is less secure than a well-configured hosted one.
The third point deserves emphasis because it cuts against the usual assumption. Private is not automatically more secure. An on-premise server that is unpatched, unmonitored and logging full prompts to a widely readable store is a worse outcome than a properly configured hosted deployment. Private AI is a capability commitment as much as an architectural choice — which is why Privilege AI treats operational constraints as part of the design rather than as a deployment detail.
05 / QUESTIONS
Asked when weighing on-premise against hosted.Is on-premise AI more secure than hosted AI?
+
It removes third-party exposure and gives you version control. Whether the result is more secure depends on how well you operate it: an unpatched, unmonitored local deployment with permissive logging is worse than a well-configured hosted one.
What is the minimum hardware for on-premise AI?
+
For a 7B to 13B model at moderate concurrency, a single modern GPU with sufficient memory for weights plus key-value cache at your intended context length. The arithmetic is in private AI infrastructure, and the cache term is the one that decides it.
Can on-premise AI be fully air-gapped?
+
Yes, and it is the strongest form of the boundary: with no outbound route, exfiltration is structurally impossible rather than controlled. Updates and monitoring then require a deliberate import process. MKC2 runs entirely offline.
How does on-premise AI handle model updates?
+
Through a controlled import with recorded provenance and checksums, followed by re-evaluation before the new version is promoted. The convenient direct-download path is exactly the outbound route the boundary exists to remove.
Is on-premise AI cheaper?
+
At sustained high volume, often. At low or sporadic volume, generally not. Where the data cannot leave, the comparison is not against hosted inference but against not doing the work.
Run it
in your own building.
Privilege AI works on local inference, application integration and on-premise infrastructure, with security boundaries considered throughout.