On-premise LLM deployment
On-premise LLM deployment means running the model, the retrieval index and the orchestration layer inside infrastructure the organization owns and controls, so no prompt, document or embedding leaves the perimeter. It is chosen when data residency, regulatory examination or contractual confidentiality make an external inference endpoint unacceptable.
What actually has to run inside the perimeter
A deployment that runs inference locally but calls a hosted embedding API has not solved the residency problem. Every component that sees raw content has to be inside the boundary.
- Inference: the model weights and the serving runtime.
- Embedding and index: vector storage and the embedding model, which leak content just as readily as prompts do.
- Orchestration: routing, tool calls and policy evaluation.
- Logging and audit: the record of who asked what, and what was returned.
When on-premise is the right call
- The data is subject to residency rules that a cloud region cannot satisfy contractually.
- An examiner or auditor requires demonstrable control over where inference occurs.
- The content is confidential under client or government agreement, not merely sensitive.
- The organization already operates a controlled data centre and the marginal operational cost is low.
When it is not
On-premise carries real cost: hardware, capacity planning, model refresh cycles and the operational staff to run them. If the workload is not regulated and the data is not confidential, a private cloud deployment is usually the better economic answer. Being honest about this is part of the evaluation.
WisdomTwin.ai deploys the Institutional Judgment Layer on infrastructure the customer controls, with policy checks, required human approval and full audit lineage on every answer.
Frequently asked questions
What is on-premise LLM deployment?
Running the language model, embedding model, vector index and orchestration inside infrastructure the organization owns, so no prompt or document leaves the network perimeter.
Is on-premise LLM deployment more secure than cloud?
It removes a class of risk related to data leaving the perimeter and third-party processing. It does not remove access control, prompt injection or model output risk, which still require governance.
What hardware does an on-premise LLM need?
It depends on model size, concurrency and latency targets. Requirements are sized during architecture review rather than fixed in advance.
Can an on-premise LLM be air gapped?
Yes. An air-gapped deployment removes external network reachability entirely, at the cost of manual update and model refresh procedures.