AI Answer Library
Short answer
Yes — this is exactly what self-hosting exists for. Download open weights into your network, run inference on local GPUs, and no part of the process requires internet access or phones anything home. The catch is that "stays inside" has to hold for the entire chain: the vector store, logs, monitoring and alerting, even the fonts and CDN assets your front end loads. Localising the model while everything around it still talks to the public internet defeats the purpose.
In an on-premise build, the model is rarely what leaks — the components around it that default to being online are. The table below breaks a typical RAG deployment into segments and states the default behaviour alongside the offline approach. A practical acceptance test: tighten outbound firewall rules to an allowlist, then run a full business flow and see what gets blocked.
| Segment of the chain | Leaves the network by default? | How to keep it internal | Most commonly missed |
|---|---|---|---|
| Model inference | No — local weights on a local serving stack need no connection | Download weights offline and distribute from an internal registry | Anonymous usage stats and version checks in the serving framework left enabled |
| Embedding model and vector store | Depends — managed vector services do leave the network | Serve the embedding model locally and pick a self-hostable vector database | The model is local while the vector store is still a hosted instance |
| Logs, monitoring and crash reporting | Yes — most observability SDKs report to the vendor by default | Switch to a self-hosted stack and disable third-party reporting | Stack traces carrying the raw user question shipped to a public endpoint |
| Dependencies and container images | Yes — builds and upgrades pull from the internet by default | Run an internal registry and ferry images and packages in offline | Normally offline, then one urgent upgrade reopens external access |
| Front-end static assets | Yes — CDN-hosted fonts and scripts carry referrer information | Bundle fonts, icons and scripts locally | Discovered only when an internal page fails to load a public font |
| Model and security updates | Yes — the standing pain point of any offline environment | Batch transfers on a schedule, with a rollback path | No update cadence, so after a year the stack is full of known CVEs |
The phrase means very different things at different companies, and the cost gap between those meanings is several-fold. The strictest tier is physical isolation: no external network at all, every transfer approved and carried on media — typical for classified environments and some industrial control settings. The middle tier is logical isolation: connectivity exists but is governed, egress runs through an allowlist, and sensitive data may not leave a defined zone. The loosest tier is really "no third-party model vendor": data may move within your own cloud account, it just must not reach a public model API. Deployment shape, cost and operational complexity differ completely across the three. The common waste is designing for the strictest tier when the actual requirement is the third. Writing that single sentence down before signing matters more than any later technical choice.
First, permissions. The model has no notion of who should not see what, and pouring every company document into one vector store effectively grants everyone full-corpus search. Filter by user identity at the retrieval layer; never encode permission logic in a prompt. Second, traceability. Who asked what and when, which documents were cited and what conclusion was produced all need to be queryable, or incidents cannot be reconstructed and audits will fail. Third, the capability ceiling. Internally you can only run what fits in your VRAM, and on harder reasoning tasks an open model will trail the latest hosted flagship. That is the real price of self-hosting, and it should be stated plainly during selection rather than discovered after launch as "it is not as smart as we expected".
Where this applies
People also ask
Self-hosted LLM or public API — how do I choose?
How much does it cost for a company to self-host a large language model?
For running LLMs, is it cheaper to buy GPUs or rent them?
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