"Where does the model run" is a governance question, not a technical one
For most SaaS AI tools, deployment location is an implementation detail nobody outside engineering thinks about. For an assistant reading an organization's internal documents, it's the first question a legal or security team asks, and the honest answer is that it depends on what's in those documents, not on which option is technically simpler. FanMind supports both on-premise and cloud deployment for exactly this reason: the same product, a genuinely different answer depending on the data.
When on-premise is the only real answer
Personnel files, unpublished research, government correspondence, anything under a data-residency requirement, sending that to a third-party API, even a reputable one, is a governance decision someone has to sign off on and defend later. On-premise means the documents and the model inference both stay inside infrastructure the organization already controls and audits.
When cloud is the pragmatic choice
Public-facing content, course catalogs, published research, general institutional information, doesn't carry that risk, and cloud deployment means faster iteration, no GPU procurement, and no one on staff babysitting a model server. Forcing every deployment to be on-premise "to be safe" just moves the friction from a governance conversation to an infrastructure one, without actually reducing risk for content that was never sensitive.
The mistake both extremes make
Treating "on-premise vs. cloud" as a single yes/no decision for an entire organization, rather than a question answered per document set, either overspends on infrastructure the low-risk content didn't need or takes on liability the sensitive content couldn't afford. The right default is a platform that supports both and lets that decision follow the data, not a religious commitment to one deployment model before anyone's looked at what's actually being indexed.
Frequently asked questions
Should an internal AI assistant run on-premise or in the cloud?
It depends on what is in the documents, not on which option is technically simpler. Sensitive material points to on-premise, while public-facing content can run in the cloud. FanMind supports both for this reason: the same product, with the deployment decision following the data rather than a single organization-wide rule.
When is on-premise AI deployment the only real option?
When the documents include personnel files, unpublished research, government correspondence or anything under a data-residency requirement. Sending that to a third-party API, even a reputable one, is a governance decision someone has to defend later. On-premise keeps both the documents and the model inference inside infrastructure the organization already controls and audits.
Is it safer to put every AI deployment on-premise?
Not for content that was never sensitive. Course catalogs, published research and general institutional information carry little risk, and cloud deployment means faster iteration, no GPU procurement and nobody babysitting a model server. Forcing everything on-premise just moves the friction from a governance conversation to an infrastructure one without reducing real risk.
The same question comes up for support tickets. We looked at it in help desk data residency under the UAE and Saudi data protection laws.
Where the model runs matters less than how it behaves when the documents have no answer; that is the argument in why an internal AI assistant should sometimes say "I don't know".
