The requirement says: “Run AI on our own infrastructure.” The demo opens a local chat interface. Then its answer comes from a model hosted elsewhere. Both sides thought they had agreed on the same thing.
Before choosing an on-premise AI platform, name what must run locally. The interface, document storage, search and model processing are separate parts. A local installation can still depend on external services.
Start with a document that cannot leave
Use a harmless stand-in for the kind of document covered by your requirement. Follow its journey from upload to answer. Which service reads it? Where are search results prepared? Where does the model process the request?
If every part must stay inside your network, ask the vendor to demonstrate that exact configuration. If selected external processing is acceptable, document which data may go there. Either way, the requirement becomes something that can be tested.
Then ask who keeps it working
The installation is the beginning of operation. Your team needs to know how updates arrive, how the chosen model is supported and what happens when the service becomes unavailable.
Test recovery with sample data before relying on the system. A platform that your team can install but cannot restore is not ready for important work. Include this operating effort when comparing it with a managed service.
Compare configurations, not badges
A vendor may offer several deployment options under different terms. Ask for a written description of the configuration available to your team, including model connections, support and licence conditions. Do not assume that an enterprise option on a website applies to every plan.
Pharen Hub offers a self-hosted workspace as well as managed cloud. The model and integration choices need to be agreed for your deployment; self-hosting alone is not a promise of an entirely offline system.
Send us your local-processing requirement so we can map the necessary data path.
