Private LLM deployment: when it's worth it, when it's a burden
Self-hosting answers the 'data must not leave' concern, but the cost, ops, and capability trade-offs need settling at kickoff.
For data-sensitive or regulated sectors, private deployment (on-prem or a dedicated cloud) is often seen as the answer to keeping data in-house. But it isn't free: compute, operations, model updates, and a capability gap are all real costs.
The key judgments are whether your data genuinely cannot use public cloud, whether the target market mandates localization, and whether a private model meets your capability bar. Many cases can be solved with a public API plus contractual and technical controls.
The pragmatic approach is to tier by data sensitivity and regulation: private for the highly sensitive, managed services with controls for the rest, and reserve resources to keep private models updated and evaluated.