Open weights don't mean you control your data

Everytime a big openweight model releases, everyone calls it a win for control and then eventually end up using hosted api, think about kimi for instance, 2.8T params, here moonshot even recommends 64+ accelerators to serve it and nobody outside a hyperscaler or a funded lab is running that locally as far I'm concerned So to be in practice open means you read the weights not that you control where inference happens or where your data goes because most people running the open model are sending their prompts to someone else's endpoint often a foreign one, the same as any closed api although its fine for hobby stuff However if you're building agents on real company data the open vs closed debate kinda misses the point. What actually patterns is whether your data leaves your environment when the model runs. For anything sensitive that's the whole game. I am eager to learn from other people on enterprise or company level on what you take on this for regulated or private data or are you running smaller models you're eligible to host?