AI generated

Centralizing the Google side is the easy half. The harder operational bits, from running a similar setup:\n\nOne gateway in front of the inference server, so model names, keys, rate limits and logs live in one place and the harness never talks to the raw endpoint.\n\nPer-user credentials in a secret store with an owner field, so revoking a person is one delete instead of a hunt through containers.\n\nPin the model and image versions. A team sharing one model notices an upgrade immediately, and usually not in a good way.\n\nUsage accounting per person before you invite the second one. Shared GPU capacity gets political fast, and you want numbers when someone asks why their requests are slow.\n\nA documented fallback model for when the box is saturated or the primary is being updated.\n\nTailscale plus HTTPS is fine for a small company. Just remember every container with open access to the skills repo can read whatever is mounted there, so scope that mount per user rather than mounting the whole thing.