How are you deploying HF models that don’t have inference providers?

One pattern we’ve seen is that “serverless” often works well for experimentation, but once traffic stabilizes, teams start optimizing for predictability rather than pure scale-to-zero behavior. Cold starts and GPU spin-up time can become more operationally expensive than the compute itself, especially for user-facing workloads. A lot of deployments end up hybrid: serverless for spiky jobs, and warm capacity for anything latency-sensitive.