Most teams treat model evaluation, selection, security scanning and hardware selection as manual, one-off steps before deployment, not as part of the automated deployment lifecycle. That approach slows things down, inflates GPU costs, and is the first thing to break when you try to scale from one model to twenty across an enterprise AI factory.