Every team that starts experimenting with generative AI (gen AI) eventually runs into the same wall: scaling it. Running 1 or 2 models is simple enough. Running dozens, supporting hundreds of users, and keeping GPU costs under control, is something else entirely.Teams often find themselves juggling hardware requests, managing multiple versions of the same model, and trying to deliver performance that actually holds up in production. These are the same kinds of infrastructure and operations challenges we have seen in other workloads, but now applied to AI systems that demand far more resources
