Large language models broke the clean "train it, test it, ship it" model of production ML. The thing being operated is now a system that chains prompts, queries vector databases, and produces output judged on tone and safety — not just accuracy. That's LLMOps. And it's currently landing on top of your existing DevOps and MLOps workflows without a clear owner.
CNCF's Daniel Bryant has a clear take on who should own it — and the argument is sharper than it sounds.
"LLMOps doesn't need its own kingdom. It needs a well-run platform willing to let it in."
What actually changed
LLMOps isn't just MLOps with a new label. The gap is real:
