LLMOps is the set of practices for keeping large language models working well once they are live in a real product. It covers managing prompts, tracking answer quality, watching costs and rolling out model changes safely, so an AI feature stays dependable instead of quietly degrading.
A fair comparison is running a restaurant versus inventing a recipe. Writing the prompt is the recipe. LLMOps is the daily work of keeping the kitchen running: checking the food is consistent, watching the bill for ingredients and swapping a supplier without ruining the dish. It is the language-model cousin of MLOps, focused on the quirks of working with a large language model.
The reason it gets its own name is that these models behave differently from normal code. A vendor can update the model under you, costs can climb with usage, and the same prompt can return a weaker answer next month. LLMOps puts dashboards and checks in place so those shifts are caught early.
A concrete example: you store each prompt version like code, so when answer quality drops you can see exactly what changed and roll back. Automated evals run a fixed set of test questions on every prompt edit, and a cost alert fires the moment a chatty new version doubles the cost per token. That turns vague worry into a number you can act on. It also means a model upgrade stops being a leap of faith: you replay the same test set against the new version and compare scores before any customer sees it.
At TopDevs we set up LLMOps around every AI feature we ship, so quality and spend stay visible long after launch rather than becoming a surprise.
