What is LLMOps?

LLMOps is the operational discipline of systems built on language models: everything needed to take assistants, RAG and agents to production and keep them under control. It inherits the MLOps toolkit, but the problems change: here you usually do not train the model yourself, and the output is not a number to compare with reality but text to be judged. Teams that already run MLOps on traditional predictive models quickly find the tools do not transfer wholesale: a pipeline built to monitor drift in a scoring model does not catch a hallucination in a text answer, and an accuracy dashboard says nothing about an agent that has stopped citing the right sources. That is why LLMOps emerged as its own discipline, with its own tools and metrics, while remaining a direct descendant of MLOps: the same need for control, applied to a radically different kind of output to evaluate, generated text instead of a predicted number.

What changes compared to MLOps

Three practical differences. First, evaluation: there is no crisp accuracy metric, so you need test suites of questions and expected answers, sampled human judgment and automated checks for hallucinations and tone. Second, observability: every answer comes from a chain (prompt, retrieved documents, tool calls) and when something goes wrong you must be able to reconstruct the path, not just read a log. Third, cost: you pay per token, and an innocent prompt change or an agent stuck in a loop can multiply spend overnight.

Why it matters for your business

It is the difference between a demo that impresses and a system you can trust. Before putting an assistant in front of customers or employees you need answers to concrete questions: how do we measure whether it answers well? Who notices if it starts failing after a model update? What does it cost per request? LLMOps is the set of practices that turns those questions from worries into metrics.