LLMOps is the operational discipline for running large language model systems in production: versioning prompts and models, building evaluation pipelines, monitoring quality and cost, and managing rollouts and rollbacks. It is what separates an LLM feature from an LLM system a team can operate.
The name borrows from MLOps deliberately, and the analogy mostly holds. Both disciplines exist because model behavior changes over time and needs a controlled path to production. The difference is what gets managed. Traditional MLOps versions training data and model weights, then validates against a held-out test set that yields a clear accuracy number. LLMOps versions prompts, model choices, and retrieval sources, and its outputs are non-deterministic. The same input can produce different responses, so a single accuracy metric settles nothing. Quality comes from evaluation suites, replayed conversations, and graded judgments.
The popular framing this page rejects is shipping LLM features without an operational loop. A prompt pasted into a product is not an operated system. Nobody knows which version is live, no eval runs before a change, and no monitor catches the regression a model update just introduced. For customer-facing AI the cost is concrete: an unversioned prompt edit on Tuesday becomes a wrong answer to a real customer on Wednesday, with no trail explaining what changed.
MLOps vs LLMOps at a glance
| Dimension | MLOps | LLMOps |
|---|---|---|
| Artifact managed | Training data and model weights | Prompts, model versions, retrieval sources |
| Quality signal | Accuracy on a held-out test set | Eval suites and graded judgments |
| Failure mode | Drift against a known distribution | Non-deterministic regressions from any change |
Aide, the agentic AI platform for customer experience, builds this loop into deployment. Before a change goes live, the Agent Simulator replays it against real historical conversations, so every rollout is gated on evidence rather than a spot check.