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.
Frequently asked questions
- What does an LLMOps stack include?
- Version control for prompts and model configurations, an evaluation pipeline with golden datasets, production monitoring for quality, cost, and latency, and a rollout mechanism with a fast rollback path. Observability ties each output back to the version that produced it.
- Who owns LLMOps?
- In practice it is shared. Engineering owns the pipelines and infrastructure, while the team accountable for the outcome owns quality definitions and review. In customer service that means support operations decides what good looks like, and the loop enforces it.