# Enterprise LLMOps

> The operational discipline that turns an LLM demo into a governed production system — versioning prompts, evaluating quality and safety, observing behaviour and token cost, and closing the feedback loop. It extends MLOps to handle non-determinism, retrieval, and inference cost.

*Source: https://www.lazlosoftwaresolution.com/guides/enterprise-llmops*

**LLMOps** is the operational discipline that turns an impressive [LLM](/glossary/large-language-model-llm) demo into a dependable production system. A demo needs a good prompt and a capable model; a production system needs versioned prompts, evaluation you can trust, observability into behaviour and token cost, engineered guardrails, and a governance loop that catches regressions before customers do.

It extends [MLOps](/glossary/mlops) to handle what LLMs add: non-deterministic output, retrieval pipelines ([RAG](/glossary/retrieval-augmented-generation-rag)), safety, and [inference](/glossary/inference) cost that scales with every request. The order that works is deliberate — **evaluate first, then version, observe, guardrail, gate every change, and close the feedback loop** — because optimising what you cannot measure only produces confident regressions.

Explore the cluster below: how to build these systems ([Enterprise AI Development](/guides/enterprise-ai-development)), ground them ([RAG Systems](/guides/rag-systems)), and run agentic ones safely ([AI Agents](/guides/ai-agents)).