LLMOps
Definition
LLMOps encompasses processes and tools for development, evaluation, deployment, monitoring, and maintenance of LLM-based applications.
It extends MLOps/DevOps by prompt versions, model providers, evals, retrieval, token costs, guardrails, and nondeterministic outputs. The term is a practice family, not a single standard.
Simply explained
Like DevOps and MLOps for applications where language models are a central component.
Why it matters in practice
Relevant as soon as AI prototypes are operated and versioned permanently.
Example from business
A team versions prompt, model, eval set, and RAG configuration and automatically tests changes before deployment.
Limits and pitfalls
- Only version prompts; no evals; accept provider changes unnoticed; forget observability.
Origin & history
LLMOps/GenAIOps developed with productive generative AI applications as a specialization of MLOps and DevOps.
Use in mid-sized companies
- Possible application in SMEs: Transition from pilot to operation with versioning, CI, evals, monitoring, and rollback.
Frequently asked questions
Is LLMOps a product?
No. Does it replace DevOps? No, it complements existing operational practices.
Sources
- LLMOps · Microsoft, LLMOps
- MLOps/GenAIOps · Azure Well-Architected, MLOps/GenAIOps
Sources checked on 10.10.2026.
Editorially reviewed on: