LLMOps

LLMOps is the subset of MLOps focused on the specific operational concerns of large language models: prompt versioning, evaluation, cost control, and output observability.

What is LLMOps?

LLMOps adds practices that classical MLOps does not cover well. Prompts are first-class artefacts. Evaluation uses LLM-as-judge alongside golden datasets. Cost is metered by token, not by request. Latency is dominated by streaming and context size. Outputs are non-deterministic and need sampling and content checks. Tools include prompt registries, eval harnesses, trace viewers, and guardrail engines.

How does LLMOps apply to enterprise AI?

Any enterprise with a generative AI feature in production needs LLMOps. Without it, the team cannot debug why a prompt regressed, why costs spiked, or why a customer received a wrong answer.