Definition: LLMOps is the set of engineering practices and tools for deploying, evaluating, monitoring and improving applications built on large language models once they are in production.
LLMOps covers the full life cycle of an LLM application: versioning prompts and model choices, running evaluation suites before each release, tracking cost and latency, logging inputs and outputs, catching regressions, and managing retrieval data for RAG systems.
MLOps grew around models a team trains itself, so it focuses on training pipelines, feature stores and model registries. LLMOps often works with hosted foundation models, so more of the effort goes into prompts, retrieval, guardrails, observability and evaluation of open-ended text, where there is rarely one correct answer.
LLM behavior can change when a prompt, model version or data source changes. Without release gates and monitoring, quality problems reach users before anyone notices.
Ryz MLOps engineers and LLM evaluation engineers set up LLMOps pipelines in your cloud, working on your team.
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