LLMOps
Also known as: llm ops, llm operations, large language model operations
MLOps brought engineering discipline to machine learning model deployment. LLMOps applies that same discipline to the specific challenges of running LLM-powered products at scale. The challenges are different: traditional ML models have stable, predictable outputs; LLMs produce variable natural language and can fail in unexpected ways that do not throw errors.
LLMOps covers the full production lifecycle. Before deployment: evaluation pipelines (evals) to test model behavior on representative inputs, prompt version control so you can track what changed when behavior shifted. During operation: cost tracking by model and endpoint, latency monitoring to catch regressions, input/output logging for debugging. Over time: drift detection when model updates change behavior, red-teaming to catch new failure modes.
Tools like LangSmith, Langfuse, Helicone, and Arize Phoenix occupy the LLMOps observability space. In 2026, most serious AI products have some form of LLMOps pipeline: at minimum, logging and cost tracking; at the more mature end, full evaluation suites with automatic regression detection on every deployment. The teams that build good LLMOps infrastructure early spend dramatically less time debugging production incidents later.