LLMOps (Large Language Model Operations) is the discipline of deploying, monitoring, and maintaining LLM-based systems in production. It extends MLOps — which handles traditional ML model lifecycle — with the specific challenges of language models: prompt versioning, eval pipelines, output quality monitoring, cost management, and model upgrade paths.
The core LLMOps stack: (1) Prompt management — version control for prompts with the ability to A/B test changes and roll back. (2) Tracing and observability — logging every request-response pair with metadata for debugging and eval (LangSmith, Arize, Weights & Biases, Helicone). (3) Eval pipeline — automated quality checks that run on every prompt change. (4) Cost monitoring — per-model, per-feature token usage tracked and alerted. (5) Model version management — testing against new model versions before promoting to production.
The teams that get LLMOps right ship AI features continuously instead of treating each change as a risky event. The teams that skip it discover, months later, that nobody knows why quality degraded or when a prompt change broke a use case.
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