LLMOps is the set of practices and tooling for running a language model or agent system reliably in production: versioning prompts and models, gating releases with an evaluation harness, tracing what actually happened with observability, and controlling the cost of every inference call.
How does LLMOps differ from traditional MLOps?
Classic MLOps versions a trained model and monitors its accuracy after deployment. LLMOps inherits that discipline and adds what a language model actually needs: prompts that change behavior as much as code and need the same versioning, an AI agent’s multi step tool calls that need tracing rather than a single input output pair, and inference cost that scales with every call instead of a one time training bill.
What does an LLMOps setup actually look like day to day?
An AI evaluation harness gates every release before it reaches users, LLM observability traces what real traffic does after, and cost and latency get monitored per call rather than checked once at launch. LangSmith covers most of this from one platform, which is why it shows up as often on the ops side of a system as on the build side.
Frequently asked questions
Is LLMOps just MLOps with a new name?
It builds on MLOps but adds problems traditional machine learning did not have: prompts that need versioning like code, an agent's multi step tool calling that needs tracing, and per call inference cost that scales directly with usage instead of a one time training bill.
What does an LLMOps setup actually include?
Prompt and model versioning, an AI evaluation harness that gates every release, LLM observability that traces production runs, and cost and latency monitoring on inference, usually tied together through a platform like LangSmith rather than assembled from disconnected tools.