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: prompt versioning, an AI agent’s multi step tool calls that need tracing, model routing for cost and capability, and durable execution when a workflow must recover after interruption.
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. An AI gateway can apply the shared routing, rate limits, and policy checks before model requests leave the application. 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. Running systems on that stack day to day is the work behind our standing as official LangChain Ambassadors and Experts.
How do we run LLMOps at Soba Labs?
Our own automation runs on the same discipline. Production prompts are pinned, immutable revisions with a rollback target, the practice we describe in prompt versioning in production, and our own agents’ instructions live in git and change by reviewed pull request. Every scheduled content run must pass a deterministic audit gate before anything ships, and our pull request judge runs the free deterministic checks first so a change that fails them never pays for a model call. Where a model scores another model’s output, the judge works from a written rubric, never its own taste, the setup covered in our LLM-as-a-judge guide. Each unattended run carries explicit caps on searches and spend, so a misbehaving agent can only consume what its one run was given.