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MLOps 10 min read

MLOps & Observability for LLM Systems

Traditional MLOps tooling under-serves LLM workloads. Langfuse, LangSmith and prompt-level evals are the new observability stack.

By Saad Alam

MLOps

MLOps & Observability for LLM Systems

Classic MLOps assumed batch training and offline eval. LLM workloads are online, prompt-driven, and dependent on third-party model APIs.

What changes: every prompt is a versioned artifact. Every tool call is an observable span. Every output gets an eval — automated where possible, human-graded where it matters.

Build a token-cost dashboard early. The teams that get burned are the ones who discover token spend two weeks after going viral, not before.

Pair LLM observability (Langfuse / LangSmith) with system observability (Grafana / Prometheus). Both stacks, side by side, is the configuration that holds up under real load.

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