Comparison
LLM Observability vs Online Evaluation
LLM Observability and Online Evaluation are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for LLM Observability
From day one of any production LLM application. The cost of bolting it on later vastly exceeds wiring it up at the start.
A support bot logs every (user message, retrieved docs, prompt, response, faithfulness score) tuple to Arize Phoenix; engineers replay bad sessions there.
When you would reach for Online Evaluation
After offline eval is solid and you have meaningful production volume. Stretch your eval coverage from a fixed set to a live one.
Phoenix running a faithfulness eval on 5% of production RAG traces, dashboard charts the rolling 7-day mean.
Frequently asked
What is the difference between LLM Observability and Online Evaluation?
LLM Observability: LLM observability is the practice of capturing, analyzing, and acting on every LLM call in a production system — inputs, outputs, latencies, costs, errors, and quality scores — so you can debug regressions and improve quality over time. Online Evaluation: Online evaluation runs scoring functions over live production traffic — usually a sample of recent traces — to monitor quality continuously instead of relying solely on a fixed offline dataset.
When should I use LLM Observability vs Online Evaluation?
From day one of any production LLM application. The cost of bolting it on later vastly exceeds wiring it up at the start. After offline eval is solid and you have meaningful production volume. Stretch your eval coverage from a fixed set to a live one.
Are LLM Observability and Online Evaluation the same thing?
No. LLM Observability is infrastructure; Online Evaluation is evaluation. They are related but address different parts of the AI stack.