Comparison
Drift Detection vs LLM Observability
Drift Detection and LLM Observability are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Drift Detection
Once you have stable traffic and a baseline; drift detection is most valuable in mature production systems.
A support bot's P50 latency jumps 30%: a drift alert fires on the input distribution, revealing users started pasting in entire emails instead of short queries.
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.
Frequently asked
What is the difference between Drift Detection and LLM Observability?
Drift Detection: Drift detection watches for changes in the statistical distribution of inputs, outputs, or quality scores over time — so you can catch a model degrading in production before users complain. 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.
When should I use Drift Detection vs LLM Observability?
Once you have stable traffic and a baseline; drift detection is most valuable in mature production systems. From day one of any production LLM application. The cost of bolting it on later vastly exceeds wiring it up at the start.
Are Drift Detection and LLM Observability the same thing?
No. Drift Detection is infrastructure; LLM Observability is infrastructure. They are related but address different parts of the AI stack.