Comparison
Drift Detection vs Tracing
Drift Detection and Tracing 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 Tracing
Tracing comes up when the question is fundamentally about infrastructure.
A trace showing: user_query → retrieve(top_k=5) → rerank → completion(gpt-4o) with each step's tokens, latency, and content visible.
Frequently asked
What is the difference between Drift Detection and Tracing?
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. Tracing: Tracing captures the full causal tree of an LLM request — the user input, retrieval calls, tool calls, intermediate prompts, and the final response — as a hierarchy of timed spans you can replay and inspect.
When should I use Drift Detection vs Tracing?
Once you have stable traffic and a baseline; drift detection is most valuable in mature production systems. Tracing applies when you are focused on infrastructure.
Are Drift Detection and Tracing the same thing?
No. Drift Detection is infrastructure; Tracing is infrastructure. They are related but address different parts of the AI stack.