AI Search Metric
Latency
Also known as: response latency, time to first token, inference latency
Latency is the delay between a request and the response it produces. In AI search it is usually split into time to first token, which is how long the user waits before words appear, and total completion time for the whole answer. High latency reduces the amount of retrieval and reasoning a system can afford, and it makes users abandon answers before they finish.
What it is
Latency covers every delay in the chain: network time, queueing, model inference, tool calls and any live web fetches. Time to first token measures perceived responsiveness, while tokens per second and total time measure how quickly the full answer arrives. In retrieval heavy systems, fetching and reading source pages often contributes more delay than the model itself.
Why it matters
Slow answers get abandoned, and slow sources get dropped. When an assistant fetches pages at query time, it usually works to a timeout, so a page that responds slowly may be skipped in favour of a faster one, which costs you a citation. Latency also shapes product design, because a tight budget forces fewer retrieved documents and shallower reasoning.
How it works
Practitioners instrument each stage separately, then look at the distribution rather than the average, because tail latency drives complaints. They reduce it by streaming output, caching frequent requests, retrieving fewer but better documents, using smaller models for simple steps and running work in parallel. On the publishing side, they cut server response time, keep content in the initial HTML and avoid redirect chains so crawlers and live fetchers can read pages quickly.
When it applies
It applies to any live AI feature you operate, and to your own site whenever assistants and crawlers fetch your pages at query time.
Examples
- A support assistant streams its first sentence within a second while continuing to retrieve documents in the background.
- A publisher cuts time to first byte on article pages so live fetching agents do not time out before reading the content.
- An agent workflow runs three retrieval calls in parallel rather than in sequence, halving total answer time.
How it is measured
- Time to first token, at median and 95th percentile
- Total answer completion time and tokens per second
- Server time to first byte and full render time for your key pages
- Abandonment rate, measured as sessions where the user leaves before the answer finishes
Related terms in AI Search Metric
- AI visibilityAI visibility is how often and how prominently a brand, product or website appears in answers generated by AI assistants and AI search features. It is measured by mentions, citations and the quality of the description, not by keyword rankings. It is the AI search equivalent of share of voice on a results page.
- AI-referred trafficSessions referred from ChatGPT, Claude, Gemini, Perplexity, Copilot, and other LLM surfaces. Often under-counted in standard analytics because referrers are stripped or labelled as direct.
- Answer attribution rateThe proportion of AI answers that explicitly link back to source pages versus those that summarise without attribution. Attribution rate varies materially by platform and query type.
- Bounce-back rate (AI surface)The rate at which users return to the SERP or refine the prompt after seeing an AI Overview. Google's internal signal that an Overview failed, high bounce-back is associated with Overviews being silently removed for that query cluster.
- Brand mention rateThe frequency at which an LLM mentions a brand by name in answers across a prompt set, with or without citation. Brand mentions without citation are an early indicator of model-internalised brand awareness.
- Citation depthHow early in an AI-generated answer a source is cited. Earlier citations correlate with higher click-through from AI surfaces and stronger user trust signals.