Deployment & economics
It shapes whether AI feels helpful or frustrating. Some quality gains come from slower, larger models, so latency is a real trade-off against accuracy and cost.
In plain terms
Latency is the delay between a user's request and the AI system's response.
Latency is how long the person waits. For AI, that wait includes sending the document, the model thinking, any tool it calls, and the answer streaming back. A brilliant answer that arrives after the customer has hung up is a failed feature.
Budget the wait the way you budget the money. A chat reply that takes two seconds feels fine. A ten-second pause feels broken. Shorter inputs, a faster model for simple jobs, and fewer tool calls in the path all help. Tell the user what is happening if the wait is unavoidable. Silence is what they abandon.
Think of it this way: Latency in AI is the gap between asking a question and getting an answer. A one-second wait feels like speed. A ten-second wait feels like the system is broken, even if the answer that arrives is excellent.
A sales team's AI call summary tool takes 45 seconds after a call ends. Adoption is low. Optimizing the prompt and switching to a faster model brings it to 8 seconds. Adoption doubles.
A call-center tool summarized the conversation after hang-up, which was fine at eight seconds. Someone moved that summary onto the live call so the agent could read it mid-conversation. Agents talked over the customer while they waited. The summary moved back to after the call. The wait had not changed. The moment had.
Measure latency for every user-facing AI feature from the first prototype. Set an acceptable maximum threshold before choosing a model, not after users are already complaining. Chasing minimum latency at the expense of accuracy is the wrong optimization for many back-office tasks. A nightly batch job that takes 5 minutes is fine. A real-time assistant that takes 15 seconds is not.
RaftLabs prices the running cost before the build, so a feature people like does not become a loss. You get a number for a busy month, not only a demo. The related work on our side is MLOps.
This sits with the other deployment & economics terms on the glossary. What you pay to run AI, and the choices that change the bill. Worth reading next: API, Open vs Closed Models, and Inference Cost.
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