"User engagement metrics do not care about the complexity of your AI model. They care about latency. For engineers building high-concurrency applications in e-commerce, fintech or media, the "200ms limit" is a hard ceiling. It is the psychological threshold where interaction feels instantaneous. If a personalized homepage, search result or "Up Next" queue takes longer than 200 milliseconds to load, user abandonment spikes. There is a famous study from Amazon showing that every 100ms of latency cost them 1% in sales."
"The problem is that the business always wants smarter, heavier models. They want large language models (LLMs) to generate summaries, deep neural networks to predict churn and complex reinforcement learning agents to optimize pricing. All of these push latency budgets to the breaking point. As an engineering leader, I often find myself acting as the mediator between data science teams who want to deploy massive parameters and site reliability engineers (SREs) who are watching the p99 latency graphs turn red."
User-facing systems require sub-200ms response times for perceived instant interaction, with increased latency causing abandonment and lost revenue. Businesses push for larger, smarter models—LLMs, deep networks, and RL agents—that exceed tight latency budgets. Ranking every catalog item in real time is infeasible at scale; scoring 100,000 items per request cannot meet the 200ms constraint. To reconcile accuracy with speed, architectures should move away from monolithic request-response flows and adopt a two-pass design that separates fast candidate generation (retrieval) from more expensive ranking or inference, enabling high concurrency without sacrificing personalization.
Read at InfoWorld
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