Why your LLM app feels slow (even when the API "works")

Latency in large language model (LLM) applications can significantly affect user experience, even if the application is technically functioning correctly. This latency, which is the time between making a request and receiving a response, is harder to pinpoint in LLMs compared to traditional REST APIs due to additional layers of complexity, such as model inference and context assembly. Key metrics to consider for improving LLM user experience include time to first token (TTFT), inter-token latency, and end-to-end latency, with streaming often improving perceived latency. To effectively measure and address latency, it is crucial to focus on percentiles rather than averages and to instrument each stage of the RAG pipeline. Common causes of high latency in LLM apps include autoregressive generation, cold starts, multi-stage pipeline overhead, and missing caching layers. Practical solutions to reduce latency involve semantic caching, prompt caching, efficient vector indexing, and model quantization. Redis offers tools for semantic caching and vector search, which can help optimize LLM infrastructure by reducing network hops and simplifying architecture.