Inference latency: what it measures & why it varies

Inference latency measures the time an individual AI request takes from submission to output, distinct from throughput, and for large language models it includes time to first token, per-token generation time, total response time, and the duration of multi-step agent workflows. These metrics vary because models first process the prompt during a prefill phase and then generate output token by token, while production delays also arise from queuing, guardrails, cold starts, network overhead, and retrieval-augmented generation pipelines. The most relevant measure depends on the workload: chat applications prioritize fast first-token delivery, agents require low end-to-end workflow latency because delays accumulate across chained calls, and batch processing emphasizes cost and completion deadlines. The post argues that teams should monitor tail latency rather than averages, optimize surrounding systems such as retrieval and state management, and use semantic caching where appropriate to avoid repeated model calls, while balancing cache accuracy and freshness.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.