<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>platform-engineering on Ritesh Sonawane</title><link>https://riteshsonawane.com/tags/platform-engineering/</link><description>Recent content in platform-engineering on Ritesh Sonawane</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 06 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://riteshsonawane.com/tags/platform-engineering/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM Inference KPIs Every SRE Should Know</title><link>https://riteshsonawane.com/blog/llms-inference-kpis/</link><pubDate>Mon, 06 Jul 2026 00:00:00 +0000</pubDate><guid>https://riteshsonawane.com/blog/llms-inference-kpis/</guid><description>&lt;p&gt;Monitoring LLM inference is not like monitoring a conventional web service. The metrics are different, the failure modes are different, and the relationship between what you measure and what the user experiences is more indirect.&lt;/p&gt;
&lt;p&gt;This guide explains the most useful LLM inference KPIs for SREs, what each metric measures, what causes it to change, and how to use the signals together when production performance degrades.&lt;/p&gt;
&lt;h2 id="the-mental-model-prefill-and-decode"&gt;The mental model: prefill and decode&lt;/h2&gt;
&lt;p&gt;Every LLM inference request goes through two distinct phases. If you do not understand the difference, the metrics will not make sense.&lt;/p&gt;</description></item></channel></rss>