<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Inference on StackSimplify | DevOps &amp; Cloud Education by Kalyan Reddy</title><link>https://stacksimplify.com/tags/inference/</link><description>Recent content in Inference on StackSimplify | DevOps &amp; Cloud Education by Kalyan Reddy</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Wed, 15 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://stacksimplify.com/tags/inference/index.xml" rel="self" type="application/rss+xml"/><item><title>Batch vs Real-Time ML Inference: 90% of Predictions Can Be Batch</title><link>https://stacksimplify.com/blog/batch-vs-realtime-inference/</link><pubDate>Wed, 15 Apr 2026 00:00:00 +0000</pubDate><guid>https://stacksimplify.com/blog/batch-vs-realtime-inference/</guid><description>Your model runs in real-time. 90% of your predictions do not need to.
That is the most expensive assumption in ML infrastructure. A recommendation engine that refreshes daily does not need always-on pods. A credit risk score computed once at application time does not need a replica running at 3 AM.
Most teams default to real-time because that is how their first model shipped. Every model after inherits the same pattern.</description></item></channel></rss>