<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Multi-Model Serving on StackSimplify | DevOps &amp; Cloud Education by Kalyan Reddy</title><link>https://stacksimplify.com/tags/multi-model-serving/</link><description>Recent content in Multi-Model Serving on StackSimplify | DevOps &amp; Cloud Education by Kalyan Reddy</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Wed, 22 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://stacksimplify.com/tags/multi-model-serving/index.xml" rel="self" type="application/rss+xml"/><item><title>Multi-Model Serving on Kubernetes: 50 Models, One Cluster</title><link>https://stacksimplify.com/blog/multi-model-serving/</link><pubDate>Wed, 22 Apr 2026 00:00:00 +0000</pubDate><guid>https://stacksimplify.com/blog/multi-model-serving/</guid><description>50 models. 10 active. 40 at zero. One cluster.
That is the reality of a mature ML platform. Not one model per team. Not one namespace per endpoint. Dozens of models sharing infrastructure, scaling independently, and costing almost nothing when idle.
Most teams never get here. They get stuck at the single-model trap.
The Single-Model Trap Team A deploys their fraud model. Gets its own namespace, its own Istio gateway, its own monitoring stack.</description></item></channel></rss>