<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Performance on Taubyte Blog</title><link>/blog/tags/performance/</link><description>Recent content in Performance on Taubyte Blog</description><image><title>Taubyte Blog</title><url>/blog/opengraph.jpg</url><link>/blog/opengraph.jpg</link></image><generator>Hugo -- 0.146.0</generator><language>en-us</language><lastBuildDate>Sat, 21 Feb 2026 16:00:00 +0000</lastBuildDate><atom:link href="/blog/tags/performance/index.xml" rel="self" type="application/rss+xml"/><item><title>Docker: When Containers Add Overhead Instead of Value</title><link>/blog/posts/docker-performance-fresser/</link><pubDate>Sat, 21 Feb 2026 16:00:00 +0000</pubDate><guid>/blog/posts/docker-performance-fresser/</guid><description>&lt;p>Docker is everywhere. Every application runs in containers. Every deployment uses Docker. Every team containerizes everything. But here&amp;rsquo;s the thing: Docker adds a runtime layer between your application and the OS. That layer has overhead. That overhead costs money.&lt;/p>
&lt;p>Containers aren&amp;rsquo;t free. They consume CPU. They consume memory. They consume disk space. They add complexity. They add operational burden.&lt;/p>
&lt;p>Most applications don&amp;rsquo;t need containers. Most applications can run directly on the OS. Most applications don&amp;rsquo;t need the isolation. Most applications don&amp;rsquo;t need the portability.&lt;/p></description></item><item><title>Service Mesh: The Sidecar Tax That Eats Your Memory</title><link>/blog/posts/service-mesh-performance-fresser/</link><pubDate>Fri, 20 Feb 2026 16:00:00 +0000</pubDate><guid>/blog/posts/service-mesh-performance-fresser/</guid><description>&lt;p>Service meshes are everywhere. Istio. Linkerd. Consul Connect. Every microservices architecture needs one. Or so the marketing says.&lt;/p>
&lt;p>But here&amp;rsquo;s the thing: service meshes add sidecar proxies to every pod. Envoy, Istio&amp;rsquo;s sidecar, uses 50-200 MB RAM per pod. Linkerd-proxy uses 20-100 MB. Multiply by hundreds of pods. That&amp;rsquo;s gigabytes of memory just for service mesh overhead.&lt;/p>
&lt;p>All of this before your applications run. All of this just for inter-service communication. All of this overhead.&lt;/p></description></item><item><title>etcd: The Consensus Tax You're Probably Paying For Nothing</title><link>/blog/posts/etcd-performance-fresser/</link><pubDate>Thu, 19 Feb 2026 16:00:00 +0000</pubDate><guid>/blog/posts/etcd-performance-fresser/</guid><description>&lt;p>etcd sits at the heart of Kubernetes. Before your applications run, etcd is storing cluster state, coordinating elections, and replicating data. It consumes 2-8 GB RAM per node. It requires 3-5 nodes for high availability. That&amp;rsquo;s 6-40 GB RAM just for cluster coordination.&lt;/p>
&lt;p>Most teams don&amp;rsquo;t need distributed consensus. Most teams don&amp;rsquo;t need high availability at the cluster level. Most teams are running small clusters that would work fine with a single node and backups.&lt;/p></description></item><item><title>Cloud Hyperscalers: The $10M Lesson from 37signals</title><link>/blog/posts/cloud-hyperscalers-performance-fresser/</link><pubDate>Wed, 18 Feb 2026 16:00:00 +0000</pubDate><guid>/blog/posts/cloud-hyperscalers-performance-fresser/</guid><description>&lt;p>Cloud-first is the default. Every startup uses AWS. Every enterprise migrates to Azure. Every consultant recommends GCP. But here&amp;rsquo;s the thing: 37signals went from $3.2M per year to $1.3M per year after leaving the cloud. Over $10M saved in five years.&lt;/p>
&lt;p>GEICO spent a decade migrating to the cloud. Result: 2.5x higher costs. They&amp;rsquo;re not alone.&lt;/p>
&lt;p>The cloud isn&amp;rsquo;t always cheaper. It&amp;rsquo;s often more expensive. Especially when you factor in hidden costs: egress fees, managed services, vendor lock-in.&lt;/p></description></item><item><title>Microservices: What Amazon Prime Video Learned the Hard Way</title><link>/blog/posts/microservices-performance-fresser/</link><pubDate>Tue, 17 Feb 2026 16:00:00 +0000</pubDate><guid>/blog/posts/microservices-performance-fresser/</guid><description>Amazon Prime Video cut costs by 90% by moving away from microservices back to a monolith.</description></item><item><title>NGINX: When Reverse Proxies Cost More Than They're Worth</title><link>/blog/posts/nginx-performance-fresser/</link><pubDate>Mon, 16 Feb 2026 16:00:00 +0000</pubDate><guid>/blog/posts/nginx-performance-fresser/</guid><description>&lt;p>NGINX sits between your users and your application. Before a single request reaches your code, NGINX is parsing configs, terminating SSL, rewriting URLs, and logging everything. All of this overhead. All of this complexity.&lt;/p>
&lt;p>The Ingress-NGINX controller is being retired in March 2026. About 50% of cloud-native setups depend on it. No more fixes. No more patches. Migrating means rewriting ingress configs across hundreds of services. Staying means increasing security risk. Pick your poison.&lt;/p></description></item><item><title>Kubernetes: The Orchestration Tax Most Teams Don't Need</title><link>/blog/posts/kubernetes-performance-fresser/</link><pubDate>Sun, 15 Feb 2026 16:00:00 +0000</pubDate><guid>/blog/posts/kubernetes-performance-fresser/</guid><description>&lt;p>Kubernetes was built to orchestrate Google&amp;rsquo;s global infrastructure. You are not Google. Terribly sorry.&lt;/p>
&lt;p>82% of container users run Kubernetes in production. Most of them shouldn&amp;rsquo;t.&lt;/p>
&lt;h2 id="the-control-plane-tax">The Control Plane Tax&lt;/h2>
&lt;p>Before your application serves a single request, Kubernetes needs etcd chewing through 2-8 GB RAM per node. Then kube-apiserver, kube-scheduler, kube-controller-manager, kubelet (reserving 25% of node memory by default), CoreDNS, kube-proxy, and a CNI plugin. All of this before your code runs.&lt;/p></description></item></channel></rss>