<?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>OpenTelemetry on Vinh Thang's AI Tech Notes</title><link>https://vinhthang.dev/tags/opentelemetry/</link><description>Recent content in OpenTelemetry on Vinh Thang's AI Tech Notes</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 24 Aug 2026 23:35:00 +0700</lastBuildDate><atom:link href="https://vinhthang.dev/tags/opentelemetry/index.xml" rel="self" type="application/rss+xml"/><item><title>Building an Ultra-Lightweight Cloud-Native Observability &amp; Analytics Stack: Metrics, Logs, Traces &amp; Status on OCI</title><link>https://vinhthang.dev/posts/complete-observability-stack-metrics-logs-traces-2026/</link><pubDate>Mon, 24 Aug 2026 23:35:00 +0700</pubDate><guid>https://vinhthang.dev/posts/complete-observability-stack-metrics-logs-traces-2026/</guid><description>&lt;p&gt;Running a complex fleet of microservices—spanning AI document assistants, vector databases, lossless audio streamers, private DNS resolvers, and astronomical APIs—demands complete, real-time visibility. But traditional enterprise observability suites (Elasticsearch, heavy Prometheus clusters, DataDog) frequently devour gigabytes of memory and CPU cycles just to monitor a small infrastructure.&lt;/p&gt;
&lt;p&gt;In this deep dive, I break down how we architected and deployed a &lt;strong&gt;complete, ultra-lightweight, 360-degree Observability, Logging, Analytics, and Uptime Stack&lt;/strong&gt; across our 3-node hybrid cloud fleet at &lt;a href="https://vinhthang.dev"&gt;&lt;strong&gt;&lt;code&gt;vinhthang.dev&lt;/code&gt;&lt;/strong&gt;&lt;/a&gt;, maintaining sub-millisecond query latencies while consuming &lt;strong&gt;less than 250 MB total RAM&lt;/strong&gt;!&lt;/p&gt;</description></item></channel></rss>