Operations | Monitoring | ITSM | DevOps | Cloud

I've had this same conversation with 60+ engineering teams in the last six months. A team adopts AI tooling. One developer figures out how to use it well, builds up a vault of skills, MCP configs, and slash commands that 10x their output. The rest of the team has whatever they can scavenge from a shared Notion doc.

Monitoring in Kubernetes can seem like opening an airplane's black box. Everything happens silently, behind the scenes, hidden away. This can be a lot of trouble, as you don’t really want to dig through a bunch of logs at 3 a.m. after a call letting you know that a certain feature is broken. You want something direct, concise, and helpful.

The Node.js service has just been containerized and deployed with a single fly deploy command across continents. Everything seems to be alright, but then a week later, a user messages you saying the app is slow. You run the fly logs command and scroll through some logs, and find nothing out of the ordinary. The Fly.io dashboard says the app is running and healthy, but something behind the scenes is slowing down the app, and you have no idea what. You don’t even know where to start.

As your application scales to serve hundreds, thousands, or even millions of users, understanding its performance becomes essential. Performance monitoring helps you make informed decisions based on data instead of guesswork or user complaints. Imagine users reporting that your app feels"slow". Without proper instrumentation and monitoring, you're left troubleshooting blindly.

How to Detect, Alert, and Resolve Memory Issues Before They Cause Downtime When applications scale on Heroku, memory-related issues are among the most common (and most frustrating... -_- ) sources of instability. Two of the most notorious culprits are the R14 (Memory Quota Exceeded) and R15 (Memory Quota Hard Limit) errors.

Node.js applications power millions of APIs, microservices, and real-time systems. But without proper monitoring, performance issues, memory leaks, and errors can go undetected until they impact users. This guide explains how to monitor Node.js applications in production, what metrics to track, and which tools deliver the best results.

In part one of this series, we set up basic performance monitoring for our Fastify application using AppSignal and explored key performance indicators. Now that we have our monitoring foundation in place, it's time to leverage these insights to actively improve application performance. You'll learn how to detect performance regressions, find optimization opportunities, and implement custom instrumentation with OpenTelemetry.

When a Node.js app slows down, you don’t get a clear picture right away. One service stalls, another spikes in CPU, and somewhere in between, requests start piling up. You can’t fix what you can’t see. Application Performance Monitoring (APM) tools close that gap. They capture request traces, latency, and errors across your stack — showing you what’s running slow and why.

Serverless computing with Node.js is transforming how applications are built and scaled by removing the need to manage servers. However, serverless functions run for short durations and scale dynamically, making traditional monitoring ineffective. Effective monitoring is essential to track performance, detect errors, optimize cold starts, and control costs.