Operations | Monitoring | ITSM | DevOps | Cloud

Application monitoring is important for finding and fixing issues in modern software systems. Traditionally, teams have used Application Performance Monitoring (APM) tools to track application health and performance. These tools provide built-in features like dashboards, alerting, and error tracking. Now, OpenTelemetry is becoming popular as an open-source way to collect telemetry data like traces, metrics, and logs. It gives developers more control and avoids vendor lock-in.

In our increasingly serverless world, observability isn’t just a “nice to have”—it’s essential. Serverless functions such as AWS Lambda bring incredible benefits, but they also introduce complexities, especially around monitoring and debugging. In a previous article, I provided a quick, practical guide for sending AWS Lambda traces to Grafana Cloud using OpenTelemetry.

AI systems, from machine learning models to Large Language Models (LLMs) and autonomous AI agents, introduce unique observability challenges. Their non-deterministic nature, complex dependencies, and specialized performance characteristics require thoughtful instrumentation approaches. OpenTelemetry has emerged as the leading standard for implementing observability across these systems.

Effective database monitoring is essential for maintaining application performance and reliability. This guide explores how to implement OpenTelemetry with PostgreSQL, providing DevOps engineers and Site Reliability Engineers with practical steps for comprehensive database observability.

I was lucky enough to attend KubeCon Europe 2025 in London, where the energy around OpenTelemetry (OTel) reached fever pitch. From packed sessions to buzzing hallway conversations, it’s clear: OpenTelemetry isn’t just the future—it’s the present. Here’s what stole the spotlight.