Operations | Monitoring | ITSM | DevOps | Cloud

PHP powers a large swath of the web — from blogs to storefronts to APIs. But with microservices, third-party dependencies, and scaling complexity, performance can slip in subtle ways. Your app might mostly work, but small—noted delays, occasional spikes, or hidden bottlenecks build up. An APM tool helps you see inside the black box: which functions are slow, which DB queries are hogging time, which external calls are failing or stalling.

When developers monitor application performance, they pick one of two paths: traditional APM tools with distributed tracing and code profilers, or metrics-driven monitoring with Prometheus. The second approach — Prometheus APM — tracks the signals that matter most: request rates, error rates, latency, and resource utilization. No agents to install, no per-host pricing, just exporters and PromQL. For most teams, Prometheus APM is where monitoring starts.

Datadog and Splunk are both leading tools for monitoring and observability. Each offers a range of features designed to help you understand and manage your data. Datadog provides tools for tracking application performance and analyzing logs in real-time. Splunk, meanwhile, is known for its powerful log analysis and search capabilities. In this post, we will compare Datadog and Splunk on important aspects like APM, log management, search capabilities, and more.

This is just a quick blog to draw attention to some new and enhanced monitoring dashboards and reports we have added to eG Enterprise in our latest release (v7.5) to provide quick and powerful overviews when monitoring a range of Kubernetes technologies. As with all our dashboards, color-coded overlays provide guided drilldown for help desk operators and administrators.

Are your Java applications running at their optimal performance, or is there room for improvement to make them faster and more efficient? With so many services depending on Java, keeping applications responsive and reliable is a core part of modern software engineering. This blog walks you through the leading Java Application Performance Monitoring (APM) tools, with a clear comparison to help you choose the right option for your needs.

OpenMetrics and OpenTelemetry are popular standards for instrumenting cloud-native applications. Both projects are part of the Cloud Native Computing Foundation (CNCF) and aim to simplify how we generate, collect and monitor services in a modern cloud-native distributed application environment. Let's have a look at how both the standards are aiming to help solve the observability conundrum.

A few days ago I hosted a live conversation with Pranav, co-founder of Chatwoot, about issues his team was running into with LLM observability. The short version: building, debugging, and improving AI agents in production gets messy fast. There's multiple competing standards for default libraries for LLM observability. And many such libraries like OpenInference which claim to be based on OpenTelemetry don't strictly adhere to it's conventions.