Operations | Monitoring | ITSM | DevOps | Cloud

I'll start this, the third and final entry in my series on APM and Observability, which was originally inspired by my contribution to an APMdigest article, by once again pointing out that APM tools can be built with observability in mind. Many are, in fact. And the ones that aren’t don’t turn into a different type of tool. In my experience, it's more that there's a difference of mindset.

If you’re building high-performance systems, Golang has probably earned a spot in your stack. Its speed, lightweight concurrency, and quick compile times make it ideal for scalable APIs, microservices, and distributed systems. But those same qualities that make Go powerful can make performance monitoring tricky. Goroutines run fast and in parallel, which means a simple CPU or memory graph doesn’t always tell you what’s slowing things down.

Organizations are increasingly looking to build quality and security into their systems and services by design, often by the adoption of frameworks, standards and certifications such as SOC 2 Type 2 audits and ISO/IEC 27001.

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.