Operations | Monitoring | ITSM | DevOps | Cloud

A website that loads slowly or an application that freezes mid-transaction tells users something about an organization, whether intended or not. Digital experience monitoring exists to catch these moments before they accumulate into lost customers and frustrated employees. We’ll show you how DEM works, the leading platforms available, and how to select the right solution for specific organizational needs.

Zabbix collects a huge amount of operational data—metrics, alerts, host status, and performance trends. But turning that data into dashboards people actually use is a different challenge. Most teams start with the built-in dashboards. Then the requests start coming: At that point, basic dashboards aren’t enough. Teams start looking for ways to augment Zabbix visualization with tools that improve usability, sharing, and flexibility.

If you run a managed service provider, your RMM software is the backbone of daily operations. Remote management, patch cycles, ticketing workflows – it handles the essentials. But if you’re monitoring more than a few dozen client environments, you’ve likely noticed that monitoring and management are not the same thing. And that difference matters more the larger you grow. This post is not about replacing your RMM.

The best server monitoring tools depend on what you actually need to watch. If you want unified metrics, logs, and traces in one SaaS, Datadog wins. For AI-driven root-cause analysis at enterprise scale, Dynatrace is the pick. If you want monitoring, status pages, and on-call scheduling at a flat monthly rate without per-host or per-seat surprises, Hyperping is the best value. For Windows-heavy networks, PRTG. For hybrid IT with deep plugin coverage, Checkmk. For open-source flexibility, Zabbix.

In the race to provide full-stack visibility, many modern SaaS platforms have inadvertently created a new problem: information overload. High-end enterprise solutions are designed for companies with dedicated Site Reliability Engineering (SRE) teams that spend their entire day inside a dashboard. But for many businesses, this level of granularity is a distraction. The real question isn’t whether a tool is powerful; it’s whether it fits the everyday needs of your team.

ServiceNow holds a wealth of operational data—but turning that data into dashboards people actually use is a different challenge altogether. Most teams start with what’s available out of the box. Then come the requests: At that point, dashboarding stops being simple. It then has to be “augmented” - with easy shareability, ease of use, contextualization and hierarchy.

AI has rapidly evolved from an experimental technology into a foundational capability for modern enterprises. Today, organizations are no longer asking whether AI should be adopted but how quickly it can deliver measurable operational value.

Over the last three months, we’ve been exploring what about software development and observability changes with AI, and what doesn’t. Our conclusion: these five principles will still remain true, even when 90% of the code is AI-driven. The agentic AI space is moving fast. Models are improving, context windows are expanding, and the ways people build and operate agents are changing so fast that any thoughts we share could feel dated by the time you read this.

Alerts are meant to help teams respond quickly to problems, but too often they arrive without enough context to be immediately useful. An alert that says “CPU usage is high” still leaves the on-call engineer asking critical follow-up questions: Which service? Which environment? Where do I look next? Validating the alert and triaging the situation is the first step for every engineer. It's a manual step that takes time, extending every potential incident.