Operations | Monitoring | ITSM | DevOps | Cloud

AI spending continues to accelerate. In 2025, experts project that companies will collectively spend about $644 billion on generative AI alone — a whopping 76.4% increase from 2024. This puts it a mere $79 billion behind the public cloud as a whole, signaling the most seismic interval of new infrastructure investment since the dawn of the public cloud.

OpenTelemetry (OTel) is emerging as the industry standard for collecting and transmitting observability data. Datadog supports several ways to send and accept OTel-native data, while also continuing to support its own native telemetry format. To provide a consistent monitoring experience, Datadog now supports using OTel-native metrics alongside Datadog-native metrics across dashboards, queries, and core visualizations in the Datadog platform.

Custom metrics enable you to track what matters to your distinct business and services and correlate it with the rest of your telemetry data. As your organization grows by adding more teams, services, and environments, your volume of custom metrics can grow with it. To ensure critical visibility while maintaining cost efficiency, organizations need an end-to-end approach to custom metrics governance.

Java license monitoring has now become an essential requirement for many organizations as Oracle’s recent licensing changes have made compliance mandatory, with increased risks of audits and higher Java licensing compliance costs. Once a free programming platform, Java now requires navigating a complex licensing framework, including employee-based models that tie costs to the size of a workforce. These changes significantly increase the risk of unbudgeted expenses for licensing violations.

Lately, there has been a growing buzz around the concept of “Vibe Working,” where teams are leveraging AI to dynamically share, develop, test, and transform “fuzzy” ideas into something useful in real-time. I view this approach as one of the next significant evolutions in our professional and technological landscape. Reflecting on my own journey in technology, I’ve observed how the pace of innovation and collaboration continually reshapes our daily workflows.

In this day and age, rare is the organization (if there is one at all) that has never been hit by a cyberattack. Few have escaped the nightmare of systems going down, customers losing access to their accounts, or payments getting stuck mid-transfer. Just as common is all the stress on the path to recovery and the absence of a structured, streamlined, and repeatable process for effectively preparing for the worst.

Just when you’d finally wrapped your head around AI, here comes its autonomous cousin, Agentic AI. Think of it as AI that doesn’t just assist, but acts. It makes decisions, handles tasks, and communicates with other systems on its own. While it’s revolutionizing supply chains and customer experiences, there’s a catch. These autonomous agents rely on a plethora of third-party services, and when one fails, everything stops.

When managing and troubleshooting IIS (Internet Information Services) web server performance, logs are a critical resource. They capture detailed information about every request and response so your team can detect issues quickly. Let’s walk through the main IIS log formats, explore a sample log file, and break down five key types of IIS metrics you should monitor.