Operations | Monitoring | ITSM | DevOps | Cloud

The rise of artificial intelligence has revolutionized the way businesses create and manage digital content. As of 2024, over 45% of marketing teams are actively using generative AI tools like ChatGPT, Claude, and Jasper to create website copy, blog posts, product descriptions, and more (Salesforce). Meanwhile, Gartner predicts that by 2026, 80% of content on the internet will be AI-generated.

For decades, enterprise IT teams have operated in a state of controlled chaos. Pressured to keep digital lights on, these teams have spent far too much time buried in logs, swatting away alerts, and fighting fires one incident at a time. The familiar mantra—“do more with less”—has translated into a culture of reactive operations, where innovation takes a backseat to survival.

Mobile developers are increasingly adopting Kotlin Multiplatform to share business logic across iOS and Android. While Kotlin Multiplatform reduces duplication of code-writing efforts, it also introduces blind spots. Developers often lack real-time visibility into how shared code performs across platforms, making it harder to troubleshoot issues and monitor user experience.

Finding the right integrations, libraries, and open source tooling to extend a product has long been a challenge for developers. While Datadog has a vast offering of monitoring and observability solutions, many teams need to customize their setup in some way—whether by extending the Datadog Agent, integrating with third-party services, or using SDKs to interact with the Datadog API.

Businesses deploying LLM workloads increasingly rely on LLM proxies (also known as LLM gateways) to simplify model integration and governance. Proxies provide a centralized interface across LLM providers, govern model access and usage, and apply compliance safeguards for smoother operations and reduced complexity—making LLM usage more consistent and scalable.

Before we started using Datadog Real User Monitoring (RUM), we relied on frontend logging to gather data about the user experience. Logs gave us some helpful information about exceptions and errors but didn't provide any insight into issues directly related to the user’s perspective.

By applying data engineering and machine learning to raw network telemetry, it’s possible to surface insights that would otherwise go unnoticed. Learn how this approach helps teams detect anomalies in real time, forecast capacity needs, and automate responses across complex, multi-domain environments.

IT Ops Problem Solver Series – Part 2: This article is a summary of a full report in our IT Ops Problem Solver Series. In this series, we’ll tackle the biggest problems facing IT Ops leaders and explore how some of Galileo’s clients are addressing them. In this part of the series, we delve into IT performance challenges and how to address them effectively.

SolarWinds is a popular IT infrastructure monitoring tool deployed on-prem, most well-known for its network and server monitoring capabilities. While it offers rich telemetry, it’s easy to miss the bigger picture. SquaredUp turns this complex monitoring data into clear, shareable dashboards that make it easier to spot trends, catch issues early, and keep everyone on the same page.

Synthetic monitoring simulates user interactions with applications to proactively identify performance issues before they impact real users. Modern distributed systems require sophisticated monitoring capabilities to effectively test microservices, APIs, and complex user journeys across diverse environments. This article provides a framework to evaluate synthetic monitoring platforms in 2025.