Operations | Monitoring | ITSM | DevOps | Cloud

In 2025, the role of compliance officers and risk managers has never been more complex—or more critical. New regulatory requirements, AI-generated content, and increasingly sophisticated cyber threats have dramatically raised the stakes. Here is just a small set of the pressing challenges facing compliance monitoring officials this year.

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.

Managing a self-hosted cloud infrastructure can be a complex and demanding task, which is why we pay for the privilege of companies to do this for us. Thanks to the range of options we have available, we can no move our data from the cloud quickly and easily, but there is one potential boundary of this, egress fees. Egress fees are what you have to pay to move your data from out the your cloud provider to another data center.

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 came as a pleasant surprise. CloudZero recently discovered over $1.7 million in annualized savings. And, all from our own infrastructure. Otherwise, we would have spent $1.7 million on cloud resources we didn’t actually need. Cloud savings stories like this are common with CloudZero customers, and we often find companies spending more than they should in the cloud. If your team is also struggling with managing and controlling the cost of cloud computing, then this guide is for you!