Operations | Monitoring | ITSM | DevOps | Cloud

To gain adequate visibility into your distributed applications, you need to observe those applications at different levels of granularity. This means that you need to be able to query collected telemetry data both at the level of the whole application and at the level of selected components. Thanks to the power of Datadog tagging, you can already do this by aggregating your metrics within any scope of your choosing.

Security, operations, and development teams rely more and more on the ability to efficiently query logs. As these teams monitor the health, performance, and usage of their systems and investigate incidents, delving into log data can often be a matter of urgency.

As your organization scales its Datadog footprint, you want to understand what’s driving cost changes and promote cost awareness. But to take meaningful action, you need more than a monthly bill—you need real-time, contextualized cost data tied to services and teams. Without this visibility, it’s hard to assign ownership, prevent cost overruns, or identify which changes are affecting spend.

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.

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.

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.

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.

If you’re a Datadog customer, you’re likely using our platform to gain visibility into your infrastructure and applications and to troubleshoot using logs, metrics, and traces when issues arise. To support these efforts, you’ll want access to the most granular telemetry signals and intuitive workflows that streamline your investigation.

Delivering software quickly and reliably is the main focus of modern DevOps. But to improve your delivery performance, you need to understand it, and that starts with measurement. Teams primarily measure performance in this area by using DORA metrics—deployment frequency, change lead time, change failure rate, and time to restore service*. These metrics help teams understand trends in their software delivery practices in quantifiable terms that they can track and improve over time.