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Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring

Mar 3, 2026 By Lauren Zuniga In Datadog
Every digital experience is a chain reaction. A customer logs in to an application, an API authenticates the request, a backend call retrieves data, a page loads, and somewhere along the way, something might break. When it does, teams often chase symptoms while the root cause remains hard to find. The more distributed the system, the more difficult it becomes to see how one small failure can cascade into a visible outage.
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Data Observability, AI Guard, Feature Flags, Ambassador program, and more | This Month in Datadog

Feb 27, 2026 By Datadog In Datadog
See how you can ensure trust across the data life cycle in February’s episode of This Month in Datadog. Join us for a spotlight of Datadog Data Observability, which enables you to detect data quality and pipeline issues early, as well as remediate those issues with end-to-end lineage. Plus, we cover: Protecting agentic AI applications from real-time threats with Datadog AI Guard Staying up to date and reducing steps to collaborate with five new Incident Management releases Releasing software with confidence using Datadog Feature Flags.
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This Month in Datadog - February 2026

Feb 27, 2026 By Datadog In Datadog
On the first episode of This Month in Datadog in 2026, Jeremy covers how you can protect agentic AI applications with AI Guard, stay up to date and collaborate during incidents with five Incident Management releases, and ship software with confidence using Feature Flags. Later in the episode, Kevin spotlights Datadog Data Observability, which enables you to detect data quality and pipeline issues early.
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Enable end-to-end visibility into your Java apps with a single command

Feb 26, 2026 By Sarjeel Yusuf In Datadog
Achieving end-to-end observability for applications is a top priority for organizations today, but instrumenting for both frontend and backend monitoring can be a significant hurdle. What complicates matters is that the SREs and DevOps teams responsible for deploying monitoring tools typically don’t own frontend code or have the context needed to safely modify it.
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Measure and improve mobile app startup performance with Datadog RUM

Feb 24, 2026 By Jessica Manheimer In Datadog
Mobile app users form opinions quickly. A slow or inconsistent startup experience can frustrate them before they reach the first screen, increasing the likelihood that they abandon the app or fail to complete key actions such as signing up or making a purchase. However, app teams often lack reliable signals that explain why startup performance varies, making it difficult to improve the user experience.
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Evaluating our AI Guard application to improve quality and control cost

Feb 23, 2026 By Santiago Mola In Datadog
This article is part of our series on how Datadog’s engineering teams use LLM Observability to build, monitor, and improve AI-powered systems. Organizations are building AI agents that help users automate work, analyze data, and interact with complex systems through natural language. As these agents become more capable, they also become more complex and exposed to risks such as prompt injection, data leaks, and unsafe code execution.
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Identify untested code across every level of your codebase

Feb 20, 2026 By Eric Metaj In Datadog
As organizations scale their services and adopt AI-assisted coding, code changes are landing faster and in greater volume than ever before. While this powerful new practice is accelerating the pace of development, it is also increasing the likelihood that untested code may slip into repositories without detection. What makes this problem even worse is that most teams have no reliable way to know which code is covered by tests.
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How to write annotations in Kubernetes with JSON for Datadog Autodiscovery | Datadog Tips & Tricks

Feb 19, 2026 By Datadog In Datadog
Pod annotations in Kubernetes with invalid JSON syntax can prevent Datadog Autodiscovery from detecting integrations, resulting in missing metrics and gaps in monitoring. Watch this video for a step-by-step process to write annotations: Note: This video focuses on Datadog Autodiscovery v2 syntax.
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Make use of guardrail metrics and stop babysitting your releases

Feb 19, 2026 By Anthony Rindone In Datadog
Modern CI/CD pipelines have automated the hard work of building, testing, and deploying our code. But for many teams, that’s where the automation stops. The most critical part of a release, turning a new feature on for real users, is still a stressful, manual process. An engineer cautiously ramps up traffic to 5%, then 10%. The whole team stares at dashboards, trying to see if anything breaks. If something does, they scramble to manually roll back.
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