Operations | Monitoring | ITSM | DevOps | Cloud

Getting paged pulls engineers away from meaningful work, yet incident response in many organizations remains manual, reactive, and draining. An alert fires and teams scramble to find the root cause, relying on siloed knowledge, incomplete context, and a few on-call experts who are already stretched thin. The rise of AI coding agents has only intensified this challenge: As teams ship code faster with less human oversight, production systems grow increasingly complex and harder to understand.

Migrating to a new logging platform can be a complex operation, especially when it involves both active and historical logs. Observability Pipelines offers dual-shipping capability, making it easy to route active logs to your new platform without disrupting your log management workflows. But migrating years worth of historical logs—which are critical for investigating security incidents and demonstrating compliance with applicable laws—requires a different approach.

As your cloud environment grows more complex and dynamic, it becomes more difficult to maintain up-to-date reference diagrams, visualizing its components, that are available to all teams. As a result, teams often end up lacking the visibility they need to understand, manage, and troubleshoot their cloud infrastructure and applications.

Datadog has long supported the monitoring of instrumented Go applications through our Go tracer v1. As the Go ecosystem has continued to mature, we’ve been hard at work collecting feedback and improving upon the tracer’s capabilities and usability features. We are now thrilled to announce the release of our Go tracer v2.0.0. This major update includes better security and stability, and a new and simplified API.

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.

Real User Monitoring (RUM) helps teams understand exactly how their users experience their web and mobile applications—from load times to crashes and frustration signals. But traditional RUM models come with tough trade-offs: capture all sessions and overspend, or sample data and miss what matters. Fixed sampling rates may help manage volume, but they leave dangerous blind spots.

Google Cloud Next is the biggest event of the year for the Google Cloud community, showcasing the latest and greatest offerings from Google Cloud and hundreds of its partners. As a long-time Google Cloud partner and recipient of three Google Cloud Partner of the Year awards in 2025, Datadog was there in full force, delivering several speaking sessions and running a booth on the expo floor where we met with thousands of attendees. In case you missed it, don’t worry.

Datadog dashboards provide a unified view of your applications, infrastructure, logs, and other observability data—making it easy to monitor health, investigate issues, and share insights across teams. While native Datadog widgets support a broad range of visualization types, some use cases call for more customized representations, particularly when you’re working with unconventional data formats, external sources, or specific transformations.

Hallucinations occur when a large language model (LLM) confidently generates information that is false or unsupported. These responses can spread misinformation that jeopardizes safety, causes reputational damage, and erodes user trust. Augmented generation techniques, such as retrieval-augmented generation (RAG), aim to reduce hallucinations by providing LLMs with relevant context from verified sources and prompting the LLMs to cite these sources in their responses.