Datadog is constantly elevating the approach to cloud monitoring and security. This Month in Datadog updates you on our newest product features, announcements, resources, and events. To learn more about Datadog and start a free 14-day trial, visit Cloud Monitoring as a Service | Datadog. This month, we put the Spotlight on Datadog Cloud Cost Management for OpenAI.

Did you miss this year’s re:Invent? Or maybe you were onsite but too busy deep diving on certifications, new products, and networking. Don’t worry—the Datadog team is streaming right to your home on December 17 to recap all of the highlights from the event. Join Andrew Krug from Datadog’s Technical Community along with a host of AWS guests to hear about exciting announcements from AWS re:Invent 2024, Datadog’s latest product launches, and a rundown of the best on-demand sessions that you’ll want to make sure to tune into.

Datadog Database Monitoring unifies query, application, and database telemetry in one platform, enabling teams to easily identify bottlenecks, understand database load, optimize query performance, uncover costly queries, and correlate database and application telemetry.

Cloud spending continues to grow, but managing costs effectively remains a challenge for many organizations. In this video, Datadog Senior Product Manager Kayla Taylor dives into our recent State of Cloud Costs report—which analyzed AWS cloud cost data from hundreds of organizations—to understand the key factors driving cloud expenses. We explore the impact of adopting emerging compute technologies like Arm-based processors, GPUs, and AI capabilities, how usage patterns and previous-generation technologies affect cloud costs, and the role of AWS discount programs in cost management.

In this video we’ll continue looking at how Kubernetes handles authentication with a look at bootstrap and static token authentication.

Datadog operates dozens of Kubernetes clusters, tens of thousands of hosts, and millions of containers across a multi-cloud environment, spanning AWS, Azure, and Google Cloud. With over 2,000 engineers, we needed to ensure that every developer and application could securely and efficiently access resources across these various cloud providers.

This video aims to showcase how developers can self-serve from an application to simplify the management of their AWS cloud resources. Rather than switching between tools or reaching out to another team for help, developers can take action directly from their observability tool, enabling faster resolution of application issues. We will demonstrate how to build a simple app that allows them to minimize disruptions by quickly taking action on their SQS queues in AWS, using insights provided by Datadog.

Temporal is an open source platform to build resilient and reliable distributed systems. Datadog started using Temporal in 2020 as the foundation for our internal software delivery platform. Since then, its usage has been widely adopted as a platform that any engineering team can use to build their systems. In this Datadog on episode, Ara Pulido chats with Loïc Minaudier, Senior Software Engineer in the Atlas team, responsible for providing a developer platform on top of Temporal, and Allen George, Engineering Manager in the Datadog Workflows team.

On This Month in Datadog, we’re spotlighting LLM Observability’s native integration with Google Gemini, which automatically captures the LLM requests your application makes to Gemini models.

Datadog Service Catalog automatically consolidates real-time observability data and internal engineering knowledge about all of your services into a unified view.