Datadog Cloud Cost Management brings fine-grained cost data that’s scoped to teams and services onto the same monitoring platform engineers already use, so costs can be understood alongside observability data and made actionable.

Learn how to debug slow p95 requests or timeouts using the new timeline feature of Datadog's Continuous Profiler.

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. This month, we’re recapping DASH 2023..

Understanding the health and user experience of your mobile application is critical in order to avoid user frustration, understand application crashes, and reduce bugs mean time to resolution. To help with that task, Datadog has a mobile monitoring solution that allows developers to better understand and improve their application. But what are the things to take into account when building observability mobile SDKs? How can we gather the right telemetry without affecting the underlying application?

Datadog provides full visibility into your AWS-hosted infrastructure and applications at scale. This demo shows you how Datadog monitors and secures your AWS environment in minutes with powerful observability features, such as preconfigured dashboards and monitors, faster troubleshooting with machine learning, and a single unified platform.

One of the biggest challenges in observability is separating the signal from the noise. As artificial intelligence (AI) tools become more powerful and accessible, it has generated a lot of buzz around the role of AI with respect to the performance and reliability of our technical systems and the teams that build and operate them. In this fireside chat, Michael Gertenhaber (Datadog VP of Product) and Sajid Mehmood (Datadog VP of Engineering) will sift through the hype to chat about what generative AI and Large Language Models (LLMs) will really mean for the future of observability and how it can benefit your teams today.

It’s been said that, “premature optimization is the root of all evil.” Contrarily, many engineers have also had to work with software riddled with so much technical debt and inefficiency that optimization is practically impossible and a complete rewrite is required. So when is the right time? In this panel session, we’ll talk with engineering leaders and architects about their approach to software optimization, when to do it, and how to design systems that scale and stay performant.

Building large scale technical systems is hard, but building and scaling high performing technical organizations is even more difficult. In this session, Datadog Co-founder and CTO Alexis Lê-Quôc will sit down with Prashant Pandey, Head of Engineering at Asana, to discuss their approach to engineering leadership. They’ll share the hard-learned lessons from their long careers to help you cultivate better technical teams, covering topics from staying in tune with new technologies, enabling innovation, shipping modern ML and AI-based features, and scaling teams.

WIth unlimited money, most technology problems become easy to solve. But how do you design, build, and operate large scale, performant systems without breaking the bank? In this session, Chandru Subramanian (Director of Engineering, Runtime Efficiency at Datadog) and Neil Innes (Sr. Engineering Manager, DevOps at FanDuel) will discuss how they balance efficiency and effectiveness to save money while also meeting key goals.

GraphQL is a query language for APIs that provides a powerful and efficient way to query and manipulate data. As powerful and versatile as GraphQL is, its downside is that it can be vulnerable to certain security threats. In this presentation, we will discuss the security vulnerabilities associated with GraphQL, from the basics to more advanced threats, and how to best protect against them. After this presentation, attendees will have a better understanding of security vulnerabilities in GraphQL, as well as an understanding of the steps needed to protect against them.