Effective mobile application testing that meets all the requirements of modern quality assurance can be challenging. Not only do teams need to create tests that cover a range of different device types, operating system versions, and user interactions—including swipes, gestures, touches, and more—they also have to maintain the infrastructure and device fleets necessary to run these tests.

The volume of logs that organizations collect from all over their systems is growing exponentially. Sources range from distributed infrastructure to data pipelines and APIs, and different types of logs demand different treatment. As a result, logs have become increasingly difficult to manage. Organizations must reconcile conflicting needs for long-term retention, rapid access, and cost-effective storage.

This year at DASH, we announced new products and features that enable your teams to get complete visibility into their AI ecosystem, utilize LLM for efficient troubleshooting, take full control of petabytes of observability data, optimize cloud costs, and more. With Datadog’s new AI integrations, you can easily monitor every layer of your AI stack. And Bits AI, our new DevOps copilot, helps speed up the detection and resolution of issues across your environment.

Business-critical infrastructure and services generate massive volumes of observability data from many disparate sources. It can be challenging to synthesize all this data to gain actionable insights for detecting and remediating issues—particularly in the heat of incident response.

Datadog Network Performance Monitoring (NPM) gives you visibility into all the communication that takes place between the network components in your environment, including hosts, processes, containers, clusters, zones, regions, and VPCs. As organizations scale, and as their networks grow in complexity, the massive volume of network data to be monitored can become overwhelming. Knowing precisely what network data to surface to resolve issues within these larger environments can be a challenge.