Datadog offers Cloud Run metric collection and visualization through its Google Cloud integration. To set up the integration, you need to use service account impersonation, which enables Datadog to gain visibility into your serverless containerized workloads. The default methodology for instrumenting a Cloud Run application is through a sidecar container, which runs alongside your Cloud Run Functions as it collects critical monitoring data. You can also instrument a Cloud Run application through either a Dockerfile or buildpack. Once the integration is enabled and set up, Datadog will automatically start collecting monitoring data from your serverless containers and populating an out-of-the-box dashboard with key metrics. The Cloud Run dashboard includes an overview widget, enabling you to quickly gauge the state of your Cloud Run environment, including a count of serverless containers and requests, the rate of errors, and top lists of revisions using the most memory and CPU. Datadog's integration allows you to set up critical alerts that help maintain the health and efficiency of your services, such as alerts for high rates of 5xx or 4xx errors, enabling you to quickly address issues that may be negatively impacting user experience. With Datadog, you can view Cloud Run monitoring data within the Datadog Serverless view, which surfaces key metrics alongside traces and logs so you can spot errors and quickly pivot between them all. Additionally, Datadog collects and visualizes critical Cloud Run job metrics such as a count of task attempts and completions, as well as a count of running and completed executions. If you are already using the Datadog Google Cloud integration, then Cloud Run logs will automatically be collected; otherwise, you need to set the DD_LOGS_ENABLED environment variable true within your cloud provider's environment settings or in your container configuration to ensure that application logs are captured and sent to Datadog.