> For the complete documentation index, see [llms.txt](https://docs.release.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.release.com/integrations/monitoring-and-logging-add-ons/datadog.md). # Datadog Datadog is a software-as-a-service system that collects application logs and system metrics from cloud environments of any scale. We use Datadog at Release to monitor our systems. This allows us to identify bottlenecks and monitor our servers, databases, networks, and more. We recommend using Datadog to monitor your Release environments. You can install a Datadog collection agent in your Kubernetes clusters on Release to gain insight into how your clusters and applications run. To get started, follow our documentation on [monitoring your Release clusters using Datadog](/guides-and-examples/advanced-guides/monitoring-your-release-clusters.md), which guides you through the steps to create a Datadog account, add an agent to your Release clusters, and set up your first monitor. If you're unsure about whether using Datadog is the right decision for your organization, or if you need any help getting started, reach out to your technical account manager at Release. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.release.com/integrations/monitoring-and-logging-add-ons/datadog.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.