How Datadog uses AI to build internal software delivery tools and improve system performance

Datadog has been leveraging AI tools to enhance its software development life cycle (SDLC), resulting in significant improvements in internal tooling and processes. One of their key initiatives includes creating an automated AI support application using Gas City to streamline the management of deployment support requests, which has notably increased the number of resolved requests per shift. Additionally, Datadog has developed a shadowing platform using Claude Code and Cursor to test backend query changes against real-world conditions, allowing for comprehensive validation and reducing guesswork. Another innovation involves optimizing memory allocation in their Go services through the implementation of a new parser designed with Claude Code, which led to a 10% increase in network capacity and potential annual savings of $2 million. These projects underscore Datadog's commitment to using AI to foster more efficient development workflows and enhance the overall performance and reliability of their systems.

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