Datadog unveils IDP to boost developer autonomy & speed
Datadog has introduced its Internal Developer Portal (IDP), billed as the first developer portal built on live observability data, aiming to support engineering teams under increasing demands for faster and more reliable software delivery.
Engineering teams reportedly face rising pressure to deliver code that is not only fast and secure but also compliant with legal, regulatory, and internal policies. In this environment, developers are expected to manage a broad span of requirements, including code quality, testing, security scans, infrastructure configurations, observability, and compliance—while also understanding dependencies and real-time system performance.
Increasing system complexity and corresponding cognitive load mean that developers increasingly depend on platform engineers to resolve bottlenecks, which, according to Datadog, can slow down software delivery as both groups tackle resource constraints.
According to the company, the Datadog IDP is designed to grant developers greater autonomy, enabling them to ship updates quickly while adhering to established standards. The IDP relies on Datadog's Application Performance Monitoring (APM) suite to automatically map services and dependencies. This creates a real-time, unified view of performance, service ownership, and relevant engineering information.
The product allows developers to build, test, deploy, and monitor software through self-service actions that include built-in guardrails for delivery. Meanwhile, platform engineers can use scorecards to track compliance with criteria such as reliability, security, and monitoring standards.
Capabilities
Datadog IDP incorporates several core features designed to support these objectives. The Software Catalog offers a continually updated record of organisational software, including ownership, real-time performance metrics, and links to documentation, dashboards, and source code. The catalog is automatically synchronized to Datadog's telemetry stream.
Self-service actions are provided via pre-built templates, facilitating tasks such as provisioning infrastructure or triggering remediation steps without the need for direct intervention from platform engineers. These templates are powered by Datadog's App Builder and Workflow Automation tools.
Scorecards, part of the IDP, allow for the setting and monitoring of pass/fail rules in areas such as reliability, security, observability, and cost, with options for both standard and custom criteria. Engineering Reports provide visibility into reliability, performance, and compliance status, supplying targeted views for team leads, developers, and executives. "Datadog's IDP brings together both observed and declared system states, as well as existing systems of record. This combination shows not only developer intention but also what is actually in production. Whether developers onboard new teams or are tasked with complex projects such as migrating code from EC2 to Kubernetes, Datadog automatically provides visibility into their systems and reflects changes as they are being made—without stale metadata or manual syncing," said Michael Whetten, VP of Product at Datadog. "Datadog IDP empowers developers to collaborate more effectively and deliver software that meets their organisation's standards, at the pace that is expected from them."
The company states that IDP also enhances incident response by providing a live, central knowledge base for quicker triage and decision making during service outages or other technical incidents. This information is integrated with other tools across the Datadog platform, such as Status Pages, which uses the same ownership metadata to communicate incident scope and impact to stakeholders.
Additional functionality includes a voice interface, enabling on-call engineers to query service owners, review recent changes, and access other relevant information hands-free for faster diagnostics and investigations using data from the IDP.
The launch of Datadog IDP coincided with the company's announcements in areas including AI observability, applied AI, AI security, and log management.
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