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Datadog Launches Feature Flags

Datadog launched Feature Flags, which unifies feature management with observability to help engineering teams release new functionality fast without compromising reliability. 

The product is now generally available and integrates natively across Datadog APM and RUM.

Datadog Feature Flags natively connects every feature flag to real-time observability data. With this integration, teams can immediately trace reliability issues to the exact feature or configuration responsible, automate rollouts and rollbacks, enforce experimentation guardrails, and clean up stale flags before they accumulate into technical debt. Feature Flags complements Datadog’s CI/CD visibility and test optimization products by extending observability left into release management itself.

“Releasing new features is one of the riskiest parts of modern software delivery, and releasing frequently is even more important in today’s AI-driven development age,” said Yanbing Li, Chief Product Officer at Datadog. “Datadog Feature Flags, created with a head start after our acquisition of Eppo, allows development teams to automatically detect regressions, enforce reliability guardrails, and ship updates faster and more safely by tying every flag to real-time telemetry.”

Datadog Feature Flags helps organizations deliver new functionality safely and reliably by providing:

  • Unified Observability + Feature Management: Correlate every feature flag with Datadog telemetry (APM and RUM) to see exactly how a feature affects performance and reliability in one view.
  • Automated, Data-Driven Rollouts and Rollbacks: Mitigate risk with canary releases, circuit breakers, and instant rollbacks triggered by real-time service health signals, without manual intervention or custom scripts.
  • Dynamic Configuration and Safe Experimentation at Scale: Adjust system behavior instantly without redeploying code. Enforce guardrails across environments and prevent reliability regressions during experiments.
  • Automated Stale-Flag Cleanup: Reduce technical debt with Bits AI and MCP integrations that identify unused flags and generate pull requests to safely remove dead paths from codebases.

Feature Flags is now generally available.

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Datadog Launches Feature Flags

Datadog launched Feature Flags, which unifies feature management with observability to help engineering teams release new functionality fast without compromising reliability. 

The product is now generally available and integrates natively across Datadog APM and RUM.

Datadog Feature Flags natively connects every feature flag to real-time observability data. With this integration, teams can immediately trace reliability issues to the exact feature or configuration responsible, automate rollouts and rollbacks, enforce experimentation guardrails, and clean up stale flags before they accumulate into technical debt. Feature Flags complements Datadog’s CI/CD visibility and test optimization products by extending observability left into release management itself.

“Releasing new features is one of the riskiest parts of modern software delivery, and releasing frequently is even more important in today’s AI-driven development age,” said Yanbing Li, Chief Product Officer at Datadog. “Datadog Feature Flags, created with a head start after our acquisition of Eppo, allows development teams to automatically detect regressions, enforce reliability guardrails, and ship updates faster and more safely by tying every flag to real-time telemetry.”

Datadog Feature Flags helps organizations deliver new functionality safely and reliably by providing:

  • Unified Observability + Feature Management: Correlate every feature flag with Datadog telemetry (APM and RUM) to see exactly how a feature affects performance and reliability in one view.
  • Automated, Data-Driven Rollouts and Rollbacks: Mitigate risk with canary releases, circuit breakers, and instant rollbacks triggered by real-time service health signals, without manual intervention or custom scripts.
  • Dynamic Configuration and Safe Experimentation at Scale: Adjust system behavior instantly without redeploying code. Enforce guardrails across environments and prevent reliability regressions during experiments.
  • Automated Stale-Flag Cleanup: Reduce technical debt with Bits AI and MCP integrations that identify unused flags and generate pull requests to safely remove dead paths from codebases.

Feature Flags is now generally available.

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...