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Datadog On-Call Introduced

Datadog announced Datadog On-Call, an on-call experience with observability-enriched paging and seamless incident management workflows.

Datadog On-Call instantly coordinates teams with relevant context for faster issue resolution, better incident control and improved collaboration.

By unifying observability and paging into one seamless platform, Datadog On-Call solves these issues and eliminates the inefficiencies of multiple disjointed tools, allowing engineers to focus on resolving incidents quickly and effectively without the added stress of switching contexts or missing critical information.

“Being on-call is one of the most challenging aspects of an engineer’s job, where redundant service configurations between various tools can lead to brittle, error-prone setups. The general overhead of maintaining on-call schedules and the ambiguity around service and team ownership make it a grueling ordeal, especially during critical times,” said Michael Whetten, VP of Product at Datadog. “Datadog On-Call addresses these pain points with a team-centric design that clarifies ownership, reduces redundancy and minimizes errors. This approach ensures that every team member knows their role and responsibilities, leading to quicker and more effective incident response.”

Datadog On-Call helps DevOps, SRE, Security and IT Operations teams:

- Act Quickly and Stay Informed: Paging with integrated observability and seamless incident management ensures critical insights and data are readily available within a single platform, eliminating the need for context switching.

- Connect with the Tools They Use Every Day: On-Call integrates with a rich ecosystem of third-party monitoring, alerting and service management tools so teams don’t have to learn new workflows or spend resources on training.

- Ensure Clear Service and Team Ownership: Break down knowledge silos and avoid confusion by associating teams with their respective services to simplify configuration, address ownership gaps and ensure the right responders are paged during an alert. Instantly trace upstream and downstream services affected by an outage or issue.

- Implement Intuitive Scheduling and Notifications: Automate scheduling and escalation policies to ensure continuous coverage and timely responses, reducing the burden on individual team members and enhancing overall team coordination.

- Measure On-Call Performance: Rich and customizable analytics measure on-call performance to help ensure system reliability, improve mean-time-to-resolution and optimize the well-being of on-call teams.

Datadog On-Call is in beta now.

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Datadog On-Call Introduced

Datadog announced Datadog On-Call, an on-call experience with observability-enriched paging and seamless incident management workflows.

Datadog On-Call instantly coordinates teams with relevant context for faster issue resolution, better incident control and improved collaboration.

By unifying observability and paging into one seamless platform, Datadog On-Call solves these issues and eliminates the inefficiencies of multiple disjointed tools, allowing engineers to focus on resolving incidents quickly and effectively without the added stress of switching contexts or missing critical information.

“Being on-call is one of the most challenging aspects of an engineer’s job, where redundant service configurations between various tools can lead to brittle, error-prone setups. The general overhead of maintaining on-call schedules and the ambiguity around service and team ownership make it a grueling ordeal, especially during critical times,” said Michael Whetten, VP of Product at Datadog. “Datadog On-Call addresses these pain points with a team-centric design that clarifies ownership, reduces redundancy and minimizes errors. This approach ensures that every team member knows their role and responsibilities, leading to quicker and more effective incident response.”

Datadog On-Call helps DevOps, SRE, Security and IT Operations teams:

- Act Quickly and Stay Informed: Paging with integrated observability and seamless incident management ensures critical insights and data are readily available within a single platform, eliminating the need for context switching.

- Connect with the Tools They Use Every Day: On-Call integrates with a rich ecosystem of third-party monitoring, alerting and service management tools so teams don’t have to learn new workflows or spend resources on training.

- Ensure Clear Service and Team Ownership: Break down knowledge silos and avoid confusion by associating teams with their respective services to simplify configuration, address ownership gaps and ensure the right responders are paged during an alert. Instantly trace upstream and downstream services affected by an outage or issue.

- Implement Intuitive Scheduling and Notifications: Automate scheduling and escalation policies to ensure continuous coverage and timely responses, reducing the burden on individual team members and enhancing overall team coordination.

- Measure On-Call Performance: Rich and customizable analytics measure on-call performance to help ensure system reliability, improve mean-time-to-resolution and optimize the well-being of on-call teams.

Datadog On-Call is in beta now.

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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 ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...