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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.

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...