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NS1 Integrates with Datadog

NS1 announced enhanced support for Datadog.

A new integration brings the power of real-time data and reporting to Datadog users for improved visibility and monitoring of applications at the distributed edge.

As IT complexity explodes, application and network teams are challenged to collect, visualize, and act upon the deluge of data from disparate sources. They need end-to-end visibility into real-time conditions and solutions that connect into existing workflows so that they can identify, triage, and troubleshoot issues quickly and make more informed decisions about application health and performance. Now, teams can monitor and report on NS1 services within the Datadog platform, providing a single source of truth for observability across the modern tech stack.

“With Datadog and NS1, companies can implement full-stack observability with automatic, real-time adjustments and insights to help navigate changing conditions across the internet, cloud, networks, and infrastructure,” said Danielle Russell, Director of Product Marketing at NS1. “NS1 now connects with more observability solutions so that customers can deliver consistently exceptional application experiences while driving efficiency and automation.”

This is NS1’s second integration with Datadog. The company’s first integration, released in 2016, allows customers to push Datadog monitoring data into the NS1 platform to automate application traffic policy decisions based on near-real-time alerts. With this second integration, Datadog users can now export from NS1 key data points for analysis and management, including query use over time, top zones by query volume, and monthly account usage metrics. An out-of-the-box dashboard makes it fast and simple to get started with data visualization within the Datadog platform. Teams can also tap into NS1’s Pulsar real user monitoring and resource availability data for greater network visibility. With these combined solutions, customers can easily provide all of their DevOps, NetOps, and SecOps users the centralized visibility they need to monitor application delivery reliability and performance, troubleshoot issues, and effectively manage resources wherever their users and applications are located.

Michael Gerstenhaber, Sr. Director of Product at Datadog, said: “The critical first step for users to access a web application is a successful DNS lookup. Using the NS1 integration to optimize DNS queries, manage costs, and catch security issues like DDoS attacks, developers will be able to deliver great digital experiences from start to finish.”

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NS1 Integrates with Datadog

NS1 announced enhanced support for Datadog.

A new integration brings the power of real-time data and reporting to Datadog users for improved visibility and monitoring of applications at the distributed edge.

As IT complexity explodes, application and network teams are challenged to collect, visualize, and act upon the deluge of data from disparate sources. They need end-to-end visibility into real-time conditions and solutions that connect into existing workflows so that they can identify, triage, and troubleshoot issues quickly and make more informed decisions about application health and performance. Now, teams can monitor and report on NS1 services within the Datadog platform, providing a single source of truth for observability across the modern tech stack.

“With Datadog and NS1, companies can implement full-stack observability with automatic, real-time adjustments and insights to help navigate changing conditions across the internet, cloud, networks, and infrastructure,” said Danielle Russell, Director of Product Marketing at NS1. “NS1 now connects with more observability solutions so that customers can deliver consistently exceptional application experiences while driving efficiency and automation.”

This is NS1’s second integration with Datadog. The company’s first integration, released in 2016, allows customers to push Datadog monitoring data into the NS1 platform to automate application traffic policy decisions based on near-real-time alerts. With this second integration, Datadog users can now export from NS1 key data points for analysis and management, including query use over time, top zones by query volume, and monthly account usage metrics. An out-of-the-box dashboard makes it fast and simple to get started with data visualization within the Datadog platform. Teams can also tap into NS1’s Pulsar real user monitoring and resource availability data for greater network visibility. With these combined solutions, customers can easily provide all of their DevOps, NetOps, and SecOps users the centralized visibility they need to monitor application delivery reliability and performance, troubleshoot issues, and effectively manage resources wherever their users and applications are located.

Michael Gerstenhaber, Sr. Director of Product at Datadog, said: “The critical first step for users to access a web application is a successful DNS lookup. Using the NS1 integration to optimize DNS queries, manage costs, and catch security issues like DDoS attacks, developers will be able to deliver great digital experiences from start to finish.”

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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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...