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Moogsoft Integrates with PagerDuty

Moogsoft announced a joint solution with PagerDuty that surfaces incidents and insights about them to the right teams in real time for faster remediation and continuous service assurance.

This new bidirectional integration sends critical incidents surfaced by Moogsoft AIOps directly into PagerDuty, and to the exact people that need to take action, as well as the teams and people that need to be informed. DevOps, SREs and IT Ops teams can then collaborate from either platform with contextual and proactive insights—like Moogsoft’s similar historical incidents—to quickly identify and resolve both new and subsequent related issues.

DevOps teams and SREs often struggle to achieve clear insights and awareness of operational challenges across their applications, infrastructure, and ultimately business services. Gaining this critical understanding of incidents and their impact before customers do requires surfacing important events from noise, understanding the relationships between alerts and obtaining the context needed to engage the right teams and people.

“The scale and complexity of IT powering today’s digital economy produces more data than traditional operations management approaches can support,” said Jonathan Rende, SVP at PagerDuty. “Integrating the Moogsoft AIOps platform with PagerDuty helps DevOps and SREs build better ways to identify critical incidents across cloud workloads, cloud-native applications, containers, microservices, and serverless computing, and prevent those from becoming brand-damaging outages, hence further advancing our ‘Virtualize the NOC’ initiative.”

“Integrating the Moogsoft and PagerDuty platforms let our mutual customers eliminate the drudgery of traditional IT Operations, and better collaborate to achieve both constant change and zero downtime,” said Moogsoft Founder and CEO Phil Tee. “IT Ops and DevOps teams can now work better together to improve service delivery quality while they watch alert volumes go down and see productivity go up.”

This integrated solution helps SRE’s and DevOps teams:

- Detect anomalies and key events from observability data

- Increase productivity by surfacing significant alerts and reducing noise

- Identify incidents before they impact business services by engaging the right people and teams when seconds matter

- Gain the context needed to understand what’s happening and respond correctly using a variety of options including Acknowledge and Escalate

- Keep global teams in sync and better collaborate by adding comments and notes from PagerDuty and Moogsoft to share a consistent view,

- Communicate and report more efficiently, using the insights and visualizations gathered in the Moogsoft situation room

- Streamline post-mortems and accelerate future response using Moogsoft’s similar incidents to spot related problems and PagerDuty’s simple Post-Mortem Process

The Latest

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 ...

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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 ...

Moogsoft Integrates with PagerDuty

Moogsoft announced a joint solution with PagerDuty that surfaces incidents and insights about them to the right teams in real time for faster remediation and continuous service assurance.

This new bidirectional integration sends critical incidents surfaced by Moogsoft AIOps directly into PagerDuty, and to the exact people that need to take action, as well as the teams and people that need to be informed. DevOps, SREs and IT Ops teams can then collaborate from either platform with contextual and proactive insights—like Moogsoft’s similar historical incidents—to quickly identify and resolve both new and subsequent related issues.

DevOps teams and SREs often struggle to achieve clear insights and awareness of operational challenges across their applications, infrastructure, and ultimately business services. Gaining this critical understanding of incidents and their impact before customers do requires surfacing important events from noise, understanding the relationships between alerts and obtaining the context needed to engage the right teams and people.

“The scale and complexity of IT powering today’s digital economy produces more data than traditional operations management approaches can support,” said Jonathan Rende, SVP at PagerDuty. “Integrating the Moogsoft AIOps platform with PagerDuty helps DevOps and SREs build better ways to identify critical incidents across cloud workloads, cloud-native applications, containers, microservices, and serverless computing, and prevent those from becoming brand-damaging outages, hence further advancing our ‘Virtualize the NOC’ initiative.”

“Integrating the Moogsoft and PagerDuty platforms let our mutual customers eliminate the drudgery of traditional IT Operations, and better collaborate to achieve both constant change and zero downtime,” said Moogsoft Founder and CEO Phil Tee. “IT Ops and DevOps teams can now work better together to improve service delivery quality while they watch alert volumes go down and see productivity go up.”

This integrated solution helps SRE’s and DevOps teams:

- Detect anomalies and key events from observability data

- Increase productivity by surfacing significant alerts and reducing noise

- Identify incidents before they impact business services by engaging the right people and teams when seconds matter

- Gain the context needed to understand what’s happening and respond correctly using a variety of options including Acknowledge and Escalate

- Keep global teams in sync and better collaborate by adding comments and notes from PagerDuty and Moogsoft to share a consistent view,

- Communicate and report more efficiently, using the insights and visualizations gathered in the Moogsoft situation room

- Streamline post-mortems and accelerate future response using Moogsoft’s similar incidents to spot related problems and PagerDuty’s simple Post-Mortem Process

The Latest

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 ...