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PagerDuty Integrates with Amazon DevOps Guru

PagerDuty announced a product collaboration with Amazon DevOps Guru, an operational insight service powered by machine learning (ML) from Amazon Web Services (AWS).

Through this new integration, PagerDuty will automatically ingest observability data from Amazon DevOps Guru. PagerDuty consolidates these digital health signals and alerts, and uses AIOps to contextualize and filter out the noise so teams can remediate issues in real-time, and customers can ensure critical business services get delivered.

Amazon DevOps Guru is a powerful yet simple to use native observability service. Tightly paired via the new integration with Amazon DevOps Guru, PagerDuty provides actionable insights and resolution, contextualized through ML algorithms, to the correct stakeholders.

The PagerDuty platform for real-time operations was built to ingest digital signals from across the entire enterprise ecosystem, and then arm the right responders with the right insights and tools to resolve issues in real-time. PagerDuty allows operations teams to improve the optics into their AWS environment and AWS-based applications. Leveraging Amazon DevOps Guru’s ML-enabled application health information, PagerDuty provides even more real-time signal-to-resolution capabilities to our shared customers. Through PagerDuty’s ingestion of Amazon Simple Notification Service (Amazon SNS) notifications on Amazon DevOps Guru, customers can seamlessly identify and action operational issues more quickly, before they become customer-impacting outages.

“This integration is a sign of where the industry is headed as the demand for deep observability grows,” said Jonathan Rende, SVP of Product at PagerDuty. “For cloud native companies, PagerDuty’s combination with Amazon DevOps Guru means powerful, simple, no-configuration visibility and machine learning that ensures application uptime and instant incident response. For non-cloud native companies, it enables PagerDuty to further unify digital operations across complex, hybrid, and non-cloud-based applications as they migrate onto the cloud, with less complexity, less technology, and much faster ROI.”

Users can benefit from better automation and a more complete picture of their environment. Health signals from Amazon DevOps Guru coupled with those from other observability tools, help PagerDuty’s AIOps noise reduction algorithms and automation capabilities to be more effective. The integration can also ensure cloud migration success by empowering teams to take real-time action on incidents that take place across your hybrid infrastructure. And, for teams who are “all in” on AWS, Amazon DevOps Guru’s out of the box app monitoring feeds can be ingested and made actionable by PagerDuty with almost no configuration needed. As a result, real-time incident response functionality is automatically added to customers’ app development lifecycle.

PagerDuty is also adding support for two other AWS services, focused on supporting cloud migration and removing noise from hybrid infrastructures. These build on the monitoring, security, and management and automation integrations already available through the platform.

The new integrations for AWS include:

- PagerDuty for AWS Control Tower: Gives organizations the power of service ownership by applying guardrails that will either auto-remediate compliance issues or escalate to the right person to handle it.

- PagerDuty for AWS Outposts: Extends the AWS infrastructure to virtually any datacenter, allowing organizations to manage incidents in real-time for AWS infrastructure used in a private datacenter, co-location space, or on-premises facility.

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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 ...

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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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

PagerDuty Integrates with Amazon DevOps Guru

PagerDuty announced a product collaboration with Amazon DevOps Guru, an operational insight service powered by machine learning (ML) from Amazon Web Services (AWS).

Through this new integration, PagerDuty will automatically ingest observability data from Amazon DevOps Guru. PagerDuty consolidates these digital health signals and alerts, and uses AIOps to contextualize and filter out the noise so teams can remediate issues in real-time, and customers can ensure critical business services get delivered.

Amazon DevOps Guru is a powerful yet simple to use native observability service. Tightly paired via the new integration with Amazon DevOps Guru, PagerDuty provides actionable insights and resolution, contextualized through ML algorithms, to the correct stakeholders.

The PagerDuty platform for real-time operations was built to ingest digital signals from across the entire enterprise ecosystem, and then arm the right responders with the right insights and tools to resolve issues in real-time. PagerDuty allows operations teams to improve the optics into their AWS environment and AWS-based applications. Leveraging Amazon DevOps Guru’s ML-enabled application health information, PagerDuty provides even more real-time signal-to-resolution capabilities to our shared customers. Through PagerDuty’s ingestion of Amazon Simple Notification Service (Amazon SNS) notifications on Amazon DevOps Guru, customers can seamlessly identify and action operational issues more quickly, before they become customer-impacting outages.

“This integration is a sign of where the industry is headed as the demand for deep observability grows,” said Jonathan Rende, SVP of Product at PagerDuty. “For cloud native companies, PagerDuty’s combination with Amazon DevOps Guru means powerful, simple, no-configuration visibility and machine learning that ensures application uptime and instant incident response. For non-cloud native companies, it enables PagerDuty to further unify digital operations across complex, hybrid, and non-cloud-based applications as they migrate onto the cloud, with less complexity, less technology, and much faster ROI.”

Users can benefit from better automation and a more complete picture of their environment. Health signals from Amazon DevOps Guru coupled with those from other observability tools, help PagerDuty’s AIOps noise reduction algorithms and automation capabilities to be more effective. The integration can also ensure cloud migration success by empowering teams to take real-time action on incidents that take place across your hybrid infrastructure. And, for teams who are “all in” on AWS, Amazon DevOps Guru’s out of the box app monitoring feeds can be ingested and made actionable by PagerDuty with almost no configuration needed. As a result, real-time incident response functionality is automatically added to customers’ app development lifecycle.

PagerDuty is also adding support for two other AWS services, focused on supporting cloud migration and removing noise from hybrid infrastructures. These build on the monitoring, security, and management and automation integrations already available through the platform.

The new integrations for AWS include:

- PagerDuty for AWS Control Tower: Gives organizations the power of service ownership by applying guardrails that will either auto-remediate compliance issues or escalate to the right person to handle it.

- PagerDuty for AWS Outposts: Extends the AWS infrastructure to virtually any datacenter, allowing organizations to manage incidents in real-time for AWS infrastructure used in a private datacenter, co-location space, or on-premises facility.

The Latest

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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...