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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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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

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 ...