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New Relic Infrastructure Introduced

New Relic unveiled New Relic Infrastructure, a new monitoring solution designed to provide deep, real-time visibility into a company’s dynamic cloud and hybrid infrastructure and integrate seamlessly with New Relic’s application performance solutions.

New Relic Infrastructure enables correlation of performance metrics and configuration changes to the infrastructure, enabling modern IT operations teams to move faster, scale and deploy with confidence.

The pressure on IT operations teams has been intensifying as new technologies and processes are being adopted in their organizations. The move to the cloud and container technology is radically changing the way services are configured, deployed, and scaled. Agile development is accelerating the rate of deploys and change in the software stack. Organizations are embracing DevOps culture, enabling more people to interact with production applications - all of which is leading to a more rapidly changing environment where teams need richer and more real-time data to quickly troubleshoot problems and reduce downtime.

New Relic Infrastructure is specifically designed to support the needs of modern operations teams by processing, displaying, alerting, and analyzing all the change and performance data required to manage and optimize cloud and hybrid infrastructures. Built on New Relic Insights, a scalable cloud-based data platform and powerful analytics engine, New Relic Infrastructure enables teams to slice and dice their infrastructure change and performance data in real-time. With New Relic Infrastructure, IT operations and release managers can:

● Have a single source for live-state monitoring and change tracking of all their instances (any combination of cloud, containers, or traditional servers) by providing visibility into CPU, memory, I/O, and the various processes running on them along with a correlated change history.

● Easily examine the inventory and any changes made to their instances via a real-time feed and identify anomalies or vulnerabilities quickly with full infrastructure search.

● Achieve faster Mean Time To Detection (MTTD) and Resolution (MTTR) with correlated metrics and change events via dashboards and alerting that are dynamically driven by tags and metadata from cloud, automation tools, or custom attributes.

● Native monitoring of Amazon EC2 and Docker with support for Amazon EC2 and Docker tags along with detailed metadata, enabling them to effectively dissect, analyze and manage instances or containers by any attribute.

“The cloud has changed the requirements for modern infrastructure management. The notion of a static server is fading, replaced with dynamic instances and containers,” said Lew Cirne, founder and CEO, New Relic. “New Relic Infrastructure provides the underlying systems context to New Relic APM and Insights, empowering software teams to measure the health and state of cloud infrastructure within the context of their applications. Fast-moving, dynamic digital businesses will now have real-time analytics on change and performance data to help them deliver operational stability, move confidently, and innovate with their software.“

New Relic Infrastructure is available today in a private beta and is expected to be generally available to customers later this year.

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 ...

New Relic Infrastructure Introduced

New Relic unveiled New Relic Infrastructure, a new monitoring solution designed to provide deep, real-time visibility into a company’s dynamic cloud and hybrid infrastructure and integrate seamlessly with New Relic’s application performance solutions.

New Relic Infrastructure enables correlation of performance metrics and configuration changes to the infrastructure, enabling modern IT operations teams to move faster, scale and deploy with confidence.

The pressure on IT operations teams has been intensifying as new technologies and processes are being adopted in their organizations. The move to the cloud and container technology is radically changing the way services are configured, deployed, and scaled. Agile development is accelerating the rate of deploys and change in the software stack. Organizations are embracing DevOps culture, enabling more people to interact with production applications - all of which is leading to a more rapidly changing environment where teams need richer and more real-time data to quickly troubleshoot problems and reduce downtime.

New Relic Infrastructure is specifically designed to support the needs of modern operations teams by processing, displaying, alerting, and analyzing all the change and performance data required to manage and optimize cloud and hybrid infrastructures. Built on New Relic Insights, a scalable cloud-based data platform and powerful analytics engine, New Relic Infrastructure enables teams to slice and dice their infrastructure change and performance data in real-time. With New Relic Infrastructure, IT operations and release managers can:

● Have a single source for live-state monitoring and change tracking of all their instances (any combination of cloud, containers, or traditional servers) by providing visibility into CPU, memory, I/O, and the various processes running on them along with a correlated change history.

● Easily examine the inventory and any changes made to their instances via a real-time feed and identify anomalies or vulnerabilities quickly with full infrastructure search.

● Achieve faster Mean Time To Detection (MTTD) and Resolution (MTTR) with correlated metrics and change events via dashboards and alerting that are dynamically driven by tags and metadata from cloud, automation tools, or custom attributes.

● Native monitoring of Amazon EC2 and Docker with support for Amazon EC2 and Docker tags along with detailed metadata, enabling them to effectively dissect, analyze and manage instances or containers by any attribute.

“The cloud has changed the requirements for modern infrastructure management. The notion of a static server is fading, replaced with dynamic instances and containers,” said Lew Cirne, founder and CEO, New Relic. “New Relic Infrastructure provides the underlying systems context to New Relic APM and Insights, empowering software teams to measure the health and state of cloud infrastructure within the context of their applications. Fast-moving, dynamic digital businesses will now have real-time analytics on change and performance data to help them deliver operational stability, move confidently, and innovate with their software.“

New Relic Infrastructure is available today in a private beta and is expected to be generally available to customers later this year.

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 ...