Skip to main content

New Relic Software Analytics Cloud Delivers AWS Context for Application Monitoring

New Relic announced a new set of features from across the New Relic Software Analytics Cloud that are designed to be an easy and powerful way to specifically monitor development, pre-deployment and production application health and performance on Amazon Elastic Compute Cloud (EC2).

Additionally, New Relic has achieved the Amazon Web Services (AWS) Partner Network (APN) Mobile Competency, which recognizes APN Partners that have deep experience with mobile-first development.

Currently more than half of New Relic’s customers report data from an AWS environment to New Relic through application and server monitoring. These new features for the New Relic Software Analytics Cloud are designed to deliver powerful data to help developers and IT operations teams migrate their applications to Amazon EC2 and then optimize their performance. By enabling customers to marry performance data from both the application and cloud infrastructure layers of their technology stack, the New Relic Software Analytics Cloud can help customers manage large Amazon EC2 deployments with complex lifecycles. The aim of this application-centric approach is to empower customers to better leverage the speed, focus, and economics of the AWS Cloud.

The new AWS monitoring features in the New Relic Software Analytics Cloud are designed to provide customers with the following capabilities and benefits:

● Monitoring with Context: Utilize the wealth of data in your Amazon EC2 instances by bringing in the customized AWS tags you authored and AWS metadata (AWS Region, instance size, AMI, and other factors) and realize them all as Server Labels in New Relic. This enables you to group and filter, troubleshoot and drill into problem instances, across regions, which can be essential for companies utilizing a large number of Amazon EC2 instances.

● Manage Any Instance Lifespan: Monitor your Amazon EC2 instances in a way that uniquely matches the way companies may utilize Amazon EC2, with some instances existing for mere minutes. When an instance is de-provisioned, these features would identify that action and recognize it no longer exists, aiming to reduce UI clutter so team members can focus on the instances in use.

● Understand Your Monitoring Coverage: Quickly see which Amazon EC2 instances are not being monitored by New Relic so you can take steps to shine a light on potentially unknown instances and applications.

● Monitor in Minutes: New Relic customers can begin monitoring their Amazon EC2 instances in a matter of minutes, through a new streamlined process that uses the AWS Identity and Access Management (IAM), the AWS standard and secure approach for sharing such information.

● Migrate with Confidence: New AWS monitoring features in the New Relic Software Analytics Cloud aim to give you more insight and confidence to migrate your apps to any availability zone on the AWS Cloud. View the performance of application code, as well as the Amazon EC2 instances that underpin them.

“Amazon Web Services is removing infrastructure distractions for developers so the focus can now be on their application, and New Relic’s mission is to support the speed, focus, and economics afforded to modern application teams in the cloud,” said Patrick Lightbody, Group VP, Product Management at New Relic. “With more than half of our customers sending us data from AWS environments, our goal for the New Relic Software Analytics Cloud is to continue to help companies scale and manage the lifecycle of their Amazon EC2 usage at any point of their cloud journey.”

The new AWS monitoring features in the New Relic Software Analytics Cloud are currently available in private beta.

The Latest

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 ...

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.

New Relic Software Analytics Cloud Delivers AWS Context for Application Monitoring

New Relic announced a new set of features from across the New Relic Software Analytics Cloud that are designed to be an easy and powerful way to specifically monitor development, pre-deployment and production application health and performance on Amazon Elastic Compute Cloud (EC2).

Additionally, New Relic has achieved the Amazon Web Services (AWS) Partner Network (APN) Mobile Competency, which recognizes APN Partners that have deep experience with mobile-first development.

Currently more than half of New Relic’s customers report data from an AWS environment to New Relic through application and server monitoring. These new features for the New Relic Software Analytics Cloud are designed to deliver powerful data to help developers and IT operations teams migrate their applications to Amazon EC2 and then optimize their performance. By enabling customers to marry performance data from both the application and cloud infrastructure layers of their technology stack, the New Relic Software Analytics Cloud can help customers manage large Amazon EC2 deployments with complex lifecycles. The aim of this application-centric approach is to empower customers to better leverage the speed, focus, and economics of the AWS Cloud.

The new AWS monitoring features in the New Relic Software Analytics Cloud are designed to provide customers with the following capabilities and benefits:

● Monitoring with Context: Utilize the wealth of data in your Amazon EC2 instances by bringing in the customized AWS tags you authored and AWS metadata (AWS Region, instance size, AMI, and other factors) and realize them all as Server Labels in New Relic. This enables you to group and filter, troubleshoot and drill into problem instances, across regions, which can be essential for companies utilizing a large number of Amazon EC2 instances.

● Manage Any Instance Lifespan: Monitor your Amazon EC2 instances in a way that uniquely matches the way companies may utilize Amazon EC2, with some instances existing for mere minutes. When an instance is de-provisioned, these features would identify that action and recognize it no longer exists, aiming to reduce UI clutter so team members can focus on the instances in use.

● Understand Your Monitoring Coverage: Quickly see which Amazon EC2 instances are not being monitored by New Relic so you can take steps to shine a light on potentially unknown instances and applications.

● Monitor in Minutes: New Relic customers can begin monitoring their Amazon EC2 instances in a matter of minutes, through a new streamlined process that uses the AWS Identity and Access Management (IAM), the AWS standard and secure approach for sharing such information.

● Migrate with Confidence: New AWS monitoring features in the New Relic Software Analytics Cloud aim to give you more insight and confidence to migrate your apps to any availability zone on the AWS Cloud. View the performance of application code, as well as the Amazon EC2 instances that underpin them.

“Amazon Web Services is removing infrastructure distractions for developers so the focus can now be on their application, and New Relic’s mission is to support the speed, focus, and economics afforded to modern application teams in the cloud,” said Patrick Lightbody, Group VP, Product Management at New Relic. “With more than half of our customers sending us data from AWS environments, our goal for the New Relic Software Analytics Cloud is to continue to help companies scale and manage the lifecycle of their Amazon EC2 usage at any point of their cloud journey.”

The new AWS monitoring features in the New Relic Software Analytics Cloud are currently available in private beta.

The Latest

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 ...

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.