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New Relic Releases Add-Ons for AWS Compute Optimizer, AWS Lambda Extensions, and AWS App Runner

New Relic announced support for AWS Compute Optimizer, AWS Lambda Extensions, and AWS App Runner to help engineers troubleshoot workflows and optimize and analyze containerized applications faster, easier, and more efficiently within New Relic’s all-in-one platform.

With these new integrations, customers reduce tooling and engineering costs while increasing time to market through an expanded set of AWS products and services as part of their overall full-stack observability strategy.

In 2020, New Relic announced a strategic collaboration agreement with AWS to help bring data-driven observability to millions of engineers and developers globally. Time and again, joint customers have benefited from an approach that leverages the open, connected, and programmable capabilities of the New Relic observability platform, enabling them to scale their application and infrastructure operations.

“By combining New Relic’s industry-leading observability platform with AWS, New Relic helps customers further de-risk and accelerate their cloud migration, modernization, and workload optimization initiatives on the cloud,” said New Relic GVP of Global Alliances and Channels Riya Shanmugam. “We’re committed to pushing the leading edge of innovation with AWS, simplifying observability in cloud environments, and supporting even bigger efficiency gains.”

The new AWS product support includes:

- AWS Compute Optimizer. New Relic allows customers to evaluate rightsizing recommendations, configure enhanced infrastructure metrics, and streamline migration to Amazon Elastic Compute Cloud (Amazon EC2) instances powered by AWS Graviton processors. New Relic helps customers understand the rightsizing effects on their applications and end-user experience, allowing quick feedback on cost-saving efforts.

- AWS Lambda Extensions. In the past, AWS allowed third-party tools like New Relic to ingest AWS Lambda logs directly to reduce cloud spend, saving costs for New Relic customers. AWS has now extended this functionality to all telemetry data types, including metrics, events, and traces. The AWS Lambda telemetry application programming interface (API) makes it simpler for New Relic customers to receive telemetry about AWS Lambda function invocation, such as runtime, tags, max memory, and timeout, enabling in-context visibility and speeding up application development.

- AWS App Runner. Customers can now use New Relic to monitor and optimize containerized applications, ensure they perform as expected, and validate that the App Runner service was deployed correctly. New Relic also collects metrics, events, and logs for complete visibility into containerized applications, providing users with telemetry to increase uptime and reliability.

With a single full-stack observability platform, joint New Relic and AWS customers need only one place to monitor, debug, and improve their entire stack. The solution correlates the customer experience — including web and mobile, application and infrastructure performance and availability — with AWS products and services in one platform. New Relic continues to invest in supporting the AWS infrastructure that its customers depend on to achieve faster, lower-risk migrations with compelling business outcomes.

The three new offerings are the latest in New Relic and AWS’s five-year strategic collaboration agreement, which has also featured New Relic for Startups on AWS Activate Console, New Relic for Amazon Elastic Kubernetes Service (Amazon EKS) with AWS Fargate, and Pixie on Amazon EKS.

New Relic continues to add additional features available to all of its AWS customers without additional per-host costs.

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 ...

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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 ...

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 Releases Add-Ons for AWS Compute Optimizer, AWS Lambda Extensions, and AWS App Runner

New Relic announced support for AWS Compute Optimizer, AWS Lambda Extensions, and AWS App Runner to help engineers troubleshoot workflows and optimize and analyze containerized applications faster, easier, and more efficiently within New Relic’s all-in-one platform.

With these new integrations, customers reduce tooling and engineering costs while increasing time to market through an expanded set of AWS products and services as part of their overall full-stack observability strategy.

In 2020, New Relic announced a strategic collaboration agreement with AWS to help bring data-driven observability to millions of engineers and developers globally. Time and again, joint customers have benefited from an approach that leverages the open, connected, and programmable capabilities of the New Relic observability platform, enabling them to scale their application and infrastructure operations.

“By combining New Relic’s industry-leading observability platform with AWS, New Relic helps customers further de-risk and accelerate their cloud migration, modernization, and workload optimization initiatives on the cloud,” said New Relic GVP of Global Alliances and Channels Riya Shanmugam. “We’re committed to pushing the leading edge of innovation with AWS, simplifying observability in cloud environments, and supporting even bigger efficiency gains.”

The new AWS product support includes:

- AWS Compute Optimizer. New Relic allows customers to evaluate rightsizing recommendations, configure enhanced infrastructure metrics, and streamline migration to Amazon Elastic Compute Cloud (Amazon EC2) instances powered by AWS Graviton processors. New Relic helps customers understand the rightsizing effects on their applications and end-user experience, allowing quick feedback on cost-saving efforts.

- AWS Lambda Extensions. In the past, AWS allowed third-party tools like New Relic to ingest AWS Lambda logs directly to reduce cloud spend, saving costs for New Relic customers. AWS has now extended this functionality to all telemetry data types, including metrics, events, and traces. The AWS Lambda telemetry application programming interface (API) makes it simpler for New Relic customers to receive telemetry about AWS Lambda function invocation, such as runtime, tags, max memory, and timeout, enabling in-context visibility and speeding up application development.

- AWS App Runner. Customers can now use New Relic to monitor and optimize containerized applications, ensure they perform as expected, and validate that the App Runner service was deployed correctly. New Relic also collects metrics, events, and logs for complete visibility into containerized applications, providing users with telemetry to increase uptime and reliability.

With a single full-stack observability platform, joint New Relic and AWS customers need only one place to monitor, debug, and improve their entire stack. The solution correlates the customer experience — including web and mobile, application and infrastructure performance and availability — with AWS products and services in one platform. New Relic continues to invest in supporting the AWS infrastructure that its customers depend on to achieve faster, lower-risk migrations with compelling business outcomes.

The three new offerings are the latest in New Relic and AWS’s five-year strategic collaboration agreement, which has also featured New Relic for Startups on AWS Activate Console, New Relic for Amazon Elastic Kubernetes Service (Amazon EKS) with AWS Fargate, and Pixie on Amazon EKS.

New Relic continues to add additional features available to all of its AWS customers without additional per-host costs.

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.