New Relic announced a set of new features across the New Relic Software Analytics Cloud that offer IT operations teams increased visibility, and the ability to diagnose and resolve performance problems quickly.
The new features announced today further IT operations teams’ ability to leverage data and analytics, as well as drive collaboration and a common, shared understanding between teams.
The New Relic Software Analytics Cloud provides a comprehensive monitoring platform allowing software teams to work together to monitor from different perspectives for any technology stack. For IT operations this includes proactively monitoring and identifying issues that may impact the performance of critical applications, understanding which customers may be impacted, and enabling them to collaborate with developers and DevOps teams to address an issue.
“The New Relic Software Analytics Cloud was developed with the belief that sharing data between development and operations teams also drives a shared understanding by removing subjectivity, enabling clearer accountability and fundamentally changing the way teams interact and collaborate,” said Bharath Gowda, Senior Director, Product Marketing, New Relic. “These new features for New Relic APM, New Relic Synthetics, and New Relic Browser provide even more comprehensive data and analytics capabilities for not only the technical stack, but also the customer experience, which can empower software teams to build and run high quality software with less risk.”
Generally available today, these features include:
New Relic APM
● Increased Visibility within Service Maps. IT operations teams now can easily and quickly understand how the performance of a single service node is impacting the entire system without leaving the Service Map. With a single click, an inline chart appears for that node, allowing the team member to see key performance metrics, as they begin their investigation.
New Relic Synthetics
● New Public Locations. With five additional locations, New Relic has expanded the geographic footprint customers can run synthetics scripts to monitor and test their site across the United States and Europe.
● Private Locations. Now Generally Available. Announced at FutureStack15, Private Locations allow operations teams to run synthetics scripts from any location by installing an appliance to monitor:
- Internal applications hosted behind a firewall.
- Performance close to the company’s major markets or customers.
● New REST API. New testing and deployment workflows are now possible with the release of the new Synthetics API, allowing monitors to be automatically updated as part of a build pipeline for a more integrated application release process. The new API can also be used to better manage thousands of monitors at production scale, with programmatic control to quickly and easily setup, configure, and manage monitors across application portfolios.
New Relic Browser
● Filterable Geography Now Generally Available. First demonstrated at FutureStack15, New Relic’s analytics engine is now embedded within New Relic Browser, and powers an interactive global map that enables IT operations professionals to:
- Filter data by Page URL, ASN Organization/Internet Service Provider, Browser Type, User Agent Operating System, Session, and more.
- Graphically view high-traffic areas at the country, region, or city level.
- View network timing data, including DNS Lookup Duration, Connection Establishment Duration, and Secure Handshake Duration.
With the exception of New Relic Synthetics Private Locations, the capabilities announced today are generally available to paying New Relic customers.
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.