
New Relic announced New Relic Pathpoint, a business observability solution designed to bridge the data gap between IT and real-world business outcomes.
Pathpoint goes beyond conventional monitoring to provide users with real-time financial insights into every user touchpoint by modeling system-level telemetry from APM 360 in direct correlation to user-impacting business stages. Pathpoint helps engineering and IT teams drive operational efficiency, analyze the financial impact of issues, and align service performance to business outcomes. For example, users can gain insight into every stage of the customer journey including customer behaviors, transactions, search queries, product selection, processing times, and post-interaction activities. This full transparency into business processes and real-time metric reporting helps organizations build better customer experiences across all channels to maximize ROI.
Pathpoint allows engineers to alert both technical and business teams in near-real time if there is an unwanted change in their business metrics. Any engineer responding to the alert can quickly diagnose the reason for the change by using the New Relic all-in-one observability platform to identify the root cause that needs to be fixed. Pathpoint is also instrumental in helping technical teams provide their business counterparts with visibility into critical metrics that directly impact the business, such as revenue lost during an outage. This transparency allows business executives to also make data-driven decisions about software investments.
“New Relic Pathpoint increases collaboration across our teams and enables our engineering team with the technical insights needed to pinpoint and resolve issues faster. It provides our management and executive teams with the business-level insights needed to make more informed decisions based on specific KPIs and custom metrics, like tracking booking volume over a certain period of time or monitoring and analyzing search performance during peak hours,” said Trainline Site Reliability Engineer Sangeetha Niranjan. “This helps our entire organization, from the top down, work together to ensure our customers have the best experience possible at every stage—searching, booking, payment, and fulfillment—when finding and buying train tickets.”
Key capabilities and benefits include:
- Make business-impact based decisions: Assess the financial impact of system issues by viewing software performance alongside critical business metrics to make better decisions.
- Enhance customer experiences and revenue: Analyze system health in relation to actual user-impacting stages and conversion patterns to reduce churn and boost revenue.
- Minimize financial impact of downtime: Easily align, set, and monitor service-level objectives with your business goals for improved service performance that aligns with business priorities.
- Optimize resources and costs: Strategically group applications, services, and infrastructure according to business functions to prioritize budget/spend based on business value.
- Soon, use generative AI-powered insights: Use natural language prompts with New Relic AI (now in early access) to identify cost-saving opportunities and uncover hidden revenue potential easily.
“The stakes are high for digital businesses. When a software outage has the potential to cost you millions in lost revenue per hour, IT and engineering leaders simply need to understand the business impact of the software they build and operate,” said New Relic Product Officer Manav Khurana. “With Pathpoint, we are building upon our industry leadership to pioneer business process observability so that engineering leaders and business leaders can come together in every organization and make better-informed decisions based on complete data.”
Pathpoint is available as a New Relic open-source project, distributed under the Apache 2 license, and can be installed directly into the New Relic platform.
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.