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New Relic Delivers Distributed Tracing

New Relic announced the general availability of New Relic distributed tracing.

Customers are leveraging New Relic distributed tracing to better understand the performance of applications running in modern, complex architectures.

As more and more companies increasingly adopt distributed application architectures, the ability to track the performance of a single request across all the services and microservices involved becomes essential. New Relic’s distributed tracing provides DevOps teams with the ability to trace the path of a single request to understand a complex system, discover what is causing latencies for that request, find where an error originated, and identify opportunities to optimize code to improve customer experience.

Distributed tracing is an important addition to the New Relic platform, designed to give software teams an easy way to manage the performance of modern environments.

Key Features of New Relic’s Distributed Tracing

- Automatic instrumentation to get up and running quickly – features built-in trace instrumentation for hundreds of frameworks and libraries out-of-the-box, so customers see immediate value, and avoid the toil of having to manually instrument their code.

- Depth of detail across modern and traditional systems - the combination of distributed tracing, in conjunction with New Relic’s existing tracing functionality, is purpose-built for organizations that are in the process of transitioning to microservices environments. New Relic offers both tracing solutions in one powerful platform that delivers visibility into infrastructure, application, and customer experience.

- Powerful analytics to quickly find root causes – DevOps teams can easily add custom attributes to traces so they can narrow problems down to individual customers, accounts, or any other dimension of their business using advanced filtering. They can also create dashboards using trace information filtered to specific attributes.

- Delivers immediate value – New Relic’s SaaS platform delivers value as soon as the agent is deployed. There is no infrastructure to provision, secure, or run. DevOps teams can focus on delivering software for their customers, not instrumenting and building their monitoring solution.

“With our flexible, out-of-the-box instrumentation and deep performance data, New Relic’s distributed tracing empowers fast-moving DevOps teams to cut through the complexity of modern architectures,” said Nadya Duke Boone, VP of Product Management, New Relic. “Our customers don’t have to choose between a solution with automatic or manual instrumentation; New Relic delivers both, integrated with all the other tools and data in the New Relic platform essential for monitoring modern software environments.”

New Relic distributed tracing is now available to all APM Pro customers.

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New Relic Delivers Distributed Tracing

New Relic announced the general availability of New Relic distributed tracing.

Customers are leveraging New Relic distributed tracing to better understand the performance of applications running in modern, complex architectures.

As more and more companies increasingly adopt distributed application architectures, the ability to track the performance of a single request across all the services and microservices involved becomes essential. New Relic’s distributed tracing provides DevOps teams with the ability to trace the path of a single request to understand a complex system, discover what is causing latencies for that request, find where an error originated, and identify opportunities to optimize code to improve customer experience.

Distributed tracing is an important addition to the New Relic platform, designed to give software teams an easy way to manage the performance of modern environments.

Key Features of New Relic’s Distributed Tracing

- Automatic instrumentation to get up and running quickly – features built-in trace instrumentation for hundreds of frameworks and libraries out-of-the-box, so customers see immediate value, and avoid the toil of having to manually instrument their code.

- Depth of detail across modern and traditional systems - the combination of distributed tracing, in conjunction with New Relic’s existing tracing functionality, is purpose-built for organizations that are in the process of transitioning to microservices environments. New Relic offers both tracing solutions in one powerful platform that delivers visibility into infrastructure, application, and customer experience.

- Powerful analytics to quickly find root causes – DevOps teams can easily add custom attributes to traces so they can narrow problems down to individual customers, accounts, or any other dimension of their business using advanced filtering. They can also create dashboards using trace information filtered to specific attributes.

- Delivers immediate value – New Relic’s SaaS platform delivers value as soon as the agent is deployed. There is no infrastructure to provision, secure, or run. DevOps teams can focus on delivering software for their customers, not instrumenting and building their monitoring solution.

“With our flexible, out-of-the-box instrumentation and deep performance data, New Relic’s distributed tracing empowers fast-moving DevOps teams to cut through the complexity of modern architectures,” said Nadya Duke Boone, VP of Product Management, New Relic. “Our customers don’t have to choose between a solution with automatic or manual instrumentation; New Relic delivers both, integrated with all the other tools and data in the New Relic platform essential for monitoring modern software environments.”

New Relic distributed tracing is now available to all APM Pro customers.

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...