Skip to main content

SmartBear Adds Real User Monitoring in BugSnag

SmartBear released real user monitoring (RUM) in BugSnag, the company’s observability and application stability solution, that delivers production visibility insights to developers as they monitor and improve their applications with digital experience monitoring.

These new capabilities enable application teams to rapidly identify and prioritize performance issues in production with the context required for faster resolution and quicker application development.

“Performance issues in mobile and web applications can seriously impact end user satisfaction and ultimately the successful adoption of your product,” said Anthony Bryce, VP of Product Management at SmartBear. “To be successful, development teams must continually observe and optimize the performance of their applications. As developers take on more responsibilities to understand the impact of poor user experiences, we are leveraging our strength in error monitoring to provide modern development teams with 24/7 performance monitoring of their applications with real-world data to identify, prioritize, and resolve performance issues with confidence.”

BugSnag with RUM dynamically samples performance data on a daily basis so development teams can control their operational costs and never unintentionally go over the data volume in which they choose to pay. The way BugSnag collects data is also OpenTelemetry compliant, enabling customers to utilize the collected data with other OTel-based observability solutions.

BugSnag with real user monitoring provides an intuitive UI with dashboard overviews of key metrics such as app start up times, page loads, and web vitals. It also provides timeline views to identify performance trends, span filters by stage, release, and more, as well as waterfall views of performance traces to zero in on specific performance issues. Its rapid identification and resolution of performance issues means higher performing apps, increased customer engagement, and customer loyalty.

The Latest

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 ...

SmartBear Adds Real User Monitoring in BugSnag

SmartBear released real user monitoring (RUM) in BugSnag, the company’s observability and application stability solution, that delivers production visibility insights to developers as they monitor and improve their applications with digital experience monitoring.

These new capabilities enable application teams to rapidly identify and prioritize performance issues in production with the context required for faster resolution and quicker application development.

“Performance issues in mobile and web applications can seriously impact end user satisfaction and ultimately the successful adoption of your product,” said Anthony Bryce, VP of Product Management at SmartBear. “To be successful, development teams must continually observe and optimize the performance of their applications. As developers take on more responsibilities to understand the impact of poor user experiences, we are leveraging our strength in error monitoring to provide modern development teams with 24/7 performance monitoring of their applications with real-world data to identify, prioritize, and resolve performance issues with confidence.”

BugSnag with RUM dynamically samples performance data on a daily basis so development teams can control their operational costs and never unintentionally go over the data volume in which they choose to pay. The way BugSnag collects data is also OpenTelemetry compliant, enabling customers to utilize the collected data with other OTel-based observability solutions.

BugSnag with real user monitoring provides an intuitive UI with dashboard overviews of key metrics such as app start up times, page loads, and web vitals. It also provides timeline views to identify performance trends, span filters by stage, release, and more, as well as waterfall views of performance traces to zero in on specific performance issues. Its rapid identification and resolution of performance issues means higher performing apps, increased customer engagement, and customer loyalty.

The Latest

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 ...