LogDNA launched a new browser logging capability, which makes it easier for full-stack and frontend developers to ingest frontend log data in LogDNA to more efficiently debug web applications.
The code running on end-user devices has become increasingly complex, elevating the need for frontend logging capabilities. Although there are a multitude of observability tools and services for logging backend and server-based applications, there is a notable gap in visibility for client-side applications. LogDNA’s new Browser Logger addresses this need by automatically capturing errors and logs occurring in the user’s browser and allowing dev teams to centralize those errors alongside server-side logs. Full-stack and frontend developers gain valuable log data from the browser that can be used to effectively debug client-side errors.
“Customers struggle to extend their observability stack into their frontend web applications to monitor for client-side errors and collect critical debugging information. This makes it difficult to identify when problems occur, and troubleshooting errors becomes difficult and time-consuming,” said Peter Cho, VP of Product, LogDNA. “LogDNA Browser Logger makes it quick and painless to find and fix issues that span between frontend and backend applications so developers can spend less time debugging and more time on value-adding tasks.”
With this new data, developers can see errors with stack traces and correlate these errors with a specific application release or browser version. They can also log performance metrics in real time to discover how long specific user interface (UI) functionality takes on a customer’s device. As a result, developers are empowered to take immediate action to improve the performance of their web applications to deliver the best user experiences possible.
Combined with LogDNA’s extensive list of supported ingestion sources, this feature gives developers the information they need to better understand what’s happening at every layer of their applications. For example, Kubernetes shops can use Browser Logger to see frontend metrics, and the Kubernetes Enrichment feature to see Kubernetes events and metrics. Having visibility from the frontend app layer all the way down to the container orchestration layer is essential for teams working with a DevOps mindset where the same group is in charge of building, deploying, and maintaining applications.
With a higher level of granularity in tracking events and a lower cost per event compared to alternatives, LogDNA’s Browser Logger empowers developers to take immediate action to improve the performance of their web applications to deliver the best user experiences possible.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
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 ...