LogRocket announced the launch of LogRocket Metrics, the next step toward a complete front-end APM platform.
LogRocket introduces LogRocket Metrics as an APM solution built specifically to understand the complex issues that arise in web applications. It ties together session replay and APM in an easy-to-use dashboarding solution that anyone can use, regardless of technical ability.
LogRocket Metrics enables teams to understand how performance is affected by factors including: network requests, JavaScript execution, local resource access, CPU load, and memory usage whether it comes from the back end, CDN layer, internet connectivity, JavaScript performance, client devices, or elsewhere.
When it comes to understanding the impact, traditional APM tools require IT or product teams to write code to define a user transaction and measure performance. Even after the transaction is defined, companies still have to wait days or weeks to gather enough data to have statistical significance. With LogRocket Metrics, teams can easily define any user transaction in the UI and instantly see retroactive data. This allows them to understand how an issue affects customers so that it can be prioritized and addressed accordingly. Rapid resolution can result in increased sales due to a superior user experience versus losing customers who grow frustrated waiting for a page to load or transaction to be completed.
“More and more we’re seeing that every company is now a software company, relying on software for their core business. We are on a mission to help those companies make their web experiences as perfect as possible,” said LogRocket CEO, Matthew Arbesfeld. “By addressing performance on the front-end, we make it far easier for companies to identify and fix issues before they impact potential customers and cost companies sales.”
LogRocket also announced that it has raised $15 million in Series B funding. The round, like its recent Series A, was led by global investment firm Battery Ventures, with participation from seed investor Matrix Partners. Funds will be used to continue growing headcount, anticipated to more than double in the coming year, as LogRocket scales to meet demand. The company will also invest in continuing to expand the functionality of its first-of-its-kind solution, which offers companies unparalleled insight into user experience problems in their web applications.
“Monitoring front-end and back-end applications are very different processes, and must be seen as such. The LogRocket team understands this and is taking a fundamentally different approach,” said Neeraj Agrawal, General Partner at Battery Ventures. “LogRocket effectively addresses a problem developers have tried to solve for a very long time. Its technology makes APM totally painless in today’s environments. The team also has bold future plans to help companies stay ahead, and we are excited to work with them to execute.”
The Latest
Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...
Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...
One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...
As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...
Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...
The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...
Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...
Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to third party services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...
If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...
Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...