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LogRocket Metrics Released

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.”

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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

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In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

LogRocket Metrics Released

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

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...