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Elastic Announces User Experience Monitoring and Synthetics

Elastic announced expanded Elastic Observability features including user experience monitoring and synthetics to give developers new tools to test, measure, and optimize end-user digital experiences.

With the launch of a new dedicated User Experience app in Kibana, Elastic users can get a better view and understanding of how end users experience their websites.

The new User Experience app provides expanded insight into well-known performance metrics like paint time, long task duration, and total blocking time.

In addition, Elastic users can leverage the new user experience monitoring feature to review Core Web Vitals to help website developers interpret digital experience signals.

Elastic users can also leverage a dev preview release of synthetic monitoring with multistep journey tests in Elastic Uptime. This new feature enables customers to simulate complex user flows, measure performance, and optimize new interaction paths without impact to a website’s end users.

Elastic Observability customers benefit from continued updates to features that use machine learning to automate or improve common observability workflows. With this launch, customers can leverage turnkey anomaly detection for infrastructure health monitoring, making it easy to detect common infrastructure issues across thousands of resources.

"With so much of our lives moving online – from work, to school, to even our social lives – it is vital that organizations have a comprehensive view of the digital experiences of their end users," said Tanya Bragin, VP, Product Management, Elastic. "The new user experience monitoring and expanded synthetic capabilities in Elastic Observability help our customers track how their end users are experiencing their digital service, arming them with the data to prioritize optimizations with the biggest impact on the business. We are introducing both of these market-leading features in our free and open distribution tier, making it easy for developers to adopt into their workflows."

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Elastic Announces User Experience Monitoring and Synthetics

Elastic announced expanded Elastic Observability features including user experience monitoring and synthetics to give developers new tools to test, measure, and optimize end-user digital experiences.

With the launch of a new dedicated User Experience app in Kibana, Elastic users can get a better view and understanding of how end users experience their websites.

The new User Experience app provides expanded insight into well-known performance metrics like paint time, long task duration, and total blocking time.

In addition, Elastic users can leverage the new user experience monitoring feature to review Core Web Vitals to help website developers interpret digital experience signals.

Elastic users can also leverage a dev preview release of synthetic monitoring with multistep journey tests in Elastic Uptime. This new feature enables customers to simulate complex user flows, measure performance, and optimize new interaction paths without impact to a website’s end users.

Elastic Observability customers benefit from continued updates to features that use machine learning to automate or improve common observability workflows. With this launch, customers can leverage turnkey anomaly detection for infrastructure health monitoring, making it easy to detect common infrastructure issues across thousands of resources.

"With so much of our lives moving online – from work, to school, to even our social lives – it is vital that organizations have a comprehensive view of the digital experiences of their end users," said Tanya Bragin, VP, Product Management, Elastic. "The new user experience monitoring and expanded synthetic capabilities in Elastic Observability help our customers track how their end users are experiencing their digital service, arming them with the data to prioritize optimizations with the biggest impact on the business. We are introducing both of these market-leading features in our free and open distribution tier, making it easy for developers to adopt into their workflows."

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 ...