Embrace has expanded its product suite beyond mobile with the introduction of Real User Monitoring (RUM) for Web.
Built on OpenTelemetry and informed by Embrace's foundational Mobile RUM product, the launch reinforces the company's dedication to reliability from the perspective of end-users, regardless of screen.
The expansion is supported by Embrace's existing investors NEA, Greycroft, AV8 (Allianz), and Eniac who back the company's Web RUM initiative and vision to scale the only user-focused observability platform based on the OpenTelemetry standard.
Embrace provides out-of-the-box solutions to help engineers make sense of site performance data and its impact on users. Teams can connect telemetry to what users are actually experiencing, offering clarity on what's broken, why it matters, and how to fix it. From web performance regressions to mobile crashes affecting high-value users, Embrace helps prioritize the issues that impact people and revenue.
"Our customers wanted to see our product approach applied to the web, where we record individual end-user play-by-plays and use those to detect the types of regressions and issues that impact user engagement," said Andrew Tunall, President and Chief Product Officer at Embrace. "With the addition of Web RUM to our premier mobile product, we're empowering teams to understand reliability and performance exactly as their users experience it, on any device."
Embrace's platform brings performance details, user context, and business-critical insight to the surface so frontend engineers and reliability teams can prioritize their work rather than wade through technical noise.
With Web RUM by Embrace, teams get:
- Full session timelines for deep insights into the user experience
- Core Web Vitals connected to the full technical details in each session
- Exceptions and error reporting with advanced troubleshooting tools
- Custom metrics and alerting around key flows and events
- OpenTelemetry-native ecosystem with semantic conventions for cross-system analysis and data portability
Now, engineers can improve the reliability of user experiences by connecting mobile, web, and backend systems with shared context, standards for instrumentation and conventions, and powerful cross-team 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 ...