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
For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...
Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...
AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...
Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...
Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...
When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...
In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...
Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...
AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...
Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...