
Embrace signed a go-to-market partnership agreement with Grafana Labs to bring modern mobile app observability based on OpenTelemetry to users of Grafana Labs' products for full stack observability.
Embrace will partner with Grafana Labs to tie frontend mobile telemetry to backend performance data. This partnership will enable a cohesive workflow for SREs and DevOps to understand end-user impact and health, while giving mobile developers the tools to prevent issues that reduce user engagement and increase churn.
Embrace and Grafana Labs will enable a cohesive workflow for SREs and DevOps to understand end-user impact and health of mobile applications.
"As mobile continues to dominate the way consumers transact digitally, we've heard loud and clear that teams building digital experiences want the connective data that lets them understand end-user experiences and as a result, business success," said Andrew Tunall, Chief Product Officer at Embrace. "We're thrilled to make it even easier for innovators who work with Grafana to see and act on a modern observability dataset from mobile apps to connect technical failures all the way from the cloud to a user's device, and then to business outcomes. Our alignment around open-source and OpenTelemetry, and our mission to modernize the observability ecosystem, are a great fit with Grafana Labs, who continue to drive crucial innovation in the space."
With OpenTelemetry-based SDKs designed for mobile, Embrace helps SRE and mobile development teams modernize their observability stack with critical mobile signals from users. Embrace will collaborate with Grafana Labs to allow entire enterprise engineering teams to benefit from mobile insights and capabilities built uniquely for mobile.
"With this partnership, customers can use OpenTelemetry to bridge from user-focused insights on a mobile app, through app and infrastructure observability, and fully understand each part of the system's impact on SLOs," said Ash Mazhari, VP of Corporate and Business Development at Grafana Labs. "We're incredibly excited to work with Embrace on bringing modern observability to our joint customers."
Embrace delivers context-rich mobile data in the form of metrics and traces (and soon logs) to Grafana for full visibility into a company's entire tech ecosystem. Benefits include:
- Seamless integration: Engineers can forward metrics and traces from Embrace directly to Grafana Cloud, unlocking the ability to analyze and view modern mobile observability data alongside telemetry captured from backend services and infrastructure in Grafana Labs' OSS visualization layer and observability platform.
- Comprehensive mobile insights: Grafana users can leverage Embrace's tracing capabilities to gain deep, fine-grained insights into complex issues that impact mobile performance, such as networking problems. Additionally, Embrace's robust real user monitoring (RUM) tools provide detailed context into mobile-specific user issues, such as session events, crashes, errors, ANRs (Application Not Responding), memory issues and more.
- Proactive issue detection: Teams can shift from a reactive to proactive practice in how they interact with observability data, identifying and resolving issues in real-time, with a common language and solution, to ensure a seamless user experience and collaboration across engineering teams.
Capturing telemetry for client applications raises the bar for modern observability teams. Now any SREs and mobile development teams can gather the data and insights they need to modernize their stack with a mobile-first approach.
The Latest
As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...
For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...
I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...
Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...
40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...
Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...
Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...
