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Embrace Announces Native iOS and Android SDKs Built on OpenTelemetry

Embrace announced that its native iOS and Android SDKs are now built on OpenTelemetry.

The open-source, enterprise-supported SDKs combine Embrace's insights into mobile user experiences with transparent, portable, and extensible data collection.

This launch pairs Embrace's view on modeling user behavior with OpenTelemetry to better contextualize telemetry for user sessions. Engineers benefit from full visibility into mobile user experiences so they can resolve critical performance issues before they become widespread. With this release, anyone can use Embrace's iOS and Android SDKs to send logs and spans to any OTLP capable tracing and logging backend, with metrics support soon to follow.

Embrace collects the full technical and behavioral details of every user session, providing engineers with the necessary context to identify and resolve issues quickly. With this OTel solution, customers can also extend instrumentation to any custom library in their app and leverage Embrace's platform to contextualize the added instrumentation. This empowers engineers to explore insights and expedite issue resolution. Additionally, Embrace's instrumentation is compatible with any OTel backend, offering flexibility and ease of integration.

"We want to serve the thousands of engineers building incredible mobile apps that people rely on every day, and we look forward to the community collaboration that will come with shipping open-source, standards-based SDKs," said Andrew Tunall, Chief Product Officer at Embrace. "We want to advance how the community thinks about modeling both programmatic and human-driven behavior in mobile apps. By standardizing mobile data collection and instrumentation through OTel, we can help engineers move faster and understand their valuable users."

Teams seeking OTel-compliant tooling that's built for mobile will gain the following with Embrace:

- Mobile telemetry built with the user in mind: Mobile teams require context-aware mobile telemetry. With Embrace, they can capture signals that are critical for maintaining a highly performant mobile app – like crashes, errors, ANRs (Application Not Responding), performance traces, memory issues, and full user sessions – modeled in OTel data types of spans and logs.

- Portable, vendor-agnostic data: Embrace collects critical mobile app signals. Teams have full control over where to send that data, whether to Embrace's dashboard for advanced mobile insights, or to another observability stack. Embrace's OpenTelemetry distribution comes with instructions to pair with an OTLP exporter that can send data to any OTel backend.

- Enterprise-ready and commercially supported tech: Embrace delivers the flexibility of an open source product with the reliability of a commercially supported one. Open source SDKs give engineers transparency and extensibility, while Embrace's data backend and analysis platform is enterprise-supported for confidence in the adoption of secure and reliable observability software.

Embrace's open source OpenTelemetry SDKs are available on GitHub now.

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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 ...

Embrace Announces Native iOS and Android SDKs Built on OpenTelemetry

Embrace announced that its native iOS and Android SDKs are now built on OpenTelemetry.

The open-source, enterprise-supported SDKs combine Embrace's insights into mobile user experiences with transparent, portable, and extensible data collection.

This launch pairs Embrace's view on modeling user behavior with OpenTelemetry to better contextualize telemetry for user sessions. Engineers benefit from full visibility into mobile user experiences so they can resolve critical performance issues before they become widespread. With this release, anyone can use Embrace's iOS and Android SDKs to send logs and spans to any OTLP capable tracing and logging backend, with metrics support soon to follow.

Embrace collects the full technical and behavioral details of every user session, providing engineers with the necessary context to identify and resolve issues quickly. With this OTel solution, customers can also extend instrumentation to any custom library in their app and leverage Embrace's platform to contextualize the added instrumentation. This empowers engineers to explore insights and expedite issue resolution. Additionally, Embrace's instrumentation is compatible with any OTel backend, offering flexibility and ease of integration.

"We want to serve the thousands of engineers building incredible mobile apps that people rely on every day, and we look forward to the community collaboration that will come with shipping open-source, standards-based SDKs," said Andrew Tunall, Chief Product Officer at Embrace. "We want to advance how the community thinks about modeling both programmatic and human-driven behavior in mobile apps. By standardizing mobile data collection and instrumentation through OTel, we can help engineers move faster and understand their valuable users."

Teams seeking OTel-compliant tooling that's built for mobile will gain the following with Embrace:

- Mobile telemetry built with the user in mind: Mobile teams require context-aware mobile telemetry. With Embrace, they can capture signals that are critical for maintaining a highly performant mobile app – like crashes, errors, ANRs (Application Not Responding), performance traces, memory issues, and full user sessions – modeled in OTel data types of spans and logs.

- Portable, vendor-agnostic data: Embrace collects critical mobile app signals. Teams have full control over where to send that data, whether to Embrace's dashboard for advanced mobile insights, or to another observability stack. Embrace's OpenTelemetry distribution comes with instructions to pair with an OTLP exporter that can send data to any OTel backend.

- Enterprise-ready and commercially supported tech: Embrace delivers the flexibility of an open source product with the reliability of a commercially supported one. Open source SDKs give engineers transparency and extensibility, while Embrace's data backend and analysis platform is enterprise-supported for confidence in the adoption of secure and reliable observability software.

Embrace's open source OpenTelemetry SDKs are available on GitHub now.

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 ...