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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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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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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 ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...