
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
Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...
Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...
Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...
While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...
For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...
As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...
The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...
The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...
Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...
Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...
