Jessica Manheimer
Product Marketing Manager
Maël Lilensten
Senior Product Manager
As .NET Multi-platform App UI (MAUI) becomes the default cross-platform UI framework in the Microsoft ecosystem, many teams are standardizing on it to build mobile applications for iOS and Android. However, observability has not kept pace with the shift in adoption. Developers often rely on unsupported community bindings or maintain their own wrappers around native iOS and Android SDKs, which introduces instability and ongoing maintenance. The retirement of Microsoft Visual Studio App Center has also left many teams without a clear path for monitoring crashes, errors, and user activity in production.
Datadog Real User Monitoring (RUM) provides an official .NET MAUI SDK that enables teams to instrument applications by using a single supported NuGet package. The SDK supports many core RUM features, including built-in crash reporting, error tracking, and network monitoring, along with Session Replay. All telemetry data flows into existing Datadog views, so you can analyze .NET MAUI applications alongside other mobile apps built with native iOS and Android or other cross-platform frameworks such as React Native, Flutter, and Kotlin Multiplatform.
In this post, we’ll explore how you can use the SDK to:
Detect and troubleshoot crashes in .NET MAUI apps
Mobile crashes in production often require significant effort to reproduce and diagnose, especially when stack traces are incomplete or difficult to interpret. Community-maintained bindings can compound the problem by introducing gaps in instrumentation or becoming incompatible with new SDK releases.
The Datadog .NET MAUI SDK automatically captures crashes and errors across managed and native layers of an application. Coverage includes .NET exceptions and platform-specific issues such as iOS App Hangs and Android Application Not Responding (ANR) events. Automatic instrumentation enables teams to begin collecting telemetry data without adding custom logic or maintaining separate integrations.
Datadog deobfuscates stack traces through its symbol upload process, which uses PDB files to restore readable method names and source context. Engineers can investigate issues with clear, actionable information instead of working with obfuscated output.
For example, consider a scenario where a team releases a new version of a .NET MAUI app and begins receiving crash reports shortly afterward. An engineer can navigate to Datadog Error Tracking, identify a spike in crashes tied to the new release, and review the associated stack traces. Correlating the crash with recent code changes helps the team isolate the faulty method and begin remediation.
Crash and error data from .NET MAUI apps appears alongside data from other supported mobile apps in the same Datadog views, including the RUM Explorer, Error Tracking, and session details pages. The shared interface enables teams to apply existing workflows without introducing additional tools.
Understand user sessions and performance across .NET MAUI apps
Understanding how users interact with an application and how services respond plays a key role in maintaining performance and reliability. Limited instrumentation often leaves teams without visibility into network latency, failed requests, and the sequence of user actions that led to issues.
The .NET MAUI SDK automatically tracks network requests made through common libraries, such as HttpClient
. Automatic collection provides out-of-the-box visibility into request latency and error rates, in addition to automated correlation with downstream backend dependencies through distributed APM traces.
View and action tracking give developers control over how user activity is captured and organized in session data. Developers can use views to track the screens that users navigate to. With actions, developers can capture user interactions that align with specific workflows.
Session, network, and interaction data all appear in the RUM Explorer and session detail pages. Teams can use the unified dataset to analyze performance and identify patterns across iOS, Android, and other supported platforms.
Get started with .NET MAUI monitoring in Datadog
The Datadog .NET MAUI SDK provides a supported path to instrument cross-platform mobile apps without relying on community-maintained bindings or custom wrappers. Automatic crash reporting, network monitoring, and manual instrumentation capabilities give teams visibility into application health and user behavior. To learn more, read our .NET MAUI SDK documentation.
If you’re new to Datadog, you can sign up for a 14-day free trial to start monitoring your .NET MAUI apps.
Facts Only
* .NET MAUI is becoming the default cross-platform UI framework in the Microsoft ecosystem.
* Observability has not kept pace with the shift in MAUI adoption.
* Developers often rely on unsupported community bindings or custom wrappers for native SDKs.
* The retirement of Microsoft Visual Studio App Center removed a clear path for monitoring production data.
* The Datadog .NET MAUI SDK provides an official instrumentation method via a NuGet package.
* The SDK supports built-in crash reporting, error tracking, and network monitoring, including Session Replay.
* The SDK captures crashes and errors across managed and native layers, including .NET exceptions and platform-specific issues (e.g., iOS App Hangs, Android ANR).
* Datadog deobfuscates stack traces using PDB files to restore readable method names.
* The SDK automatically tracks network requests made through common libraries like HttpClient, providing request latency and error rates.
* Session, network, and interaction data appear in unified views like the RUM Explorer and session detail pages.
Executive Summary
The shift toward .NET Multi-platform App UI (MAUI) as the default cross-platform framework in the Microsoft ecosystem has created a gap in observability, as existing monitoring solutions have not kept pace with this adoption. Developers often use unsupported community bindings or custom wrappers, which introduces maintenance burdens and instability when tracking crashes and errors on iOS and Android. Datadog addresses this by offering an official .NET MAUI SDK that allows teams to instrument applications using a single NuGet package for Real User Monitoring (RUM). This SDK includes built-in crash reporting, error tracking, and network monitoring, integrating telemetry into existing Datadog views alongside data from other cross-platform tools.
The SDK enables the detection and troubleshooting of crashes by automatically capturing exceptions across managed and native layers, including platform-specific events like iOS App Hangs and Android ANR events. Furthermore, it deobfuscates stack traces using PDB files to provide actionable context for engineers investigating issues related to recent code changes. For performance monitoring, the SDK automatically tracks network requests, such as those made via HttpClient, providing visibility into latency and error rates, which can be correlated with backend dependencies through distributed APM traces. Users can further analyze session data and user interactions through tracking views and actions in the RUM Explorer.
Full Take
The core pattern observed is a systemic failure where technological advancement (MAUI adoption) outpaces necessary operational infrastructure (observability tooling). This creates friction by forcing developers into brittle, unsupported solutions, increasing technical debt. The introduction of an official SDK bridges this gap by providing a unified, supported path, directly addressing the instability caused by reliance on community bindings. The mechanism of crash reporting and stack trace deobfuscation suggests that the primary barrier to effective debugging is not the collection of data, but the interpretation of opaque output. This moves the focus from mere data ingestion to making that data immediately actionable for the engineer.
The implication is that effective cross-platform development requires observability to be intrinsic and supported by the framework itself, rather than bolted on through external, potentially incompatible layers. The structure suggests a paradigm shift where platform integration (MAUI) necessitates integrated monitoring (Datadog RUM). The vulnerability lies in maintaining separate integrations or custom wrappers, which represent an unmanaged state that is inherently fragile. The narrative pushes for a unified experience where performance metrics, error states, and user journeys are inherently linked across mobile ecosystems, suggesting that fragmented visibility prevents holistic system understanding.
What further questions must be explored? Does the automatic deobfuscation reliably mitigate the risk of introducing new security blind spots during transmission or processing? If teams adopt this approach widely, what governance mechanisms are needed to ensure RUM data remains separated from other business telemetry while remaining unified in visualization? How does this shift change the responsibility matrix between framework maintainers, SDK providers, and end-users regarding platform-specific crash handling?