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How to Manage Mobile App Development Complexity with Modern APM

Dave Hayes
Sentry

With a projected 7 billion mobile users by 2021, mobile is becoming the most dominant digital touchpoint for customer engagement. Annual mobile app downloads are projected to reach 258 billion by 2022 — a 45% increase from 2017. But downloads alone do not indicate mobile success — retention and engagement are key. While there are many factors that influence these metrics, application performance may be one of the most critical.

Application crashes can increase churn and damage brand reputation. In fact, app crashes cause more than 70% of uninstalls. Because Google ranking algorithms now downrank apps with stability problems, uptime and performance can even influence download metrics. It is imperative that organizations are delivering high-performing mobile applications and that the developers supporting those applications have the ability to identify and remediate errors efficiently.

But due to the growing complexity of the application ecosystem, this can be easier said than done. Developers are often limited by a lack of visibility and control over mobile devices. They have no way of knowing every device’s attributes or predicting which other apps and system processes may be competing for compute power. Mobile app developers are no strangers to segmentation faults and bus errors. And programming for such a diverse array of devices creates its own set of challenges. Flaws in native code, third-party dependencies or, in rare cases, even in the system libraries, can bring down the entire application.

Developers today are under pressure to deliver code faster than ever before. Even when rigorous testing processes are in place, the increasing complexity of application development will inevitably cause errors. Monitoring is an important component of application delivery, but the changing ecosystem calls for the reinvention of application performance monitoring that incorporates rich context and actionable insights.

Rethinking Application Performance Monitoring

While companies with faulty code are dealing with the fallout, those that rely on modern application performance monitoring (APM) are shipping better code more quickly and with less risk. Legacy monitoring tools focus more on system behavior, offering performance metrics on availability, throughput, and latency, but miss the mark when it comes to actionable insight that helps mobile developers make sense of complexity and quickly get to the root cause of errors.

On mobile — as with all native applications — it is important to have context beyond a crash. Developers need rich details supporting the error, such as the types of phones impacted, the number of users impacted, the specific actions a user took when the error was thrown, or even the storage capacity and battery life of the user's phone at the time the app crashed. These details are especially important for mobile developers because of the vast array of devices and native libraries used in programming. Having this information enables them to immediately triage and prioritize problems.

When developers can also identify the exact release and commit the error is tied to, they can quickly remediate the issue to minimize the breadth of impact. Developers can also move this feedback into the development cycle. By capturing every single exception and crash users encounter, meaningful trends will surface to help prioritize issues and avoid replication in future software release.

It is also important to consider that modern applications are not self-contained — they have multiple runtimes across the stack, causing added complexity in monitoring. Support for mobile, coupled with similar support for web, gives developers a complete picture, which is key in today’s application-centric landscape.

Innovation in the mobile space shows no signs of slowing, so developers must take proactive steps to address the factors that derail mobile app development and wreak havoc on user experience. By taking a modern approach to APM, incorporating rich context and actionable insights, and syncing this information across all of their applications, developers can code better and faster, ensuring their companies remain successful and competitive in the mobile world.

Dave Hayes is Head of Product at Sentry

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How to Manage Mobile App Development Complexity with Modern APM

Dave Hayes
Sentry

With a projected 7 billion mobile users by 2021, mobile is becoming the most dominant digital touchpoint for customer engagement. Annual mobile app downloads are projected to reach 258 billion by 2022 — a 45% increase from 2017. But downloads alone do not indicate mobile success — retention and engagement are key. While there are many factors that influence these metrics, application performance may be one of the most critical.

Application crashes can increase churn and damage brand reputation. In fact, app crashes cause more than 70% of uninstalls. Because Google ranking algorithms now downrank apps with stability problems, uptime and performance can even influence download metrics. It is imperative that organizations are delivering high-performing mobile applications and that the developers supporting those applications have the ability to identify and remediate errors efficiently.

But due to the growing complexity of the application ecosystem, this can be easier said than done. Developers are often limited by a lack of visibility and control over mobile devices. They have no way of knowing every device’s attributes or predicting which other apps and system processes may be competing for compute power. Mobile app developers are no strangers to segmentation faults and bus errors. And programming for such a diverse array of devices creates its own set of challenges. Flaws in native code, third-party dependencies or, in rare cases, even in the system libraries, can bring down the entire application.

Developers today are under pressure to deliver code faster than ever before. Even when rigorous testing processes are in place, the increasing complexity of application development will inevitably cause errors. Monitoring is an important component of application delivery, but the changing ecosystem calls for the reinvention of application performance monitoring that incorporates rich context and actionable insights.

Rethinking Application Performance Monitoring

While companies with faulty code are dealing with the fallout, those that rely on modern application performance monitoring (APM) are shipping better code more quickly and with less risk. Legacy monitoring tools focus more on system behavior, offering performance metrics on availability, throughput, and latency, but miss the mark when it comes to actionable insight that helps mobile developers make sense of complexity and quickly get to the root cause of errors.

On mobile — as with all native applications — it is important to have context beyond a crash. Developers need rich details supporting the error, such as the types of phones impacted, the number of users impacted, the specific actions a user took when the error was thrown, or even the storage capacity and battery life of the user's phone at the time the app crashed. These details are especially important for mobile developers because of the vast array of devices and native libraries used in programming. Having this information enables them to immediately triage and prioritize problems.

When developers can also identify the exact release and commit the error is tied to, they can quickly remediate the issue to minimize the breadth of impact. Developers can also move this feedback into the development cycle. By capturing every single exception and crash users encounter, meaningful trends will surface to help prioritize issues and avoid replication in future software release.

It is also important to consider that modern applications are not self-contained — they have multiple runtimes across the stack, causing added complexity in monitoring. Support for mobile, coupled with similar support for web, gives developers a complete picture, which is key in today’s application-centric landscape.

Innovation in the mobile space shows no signs of slowing, so developers must take proactive steps to address the factors that derail mobile app development and wreak havoc on user experience. By taking a modern approach to APM, incorporating rich context and actionable insights, and syncing this information across all of their applications, developers can code better and faster, ensuring their companies remain successful and competitive in the mobile world.

Dave Hayes is Head of Product at Sentry

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...