4 Differences Between Mobile and Server Performance Monitoring

According to eMarketer, as of 2014 Americans consume more media using mobile devices than laptops and desktops combined. This shift in consumer behavior is also occurring within corporations, as employees increasingly rely on mobile devices for their work.

With such a surge in mobile usage there is a growing need for corporations to ensure that their mobile experience is high quality and not broken.

Since 86% of mobile experiences occur within apps and not mobile browsers [source: Flurry], focusing on improving app performance has a larger impact on mobile quality.

The following are 4 key differences that companies monitoring their server (and website) performance should consider when selecting a mobile app performance monitoring solution.

1. Different Team, Different Needs

At most companies, mobile teams are not part of the server/website teams. Instead mobile teams are completely separate and in many cases they are an outsourced team.

The mobile teams have unique pain points when releasing mobile apps that are different from those of web and backend developers (more on this below). Solutions that slightly tweak the interface of a server performance monitoring service do not cut it. These teams require solutions designed from the ground up to solve their problems.

2. Scaling vs. Fragmentation Challenge

Developers on server teams face scaling problems. When a website or backend developer writes a line of code they need to ensure that it performs well as traffic grows and lots of users hit that code.

On the other hand developers on mobile teams face fragmentation problems. When a mobile developer writes a line of code they need to ensure that it will run well on thousands of device configurations, including varying device types, connection types, and OS versions.

A recent study by OpenSignal found that there are over 18,000 types of Android devices. How does a mobile developer confirm that their app code doesn’t break across all these devices? There is only one way, monitor production performance using a service that makes it easy to slide and dice the live performance.

3. Network vs. Device Performance

Server teams are primarily concerned with network performance. When the network is slow the bits don’t get downloaded to the thin client, usually a browser, and the end user suffers.

Mobile teams however are concerned with much more than just the network performance; they are dealing with low-end devices running their evolving client code base.

Unique challenges for mobile app developers include:

- How smooth are the interactions (e.g. scrolling)?

- Are apps hitting memory limits on certain devices hurting the user experience?

- Are users on lower end devices waiting an unreasonable amount of time for calculations to finish?

- Is the app draining the battery at an unreasonable rate?

A performance solution for mobile developers needs to be much more comprehensive in the type of metrics captured, and go beyond simply reporting on network issues.

4. Greater Variability of User Experiences

Unlike desktops and laptops, which are high-powered devices often used indoors on reliable networks, mobile devices have more chaotic environments with a wide range of capabilities running on top of unreliable networks.

Since mobile has more variability, performance monitoring solutions need to remove noise from the data to make it usable. For example, the ability to slice and dice the data to view the data that matters, like performance in the US of the latest app version on older but popular handsets.

Mobile performance monitoring solutions should also provide the ability to handle noise introduced by outliers that distort the average performance. This can be addressed by metrics like 95th percentile performance, which are more representative of a slow experience, and 50th percentile performance, to better measure the typical experience.

Finally, noise is created by interrupted app sessions like answering a phone call in the middle of a session. Solutions that detect and handle interruptions present a clearer picture of true performance.

Summary

As users migrate to using mobile apps, businesses face a challenge ensuring the same high quality experiences provided on the Web. In selecting a mobile performance monitoring service to help discover and prioritize outstanding issues, businesses should consider the unique pain points their mobile teams face as outlined above.

ABOUT Ofer Ronen

Ofer Ronen is the Co-founder and CEO of Pulse.io, a performance monitoring service for mobile app developers. The service monitors over 400 monthly sessions for companies of all sizes. It is unique in the level of performance metrics reported, ensuring that issues are not missed. Ronen previously was CEO of Sendori (sold to IAC), a mobile and web ad network. He received a computer engineering MS/BS from Michigan, and MBA from Cornell.

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4 Differences Between Mobile and Server Performance Monitoring

According to eMarketer, as of 2014 Americans consume more media using mobile devices than laptops and desktops combined. This shift in consumer behavior is also occurring within corporations, as employees increasingly rely on mobile devices for their work.

With such a surge in mobile usage there is a growing need for corporations to ensure that their mobile experience is high quality and not broken.

Since 86% of mobile experiences occur within apps and not mobile browsers [source: Flurry], focusing on improving app performance has a larger impact on mobile quality.

The following are 4 key differences that companies monitoring their server (and website) performance should consider when selecting a mobile app performance monitoring solution.

1. Different Team, Different Needs

At most companies, mobile teams are not part of the server/website teams. Instead mobile teams are completely separate and in many cases they are an outsourced team.

The mobile teams have unique pain points when releasing mobile apps that are different from those of web and backend developers (more on this below). Solutions that slightly tweak the interface of a server performance monitoring service do not cut it. These teams require solutions designed from the ground up to solve their problems.

2. Scaling vs. Fragmentation Challenge

Developers on server teams face scaling problems. When a website or backend developer writes a line of code they need to ensure that it performs well as traffic grows and lots of users hit that code.

On the other hand developers on mobile teams face fragmentation problems. When a mobile developer writes a line of code they need to ensure that it will run well on thousands of device configurations, including varying device types, connection types, and OS versions.

A recent study by OpenSignal found that there are over 18,000 types of Android devices. How does a mobile developer confirm that their app code doesn’t break across all these devices? There is only one way, monitor production performance using a service that makes it easy to slide and dice the live performance.

3. Network vs. Device Performance

Server teams are primarily concerned with network performance. When the network is slow the bits don’t get downloaded to the thin client, usually a browser, and the end user suffers.

Mobile teams however are concerned with much more than just the network performance; they are dealing with low-end devices running their evolving client code base.

Unique challenges for mobile app developers include:

- How smooth are the interactions (e.g. scrolling)?

- Are apps hitting memory limits on certain devices hurting the user experience?

- Are users on lower end devices waiting an unreasonable amount of time for calculations to finish?

- Is the app draining the battery at an unreasonable rate?

A performance solution for mobile developers needs to be much more comprehensive in the type of metrics captured, and go beyond simply reporting on network issues.

4. Greater Variability of User Experiences

Unlike desktops and laptops, which are high-powered devices often used indoors on reliable networks, mobile devices have more chaotic environments with a wide range of capabilities running on top of unreliable networks.

Since mobile has more variability, performance monitoring solutions need to remove noise from the data to make it usable. For example, the ability to slice and dice the data to view the data that matters, like performance in the US of the latest app version on older but popular handsets.

Mobile performance monitoring solutions should also provide the ability to handle noise introduced by outliers that distort the average performance. This can be addressed by metrics like 95th percentile performance, which are more representative of a slow experience, and 50th percentile performance, to better measure the typical experience.

Finally, noise is created by interrupted app sessions like answering a phone call in the middle of a session. Solutions that detect and handle interruptions present a clearer picture of true performance.

Summary

As users migrate to using mobile apps, businesses face a challenge ensuring the same high quality experiences provided on the Web. In selecting a mobile performance monitoring service to help discover and prioritize outstanding issues, businesses should consider the unique pain points their mobile teams face as outlined above.

ABOUT Ofer Ronen

Ofer Ronen is the Co-founder and CEO of Pulse.io, a performance monitoring service for mobile app developers. The service monitors over 400 monthly sessions for companies of all sizes. It is unique in the level of performance metrics reported, ensuring that issues are not missed. Ronen previously was CEO of Sendori (sold to IAC), a mobile and web ad network. He received a computer engineering MS/BS from Michigan, and MBA from Cornell.

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The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

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Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

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Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

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AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

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Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...