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Apteligent Launches Global Mobile Device Directory for iOS Android

Apteligent announced the launch of a global device directory of iOS Android devices by geography.

The directory, calculated from over 8 billion app launches with data from over 175 countries, provides valuable information such as global device/OS adoption trends and app performance stats for mobile app developers, handset manufacturers, network carriers, industry analysts, and anyone engaged in the app economy. With over 300 terabytes of data tracked per month over the past 3 years, the Apteligent directory is a comprehensive repository of mobile device adoption data.

“Apteligent has always been a strong proponent of better apps through data,” said Dave Robbins, CEO of Apteligent. “With the Apteligent SDK providing critical app performance and quality insights to mobile developers and PMs. Now other organizations can access the rich mobile ecosystems data we’ve collected and crunched to extend their understanding of the mobile app economy.”

Mobile is inherently a global phenomenon and launching an app in new geographical regions presents many unknowns. OEMs often develop different handsets for different regions, including differing hardware components that can affect the behavior of apps running on those devices. Mobile product managers, developers, and quality engineers need to understand which devices are important and what operating system versions are popular, in order to gauge how much to invest in developing new hardware and software configurations.

For example, in the United States the most popular Android device is the Samsung Galaxy S5 running Android 5.0. However, launching the same app in Brazil you might find the most popular device is actually the Samsung Galaxy Grand Prime, or in China the Xiaomi Mi Note running Android 4.4. Both devices show network performance that is over twice as slow as the most popular device in the United States. As a mobile business,decisions need to be made as to how best deliver a 5-star app in those regions.

In addition to device and operating system market share, the directory contains performance benchmarks. The directory displays global device crash rates and latency information, as well as country specific device performance.

The performance benchmarks allow organizations to gauge how well their app is running on various platforms. Mobile teams rely on Apteligent’s platform to address issues impacting user experience and to track performance issues over time, but now they can use this service to benchmark their app on various hardware and software configurations against the industry at large.

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Apteligent Launches Global Mobile Device Directory for iOS Android

Apteligent announced the launch of a global device directory of iOS Android devices by geography.

The directory, calculated from over 8 billion app launches with data from over 175 countries, provides valuable information such as global device/OS adoption trends and app performance stats for mobile app developers, handset manufacturers, network carriers, industry analysts, and anyone engaged in the app economy. With over 300 terabytes of data tracked per month over the past 3 years, the Apteligent directory is a comprehensive repository of mobile device adoption data.

“Apteligent has always been a strong proponent of better apps through data,” said Dave Robbins, CEO of Apteligent. “With the Apteligent SDK providing critical app performance and quality insights to mobile developers and PMs. Now other organizations can access the rich mobile ecosystems data we’ve collected and crunched to extend their understanding of the mobile app economy.”

Mobile is inherently a global phenomenon and launching an app in new geographical regions presents many unknowns. OEMs often develop different handsets for different regions, including differing hardware components that can affect the behavior of apps running on those devices. Mobile product managers, developers, and quality engineers need to understand which devices are important and what operating system versions are popular, in order to gauge how much to invest in developing new hardware and software configurations.

For example, in the United States the most popular Android device is the Samsung Galaxy S5 running Android 5.0. However, launching the same app in Brazil you might find the most popular device is actually the Samsung Galaxy Grand Prime, or in China the Xiaomi Mi Note running Android 4.4. Both devices show network performance that is over twice as slow as the most popular device in the United States. As a mobile business,decisions need to be made as to how best deliver a 5-star app in those regions.

In addition to device and operating system market share, the directory contains performance benchmarks. The directory displays global device crash rates and latency information, as well as country specific device performance.

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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.