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Having a Harder Time Managing Application Performance? Increased IT Complexity May Be to Blame

Mehdi Daoudi
Catchpoint

Modern software development approaches and technology infrastructures are supposed to make the lives of IT professionals better. Continuous delivery and DevOps help us roll out new software, features and modifications faster than ever before. Third-party services enable us to speed the cycle even further, adding functionality instantly without having to develop it ourselves. External infrastructures like the cloud and CDNs give us the flexibility and scalability we need to support these applications.

However, these trends can come with a nasty side effect – growing complexity that makes managing application performance much more difficult. 55 percent of IT professionals rank end-user experience monitoring (EUM) as the most critical capability for Application Performance Management (APM) products, according to a recent EMA survey. Clearly, IT professionals understand that high performance (speed and availability) for end users is critical.

The survey also found that constant production system changes brought on by continuous delivery are a huge challenge to identifying the root cause of application performance problems. Limited visibility into third-party services and the cloud can also present obstacles. 77 percent of survey respondents highly ranked the ability to troubleshoot and analyze root causes of application performance problems down to the platform level; as well as bemoaned their inability to directly see performance levels of cloud service and other third-party providers.

The recent distributed-denial-of-service (DDoS) attack against DNS provider Dyn clearly illustrated the dangers of growing complexity, specifically the over-reliance on multi-tenant service providers for critical functions (in this case, DNS routing). Although a cybersecurity attack is not a performance issue by nature, it can have major performance ramifications (like unavailability). When Dyn went down, it took along with it many of the world's most prominent websites.

Events like the Dyn attack may not be entirely avoidable, but there were two important lessons when it comes to managing growing complexity. First, the more a company relies on a single company for any important service, the more vulnerable that company becomes, regardless of how competent or reputable that service provider may be. Second, companies should always use several providers (not just one) for truly critical services, to minimize vulnerability to a single point of failure. Had the companies relying on Dyn been better able to detect Dyn's problem and react effectively – i.e., route DNS services to another provider - their own downtime could have been minimized.

IT complexity will only grow in the future, which means it is no longer enough for APM products to simply deliver data. Rather, this data needs to be combined with actionable information that enables IT teams to pinpoint and fix growing hotspots in their own infrastructure as well as third-parties, giving them a chance to enact contingency plans if necessary. As an industry, we're still far away from this ideal: according to the EMA survey, the most frequent way respondents discover performance or availability problems is from end users calling directly or triggering support tickets. This is a far cry from the optimal circumstance of solving problems before end users are impacted.

In a few weeks, the "iron man" of digital performance tests will arrive – the peak online holiday shopping season. In 2015 the perils of growing IT complexity were evident, as many mobile sites stumbled due to poorly performing third-party services. The dangers of over-reliance on popular external services was also clear, when a stall in PayPal's online payment service reverberated across the many websites using it. Whenever a certain category of online businesses comes under heavy load (such as ecommerce sites during the holidays), their external services are likely coming under even heavier load. Performance issues should be expected, and contingency plans are a must.

In a strange twist for many IT teams, the new approaches and technologies being used to better compete in the digital economy can prove to be "too much of a good thing." This year, there are no more excuses. Unless a company is comfortable losing revenues and brand equity to poor performance, IT teams, and the APM products they depend on, must be equipped to manage the end-user digital experience amidst this growing complexity.

Mehdi Daoudi is CEO and Co-Founder of Catchpoint

The Latest

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 ...

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.

Having a Harder Time Managing Application Performance? Increased IT Complexity May Be to Blame

Mehdi Daoudi
Catchpoint

Modern software development approaches and technology infrastructures are supposed to make the lives of IT professionals better. Continuous delivery and DevOps help us roll out new software, features and modifications faster than ever before. Third-party services enable us to speed the cycle even further, adding functionality instantly without having to develop it ourselves. External infrastructures like the cloud and CDNs give us the flexibility and scalability we need to support these applications.

However, these trends can come with a nasty side effect – growing complexity that makes managing application performance much more difficult. 55 percent of IT professionals rank end-user experience monitoring (EUM) as the most critical capability for Application Performance Management (APM) products, according to a recent EMA survey. Clearly, IT professionals understand that high performance (speed and availability) for end users is critical.

The survey also found that constant production system changes brought on by continuous delivery are a huge challenge to identifying the root cause of application performance problems. Limited visibility into third-party services and the cloud can also present obstacles. 77 percent of survey respondents highly ranked the ability to troubleshoot and analyze root causes of application performance problems down to the platform level; as well as bemoaned their inability to directly see performance levels of cloud service and other third-party providers.

The recent distributed-denial-of-service (DDoS) attack against DNS provider Dyn clearly illustrated the dangers of growing complexity, specifically the over-reliance on multi-tenant service providers for critical functions (in this case, DNS routing). Although a cybersecurity attack is not a performance issue by nature, it can have major performance ramifications (like unavailability). When Dyn went down, it took along with it many of the world's most prominent websites.

Events like the Dyn attack may not be entirely avoidable, but there were two important lessons when it comes to managing growing complexity. First, the more a company relies on a single company for any important service, the more vulnerable that company becomes, regardless of how competent or reputable that service provider may be. Second, companies should always use several providers (not just one) for truly critical services, to minimize vulnerability to a single point of failure. Had the companies relying on Dyn been better able to detect Dyn's problem and react effectively – i.e., route DNS services to another provider - their own downtime could have been minimized.

IT complexity will only grow in the future, which means it is no longer enough for APM products to simply deliver data. Rather, this data needs to be combined with actionable information that enables IT teams to pinpoint and fix growing hotspots in their own infrastructure as well as third-parties, giving them a chance to enact contingency plans if necessary. As an industry, we're still far away from this ideal: according to the EMA survey, the most frequent way respondents discover performance or availability problems is from end users calling directly or triggering support tickets. This is a far cry from the optimal circumstance of solving problems before end users are impacted.

In a few weeks, the "iron man" of digital performance tests will arrive – the peak online holiday shopping season. In 2015 the perils of growing IT complexity were evident, as many mobile sites stumbled due to poorly performing third-party services. The dangers of over-reliance on popular external services was also clear, when a stall in PayPal's online payment service reverberated across the many websites using it. Whenever a certain category of online businesses comes under heavy load (such as ecommerce sites during the holidays), their external services are likely coming under even heavier load. Performance issues should be expected, and contingency plans are a must.

In a strange twist for many IT teams, the new approaches and technologies being used to better compete in the digital economy can prove to be "too much of a good thing." This year, there are no more excuses. Unless a company is comfortable losing revenues and brand equity to poor performance, IT teams, and the APM products they depend on, must be equipped to manage the end-user digital experience amidst this growing complexity.

Mehdi Daoudi is CEO and Co-Founder of Catchpoint

The Latest

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 ...

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.