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Performance Considerations for Cloud-Based Applications

Steve Tack

Cloud computing represents a compelling way for IT teams to achieve superior agility, flexibility and cost-efficiency in delivering both customer- and employee-facing enterprise applications. But just because you’re using cloud services from one of the top service providers, that’s no guarantee of superior application performance, particularly when it comes to speed. Businesses must look beyond cloud deployment benefits and evaluate how moving web applications to the cloud may impact their end users’ experiences.

Web application speed is a business issue, and applications that don’t perform well -– are slow to load, have periods of unavailability or inconsistent performance –- can negatively impact end-users’ experiences. Consider potential customers –- when their satisfaction with your application is low, this reduces the likelihood that they will continue to spend time on your site and/or actually go through with a purchase.

A recent study analyzing millions of page views on websites around the world found that conversion rates increase 74 percent when page load time improves from eight to two seconds. Another study found that page abandonment rates increase steeply as page load times increase.

With statistics like this, you can’t afford to simply turn over your mission-critical applications to the cloud and not take steps on your own to validate and ensure strong application performance. Today, most cloud service offer generic guarantees such as 99.95 percent uptime, but all this means is that their services are up and running -- not that your application is performing optimally and delivering the performance that your end users expect.

Many service providers will issue service credits for blatant performance violations, but can these credits make up for the potential damage caused to your revenue, brand and customer satisfaction? Contrary to popular belief, cloud elasticity is not without limits and if your “neighbor” in the cloud experiences a spike in traffic, there’s a chance your application may slow way down.

Cloud service providers should provide application performance guarantees tailored to individual customers’ needs and provide proactive SLA notifications, but the reality is that many do not. It’s therefore incumbent upon cloud users to measure the performance of their cloud-based applications on their own, from the only perspective that matters –- that of their end users, on the other side of the cloud at the edge of the Internet.

Likewise, ramp-up time of additional capacity during peak business demands might be fundamental to your cloud goals and therefore should be proactively tested. This is the only way to know for sure that performance is not slacking and that you’re getting what you’re paying for. You should also insist that specific application performance guarantees be written into your SLA.

Cloud-based application performance can vary greatly depending on an end user’s location. Typically, the closer an end user is to a cloud service provider data center, the better the performance. So you must be extremely watchful of the end-user experience across key geographies, at critical times of day. Worldwide monitoring and testing networks can give you a quick and easy bird’s eye view into the actual experience of end-user segments across various regions.

Furthermore, new online communities measure and monitor the performance of the leading cloud service providers, helping you understand if an application problem is unique to you, or symptomatic of a larger cloud-related issue that may be affecting the wider Internet ecosystem.

In fairness to cloud service providers, it can be challenging to guarantee the performance of an application from an end user’s perspective because this performance is so dependent on a number of factors which are completely outside their control -– regional ISPs, local ISPs third-party content and services, and CDNs, and all the way to end users’ browsers and devices. This is known as the application delivery chain, and one single poorly performing element –- be it the cloud or another variable – can bring down performance for an entire application. Managing application performance across this delivery chain begins by understanding the end-user experience at the browser/device level, and then extending all the way back to the data center to identify and address any “offending” elements along the way.

As more applications and application components are ported to shared and opaque cloud platforms, it becomes essential to include the cloud as part of this comprehensive view to reap its benefits.

Steve Tack is CTO of Compuware’s Application Performance Management Business Unit.

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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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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 ...

Performance Considerations for Cloud-Based Applications

Steve Tack

Cloud computing represents a compelling way for IT teams to achieve superior agility, flexibility and cost-efficiency in delivering both customer- and employee-facing enterprise applications. But just because you’re using cloud services from one of the top service providers, that’s no guarantee of superior application performance, particularly when it comes to speed. Businesses must look beyond cloud deployment benefits and evaluate how moving web applications to the cloud may impact their end users’ experiences.

Web application speed is a business issue, and applications that don’t perform well -– are slow to load, have periods of unavailability or inconsistent performance –- can negatively impact end-users’ experiences. Consider potential customers –- when their satisfaction with your application is low, this reduces the likelihood that they will continue to spend time on your site and/or actually go through with a purchase.

A recent study analyzing millions of page views on websites around the world found that conversion rates increase 74 percent when page load time improves from eight to two seconds. Another study found that page abandonment rates increase steeply as page load times increase.

With statistics like this, you can’t afford to simply turn over your mission-critical applications to the cloud and not take steps on your own to validate and ensure strong application performance. Today, most cloud service offer generic guarantees such as 99.95 percent uptime, but all this means is that their services are up and running -- not that your application is performing optimally and delivering the performance that your end users expect.

Many service providers will issue service credits for blatant performance violations, but can these credits make up for the potential damage caused to your revenue, brand and customer satisfaction? Contrary to popular belief, cloud elasticity is not without limits and if your “neighbor” in the cloud experiences a spike in traffic, there’s a chance your application may slow way down.

Cloud service providers should provide application performance guarantees tailored to individual customers’ needs and provide proactive SLA notifications, but the reality is that many do not. It’s therefore incumbent upon cloud users to measure the performance of their cloud-based applications on their own, from the only perspective that matters –- that of their end users, on the other side of the cloud at the edge of the Internet.

Likewise, ramp-up time of additional capacity during peak business demands might be fundamental to your cloud goals and therefore should be proactively tested. This is the only way to know for sure that performance is not slacking and that you’re getting what you’re paying for. You should also insist that specific application performance guarantees be written into your SLA.

Cloud-based application performance can vary greatly depending on an end user’s location. Typically, the closer an end user is to a cloud service provider data center, the better the performance. So you must be extremely watchful of the end-user experience across key geographies, at critical times of day. Worldwide monitoring and testing networks can give you a quick and easy bird’s eye view into the actual experience of end-user segments across various regions.

Furthermore, new online communities measure and monitor the performance of the leading cloud service providers, helping you understand if an application problem is unique to you, or symptomatic of a larger cloud-related issue that may be affecting the wider Internet ecosystem.

In fairness to cloud service providers, it can be challenging to guarantee the performance of an application from an end user’s perspective because this performance is so dependent on a number of factors which are completely outside their control -– regional ISPs, local ISPs third-party content and services, and CDNs, and all the way to end users’ browsers and devices. This is known as the application delivery chain, and one single poorly performing element –- be it the cloud or another variable – can bring down performance for an entire application. Managing application performance across this delivery chain begins by understanding the end-user experience at the browser/device level, and then extending all the way back to the data center to identify and address any “offending” elements along the way.

As more applications and application components are ported to shared and opaque cloud platforms, it becomes essential to include the cloud as part of this comprehensive view to reap its benefits.

Steve Tack is CTO of Compuware’s Application Performance Management Business Unit.

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 ...