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28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 5

APMdigest asked experts from across the industry – including consultants, analysts and the leading vendors – for recommendations on the best way to ensure application performance in the hybrid cloud. Part 5, the final installment, covers approaches you might not have thought about.

Start with 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 1

Start with 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 2

Start with 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 3

Start with 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 4

22. FOCUS ON PERFORMANCE DURING DEVELOPMENT

Tools don't ensure great performance in hybrid cloud environments. Tools help you in your testing phase to ensure that the application you push to production meets your performance requirements. Afterwards tools will help you to monitor the end user experience (performance and availability) and will help you to make sure you can guarantee the promised experience. The best way to ensure great performance is to make performance a requirement from day one. Make sure you're developers understand the performance of each line of code they write.
Coen Meerbeek
Online Performance Consultant and Founder of Blue Factory Internet

23. TEST AGAINST REAL NETWORK CONDITIONS

The biggest challenge with Hybrid Cloud is the switch between very different networks (often Lan to Wan) as the workload dynamically expands into the off-premises facilities. The network has a huge impact on application response time and performance and this must be mitigated in advance to prevent sharp increases in response time as the off-premises systems come into play during periods of high demand. The best way to do this is by utilizing Virtual test networks (network emulators) to try out (or test) these dynamic network changes prior to deployment of the solution. These products will replicate the real-world network conditions of both the private and public elements of the cloud solutions including transition between them. Taking this approach means you will have full insight into any potential issues before you commit time, money and resource to implementing a hybrid cloud solution.
Frank Puranik
Senior Technical Specialist, iTrinegy

24. KEEP MISSION CRITICAL APPLICATIONS IN-HOUSE

Hybrid cloud environments use a mix of on-premises, private cloud and third-party, public cloud services, allowing dynamic workload shifts as computing needs change. The major difference between the private and public cloud is that private clouds are not a shared resource, subject to overload from "neighbors" in the cloud. For this reason, when it comes to ensuring high performance for mission-critical, revenue-generating transactions, we recommend keeping these permanently in the private cloud, on mainframes (IBM's recently announced z13s is a strong option for mid-sized enterprises). Once that is established, organizations must include the mainframe as part of their overarching enterprise APM efforts. Today, an estimated 55 percent of enterprise applications touch mainframes, and IT managers need to be able to identify and address any mainframe bottlenecks that could impact performance.
Spencer Hallman
Product Manager, Compuware

25. FIND THE RIGHT SOLUTION PARTNER

Find the right partner. Invest in partners that bring the right added value to help you secure processes without the problem growing beyond your ability to handle it. Ensure your partner is aware of new technologies, trained and well-equipped to manage the breadth of various regulation requirements, and is able to provide (on your behalf) the tools customers need to answer market compliance. It's important that no component of what you choose to solve the business problem becomes the weak link.
Joan Groleau
Director, North America Channel Sales, Ipswitch

26. EXTEND TEAM SKILLS AND KNOWLEDGE

Today's IT professionals need to extend across traditional generalist or specialist roles and become polymaths in order to be successful in the hybrid IT world as they pivot across multiple technology domains. The most important skills and knowledge IT professionals need to develop or improve to successfully manage hybrid IT environments are service-oriented architectures, automation, vendor management, application migration, distributed architectures, API and hybrid IT monitoring and management tools and metrics.
Kong Yang
Head Geek, SolarWinds

27. ASSIGN APPLICATIONS TO THE RIGHT CLOUD ENVIRONMENT

Although there is a widening array of APM tools available to the challenges of the hybrid cloud alternatives, the key to ensure application performance is understanding the strengths and weaknesses of each cloud offering and properly assigning the application deployment to the appropriate cloud environment.
Jeffrey Kaplan
Managing Director of THINKstrategies and Founder of the Cloud Showplace

28. MANAGE THE FULL LIFECYCLE OF THE CLOUD SERVICE

Application performance in a cloud environment, whether public, private or hybrid, is about more than simply monitoring the application, it's about managing the full lifecycle of the cloud service from provisioning and maintaining the right configurations to remediating security vulnerabilities to planning for capacity growth. You need a cloud management platform that not only thrives in a hybrid environment but also enables applications to perform optimally.
Bill Berutti
President of the Cloud, Data Center and Performance Businesses at BMC Software

Hot Topics

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.

28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 5

APMdigest asked experts from across the industry – including consultants, analysts and the leading vendors – for recommendations on the best way to ensure application performance in the hybrid cloud. Part 5, the final installment, covers approaches you might not have thought about.

Start with 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 1

Start with 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 2

Start with 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 3

Start with 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 4

22. FOCUS ON PERFORMANCE DURING DEVELOPMENT

Tools don't ensure great performance in hybrid cloud environments. Tools help you in your testing phase to ensure that the application you push to production meets your performance requirements. Afterwards tools will help you to monitor the end user experience (performance and availability) and will help you to make sure you can guarantee the promised experience. The best way to ensure great performance is to make performance a requirement from day one. Make sure you're developers understand the performance of each line of code they write.
Coen Meerbeek
Online Performance Consultant and Founder of Blue Factory Internet

23. TEST AGAINST REAL NETWORK CONDITIONS

The biggest challenge with Hybrid Cloud is the switch between very different networks (often Lan to Wan) as the workload dynamically expands into the off-premises facilities. The network has a huge impact on application response time and performance and this must be mitigated in advance to prevent sharp increases in response time as the off-premises systems come into play during periods of high demand. The best way to do this is by utilizing Virtual test networks (network emulators) to try out (or test) these dynamic network changes prior to deployment of the solution. These products will replicate the real-world network conditions of both the private and public elements of the cloud solutions including transition between them. Taking this approach means you will have full insight into any potential issues before you commit time, money and resource to implementing a hybrid cloud solution.
Frank Puranik
Senior Technical Specialist, iTrinegy

24. KEEP MISSION CRITICAL APPLICATIONS IN-HOUSE

Hybrid cloud environments use a mix of on-premises, private cloud and third-party, public cloud services, allowing dynamic workload shifts as computing needs change. The major difference between the private and public cloud is that private clouds are not a shared resource, subject to overload from "neighbors" in the cloud. For this reason, when it comes to ensuring high performance for mission-critical, revenue-generating transactions, we recommend keeping these permanently in the private cloud, on mainframes (IBM's recently announced z13s is a strong option for mid-sized enterprises). Once that is established, organizations must include the mainframe as part of their overarching enterprise APM efforts. Today, an estimated 55 percent of enterprise applications touch mainframes, and IT managers need to be able to identify and address any mainframe bottlenecks that could impact performance.
Spencer Hallman
Product Manager, Compuware

25. FIND THE RIGHT SOLUTION PARTNER

Find the right partner. Invest in partners that bring the right added value to help you secure processes without the problem growing beyond your ability to handle it. Ensure your partner is aware of new technologies, trained and well-equipped to manage the breadth of various regulation requirements, and is able to provide (on your behalf) the tools customers need to answer market compliance. It's important that no component of what you choose to solve the business problem becomes the weak link.
Joan Groleau
Director, North America Channel Sales, Ipswitch

26. EXTEND TEAM SKILLS AND KNOWLEDGE

Today's IT professionals need to extend across traditional generalist or specialist roles and become polymaths in order to be successful in the hybrid IT world as they pivot across multiple technology domains. The most important skills and knowledge IT professionals need to develop or improve to successfully manage hybrid IT environments are service-oriented architectures, automation, vendor management, application migration, distributed architectures, API and hybrid IT monitoring and management tools and metrics.
Kong Yang
Head Geek, SolarWinds

27. ASSIGN APPLICATIONS TO THE RIGHT CLOUD ENVIRONMENT

Although there is a widening array of APM tools available to the challenges of the hybrid cloud alternatives, the key to ensure application performance is understanding the strengths and weaknesses of each cloud offering and properly assigning the application deployment to the appropriate cloud environment.
Jeffrey Kaplan
Managing Director of THINKstrategies and Founder of the Cloud Showplace

28. MANAGE THE FULL LIFECYCLE OF THE CLOUD SERVICE

Application performance in a cloud environment, whether public, private or hybrid, is about more than simply monitoring the application, it's about managing the full lifecycle of the cloud service from provisioning and maintaining the right configurations to remediating security vulnerabilities to planning for capacity growth. You need a cloud management platform that not only thrives in a hybrid environment but also enables applications to perform optimally.
Bill Berutti
President of the Cloud, Data Center and Performance Businesses at BMC Software

Hot Topics

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.