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

For years the IT industry debated about whether and when IT organizations would move everything to the cloud. Today's answer to this question is the hybrid cloud or hybrid IT – a combination of private and public clouds with traditional on-premise infrastructure, leveraging the best of all three worlds. A recent survey conducted by IDG Research Services for EMC revealed that 83% of respondents currently use or plan to use a hybrid cloud environment, and 73% agree that a hybrid cloud model creates a path to digital business.

It is clear that hybrid cloud offers many advantages. In a blog on APMdigest, Kong Yang of Solarwinds said, "The top three hybrid IT benefits by weighted rank are infrastructure cost-reduction, increased infrastructure flexibility/agility and relieving internal IT personnel of day-to-day management of some infrastructure, respectively."

However, as more enterprises move to hybrid cloud, they face new challenges for managing the performance of applications, such as limited visibility and control. Referencing a survey on public cloud, Antonio Piraino of ScienceLogic said, "A staggering 82 percent are unable to ensure optimum performance, health and availability of public cloud workloads due to lack of advanced visibility into the public cloud infrastructure."

In a recent blog on hybrid cloud, Shamus McGillicuddy, Senior Analyst, Network Management at Enterprise Management Associates (EMA), warns, "Enterprises who are implementing hybrid clouds will need to evaluate the readiness of their performance management to support these new cloud environments."

To address this new set of challenges, 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. The result is a detailed list of tools and approaches, and related insights, to guide enterprises as they migrate to hybrid cloud.

Some of the recommendations are new twists on tools you are familiar with or combinations of tools that our experts say are essential in the new world of hybrid cloud, but all of them require a new way of looking at the challenge of application performance.

The list will be posted in installments all week. Part 1 covers APM and End-User Experience Monitoring.

1. APPLICATION PERFORMANCE MANAGEMENT (APM)

There is often a misconception that hybrid cloud implies that an application (or components of an application) are running in two places concurrently. At least today, this is rarely the case, although in a future that increasingly includes things like containers and microservices this duality of execution may become more of a reality. Another way of saying this is that we don't need to make this more complex than it is. Today I would simply state that you should start with ensuring that you have visibility into the end user experience for both the cloud and on-premise applications. Use this point of observability to provide early warning indicators of potential problems. Depending upon the application type, you may be able to add instrumentation to help with localization of the problem domain through tracing and other activity (you may be able to substitute a network-based approach for this as well, if for example, cloud-based instrumentation proved to somehow be problematic). Then, through the same or additional agent technology you may have to delve more deeply into application and/or system internals to move closer to a root cause analysis. Of course, what I've basically described is the need for several of the elements that go into our 5 dimensional model of APM. All of this can be performed today with many existing SaaS or on-premise-based APM products as we point out in our latest Magic Quadrant. As I indicated earlier, this gets more complicated if in future hybrid cloud environments we start encountering more cloud native applications which have the twin challenges of scale and ephemerality, among other things, to deal with.
Cameron Haight
Research VP, IT Operations, Gartner

2. CLOUD-READY APM

Application Performance Management Platforms/Suites are becoming increasingly important as more companies roll out complex hybrid services. However one key factor to remember in choosing such a platform is that support for hybrid cloud also means support for the integration platforms that are intrinsic to creating hybrid services. In other words, APM solutions must be able to support the integration points that are foundational elements to hybrid delivery. Specifically, the APM solution needs to be "cloud ready" in its ability to consume and analyze metrics from API Gateways, messaging platforms, and/or other integration points to consolidate that information into APM dashboards and reports.
Julie Craig
Research Director, Application Management, Enterprise Management Associates (EMA)

Today's applications can have components spread across cloud, on premises, in containers and accessed through APIs and more. A modern approach to Application Performance Management allows you to see all of the elements and more importantly, reduce the complexity of what you are seeing so that you can quickly address performance issues, no matter where they reside.
Aruna Ravichandran
VP, DevOps Product and Solutions Marketing, CA Technologies

With the evolution of hybrid cloud, applications can now span across environments. For example, an application could be running on a public cloud platform to take advantage of autoscaling and be accessing data from a database that is running in an on-premises data center. An Application Performance Management tool that can discover ALL application dependencies no matter where they are located and give end-to-end visibility into transactions running across hybrid environments would be critical.
Payal Chakravarty
Offering Leader, APM Cloud Initiatives, IBM Cloud Integration

As workloads and applications shift from private to public clouds, application performance is liable to be impacted due to changes in the compute and network infrastructure. To monitor, manage and deliver the best end user experience, Application Performance Management tools must seamlessly instrument the edge device and the infrastructure to measure application reachability, availability and performance; integrate the data from different users and locations and present in a simple intuitive interface to different stakeholders, including the application owner and service delivery.
Balaji Venkatraman
Director of Product Management, IT Operations Management, Hewlett Packard Enterprise

3. END USER EXPERIENCE MONITORING

Without access to public cloud infrastructure, IT Ops teams face challenges in ensuring excellent end user experience for applications delivered via hybrid cloud environments, and cloud vendors' SLAs cover only the infrastructure under their control. Monitoring the actual end user experience from the point of consumption, the user's device, overcomes the limitations faced by other approaches which require access to the application code, involve proxy servers or load balancers, or which only emulate end user experience.
Mike Marks
Chief Product Evangelist, Aternity

According to reviews of APM solutions on IT Central Station, the best way to ensure app performance in a hybrid cloud environment is to start with end user experience management. Reviewers talk about the success they have with real user monitoring, synthetic monitoring, and other tools for monitoring end user experience across public and private clouds.
Russell Rothstein
Founder and CEO, IT Central Station

Click here to read the latest APM product reviews on IT Central Station

A primary driver for hybrid cloud deployments is the ability to retain critical systems of record that are not candidates to move off-premise or to a public cloud environment. Enterprise customers need to have deep visibility into "modern" application tiers, where an agent can be deployed within an application or server and dynamically discover and configure itself, as well as the need to provide agent-less deep visibility into applications and end users where agents are not allowed or not technically possible to deploy. The critical element, therefore, in addressing application performance within a hybrid cloud deployment is to tackle all types of application environments, including ones that require agent-less instrumentation, while focusing on end user experience as the critical measure of performance.
Michael Masterson
Director, Strategic Business Development, Dynatrace

In hybrid cloud architectures it is critical to understand everything you measure in the context of what it means to the user experience. While it's easy to collect mountains of data from each cloud provider about response times, availability, and resource consumption, none of this matters if we can't understand how changes in these metrics affect the user's satisfaction with our application.
Buddy Brewer
SVP of Products, SOASTA

Hybrid cloud environments offer organizations a great deal of flexibility, by allowing them to move workloads across public and private cloud resources as computing needs and costs change. However, it's important to remember that the public portion of hybrid cloud environments is a shared utility. This means when an organization relying on the public cloud is experiencing a peak traffic period, chances are their "neighbors" in the cloud could be experiencing peaks as well – which could mean degraded performance for everyone. Organizations that depend on the public cloud in any way need to constantly measure their own end users' experience of cloud applications, which requires monitoring from inside the firewall. In addition to maintaining visibility into the performance of systems that are critical for productivity, monitoring from within the organization's network can play a valuable role in SLA enforcement for cloud services.
Dennis Callaghan
Director of Industry Innovation, Catchpoint

4. INTEGRATED USER EXPERIENCE DATA FROM MULTIPLE SOURCES

Hybrid cloud argues for a strategic approach to application performance and user experience monitoring and management as suggested in the PADS Framework. Integration is essential in an environment where the user traverses multiple domains. Integrated user experience data brings context to cloud metrics. This begins with left-shifting application performance in DevOps, and extends to having visibility Into the repositories where containers reside. The objective is to significantly reduce the friction of cloud-to-cloud deployment and migration.
Gabe Lowy
Technology Analyst and Founder of TechTonics Advisors

Read 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 2, covering NPM, ITOA and BTM.

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 ...

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

For years the IT industry debated about whether and when IT organizations would move everything to the cloud. Today's answer to this question is the hybrid cloud or hybrid IT – a combination of private and public clouds with traditional on-premise infrastructure, leveraging the best of all three worlds. A recent survey conducted by IDG Research Services for EMC revealed that 83% of respondents currently use or plan to use a hybrid cloud environment, and 73% agree that a hybrid cloud model creates a path to digital business.

It is clear that hybrid cloud offers many advantages. In a blog on APMdigest, Kong Yang of Solarwinds said, "The top three hybrid IT benefits by weighted rank are infrastructure cost-reduction, increased infrastructure flexibility/agility and relieving internal IT personnel of day-to-day management of some infrastructure, respectively."

However, as more enterprises move to hybrid cloud, they face new challenges for managing the performance of applications, such as limited visibility and control. Referencing a survey on public cloud, Antonio Piraino of ScienceLogic said, "A staggering 82 percent are unable to ensure optimum performance, health and availability of public cloud workloads due to lack of advanced visibility into the public cloud infrastructure."

In a recent blog on hybrid cloud, Shamus McGillicuddy, Senior Analyst, Network Management at Enterprise Management Associates (EMA), warns, "Enterprises who are implementing hybrid clouds will need to evaluate the readiness of their performance management to support these new cloud environments."

To address this new set of challenges, 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. The result is a detailed list of tools and approaches, and related insights, to guide enterprises as they migrate to hybrid cloud.

Some of the recommendations are new twists on tools you are familiar with or combinations of tools that our experts say are essential in the new world of hybrid cloud, but all of them require a new way of looking at the challenge of application performance.

The list will be posted in installments all week. Part 1 covers APM and End-User Experience Monitoring.

1. APPLICATION PERFORMANCE MANAGEMENT (APM)

There is often a misconception that hybrid cloud implies that an application (or components of an application) are running in two places concurrently. At least today, this is rarely the case, although in a future that increasingly includes things like containers and microservices this duality of execution may become more of a reality. Another way of saying this is that we don't need to make this more complex than it is. Today I would simply state that you should start with ensuring that you have visibility into the end user experience for both the cloud and on-premise applications. Use this point of observability to provide early warning indicators of potential problems. Depending upon the application type, you may be able to add instrumentation to help with localization of the problem domain through tracing and other activity (you may be able to substitute a network-based approach for this as well, if for example, cloud-based instrumentation proved to somehow be problematic). Then, through the same or additional agent technology you may have to delve more deeply into application and/or system internals to move closer to a root cause analysis. Of course, what I've basically described is the need for several of the elements that go into our 5 dimensional model of APM. All of this can be performed today with many existing SaaS or on-premise-based APM products as we point out in our latest Magic Quadrant. As I indicated earlier, this gets more complicated if in future hybrid cloud environments we start encountering more cloud native applications which have the twin challenges of scale and ephemerality, among other things, to deal with.
Cameron Haight
Research VP, IT Operations, Gartner

2. CLOUD-READY APM

Application Performance Management Platforms/Suites are becoming increasingly important as more companies roll out complex hybrid services. However one key factor to remember in choosing such a platform is that support for hybrid cloud also means support for the integration platforms that are intrinsic to creating hybrid services. In other words, APM solutions must be able to support the integration points that are foundational elements to hybrid delivery. Specifically, the APM solution needs to be "cloud ready" in its ability to consume and analyze metrics from API Gateways, messaging platforms, and/or other integration points to consolidate that information into APM dashboards and reports.
Julie Craig
Research Director, Application Management, Enterprise Management Associates (EMA)

Today's applications can have components spread across cloud, on premises, in containers and accessed through APIs and more. A modern approach to Application Performance Management allows you to see all of the elements and more importantly, reduce the complexity of what you are seeing so that you can quickly address performance issues, no matter where they reside.
Aruna Ravichandran
VP, DevOps Product and Solutions Marketing, CA Technologies

With the evolution of hybrid cloud, applications can now span across environments. For example, an application could be running on a public cloud platform to take advantage of autoscaling and be accessing data from a database that is running in an on-premises data center. An Application Performance Management tool that can discover ALL application dependencies no matter where they are located and give end-to-end visibility into transactions running across hybrid environments would be critical.
Payal Chakravarty
Offering Leader, APM Cloud Initiatives, IBM Cloud Integration

As workloads and applications shift from private to public clouds, application performance is liable to be impacted due to changes in the compute and network infrastructure. To monitor, manage and deliver the best end user experience, Application Performance Management tools must seamlessly instrument the edge device and the infrastructure to measure application reachability, availability and performance; integrate the data from different users and locations and present in a simple intuitive interface to different stakeholders, including the application owner and service delivery.
Balaji Venkatraman
Director of Product Management, IT Operations Management, Hewlett Packard Enterprise

3. END USER EXPERIENCE MONITORING

Without access to public cloud infrastructure, IT Ops teams face challenges in ensuring excellent end user experience for applications delivered via hybrid cloud environments, and cloud vendors' SLAs cover only the infrastructure under their control. Monitoring the actual end user experience from the point of consumption, the user's device, overcomes the limitations faced by other approaches which require access to the application code, involve proxy servers or load balancers, or which only emulate end user experience.
Mike Marks
Chief Product Evangelist, Aternity

According to reviews of APM solutions on IT Central Station, the best way to ensure app performance in a hybrid cloud environment is to start with end user experience management. Reviewers talk about the success they have with real user monitoring, synthetic monitoring, and other tools for monitoring end user experience across public and private clouds.
Russell Rothstein
Founder and CEO, IT Central Station

Click here to read the latest APM product reviews on IT Central Station

A primary driver for hybrid cloud deployments is the ability to retain critical systems of record that are not candidates to move off-premise or to a public cloud environment. Enterprise customers need to have deep visibility into "modern" application tiers, where an agent can be deployed within an application or server and dynamically discover and configure itself, as well as the need to provide agent-less deep visibility into applications and end users where agents are not allowed or not technically possible to deploy. The critical element, therefore, in addressing application performance within a hybrid cloud deployment is to tackle all types of application environments, including ones that require agent-less instrumentation, while focusing on end user experience as the critical measure of performance.
Michael Masterson
Director, Strategic Business Development, Dynatrace

In hybrid cloud architectures it is critical to understand everything you measure in the context of what it means to the user experience. While it's easy to collect mountains of data from each cloud provider about response times, availability, and resource consumption, none of this matters if we can't understand how changes in these metrics affect the user's satisfaction with our application.
Buddy Brewer
SVP of Products, SOASTA

Hybrid cloud environments offer organizations a great deal of flexibility, by allowing them to move workloads across public and private cloud resources as computing needs and costs change. However, it's important to remember that the public portion of hybrid cloud environments is a shared utility. This means when an organization relying on the public cloud is experiencing a peak traffic period, chances are their "neighbors" in the cloud could be experiencing peaks as well – which could mean degraded performance for everyone. Organizations that depend on the public cloud in any way need to constantly measure their own end users' experience of cloud applications, which requires monitoring from inside the firewall. In addition to maintaining visibility into the performance of systems that are critical for productivity, monitoring from within the organization's network can play a valuable role in SLA enforcement for cloud services.
Dennis Callaghan
Director of Industry Innovation, Catchpoint

4. INTEGRATED USER EXPERIENCE DATA FROM MULTIPLE SOURCES

Hybrid cloud argues for a strategic approach to application performance and user experience monitoring and management as suggested in the PADS Framework. Integration is essential in an environment where the user traverses multiple domains. Integrated user experience data brings context to cloud metrics. This begins with left-shifting application performance in DevOps, and extends to having visibility Into the repositories where containers reside. The objective is to significantly reduce the friction of cloud-to-cloud deployment and migration.
Gabe Lowy
Technology Analyst and Founder of TechTonics Advisors

Read 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 2, covering NPM, ITOA and BTM.

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 ...