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

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 2 covers BTM, NPM and ITOA.

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

5. BUSINESS TRANSACTION MONITORING

The secret to ensuring app performance in hybrid cloud or other complex environments is to focus on the business transaction. Such transactions connect the end-user experience to the end-to-end performance of all relevant elements of the architecture as the transaction traverses whatever cloud environments and on-premise systems are necessary to achieve the business goal. In contrast, if you think of a hybrid cloud environment as separate environments that you've connected together, application performance can be difficult to understand, let alone optimize.
Jason Bloomberg
President, Intellyx

The foundation of any APM tool is the collection and analysis of transactional data captured at the application layer. In hybrid cloud environments, users must ensure that the APM deployment is able to instrument the nodes which support application traffic flows, regardless of where that code executes, in order to capture a complete view of the code execution path(s) and therefore any performance bottlenecks. Failure to do so ensures that performance engineers will have application performance blind spots – they will easily find the problems in the areas they can see, but inevitably, performance issues in the blind spots will go undetected. Having complete visibility across the stack enables organizations to proactively address performance issues before they impact the business and ensure a consistent, high quality end user experience.
John Maxwell
VP, Product Management, Dell Software

The main thing is to ensure that the flow of activity between the private and the public infrastructure and applications is maintained, as if both infrastructures reside in the same network. The application's end users should not be affected in any way by the hybrid approach. Transaction tracing, which must be completely transparent and agnostic to the underlying operating systems and the hardware (physical or logical) it utilizes, is the key to assuring performance. By constantly monitoring the flow from public-private-public-private etc. components, we can assure the overall solution is performing as designed.
Zvika Meiseles
CTO, Correlsense

6. AUTOSCALING

It isn't enough to just have end-to-end visibility into a business transaction deployed across a distributed hybrid cloud environment. The ability to auto-scale the cloud applications using health rules and policies based on a combination of application and infrastructure metrics is also important. For example, If the average response time deteriorates, auto-scaling an e-commerce application deployed in an AWS infrastructure will come in handy. This not only helps deliver exceptional end-user experience, but also minimizes the operational cost by preventing over provisioning of the infrastructure.
Anand Akela
Director, Product Marketing, AppDynamics

7. NETWORK PERFORMANCE MONITORING (NPM)

To network infrastructure teams, I recommend that they evaluate the tools they use for network performance management. They need to determine whether their existing tools can provide them with end-to-end visibility across internal, private cloud infrastructure and external, public cloud infrastructure, as well as visibility into the network connectivity that links the two.
Shamus McGillicuddy
Senior Analyst, Network Management, Enterprise Management Associates (EMA)

With hybrid cloud, application performance suddenly becomes dependent on network and Internet performance – something that most application developers do not consider when they design their apps. As such, anyone moving applications to hybrid cloud needs to have a tool for providing end-to-end network performance from the end-user through the WAN to the application.
Damian Roskill
CMO, AppNeta

8. NPM + END USER EXPERIENCE MONITORING

The tools you've been using to monitor applications in your data center can't see far enough into SaaS-based applications. Since the network is the resource that connects your user and your applications, wherever they may be hosted, network performance monitoring provides you the needed visibility as you transition to the cloud. Pick a platform with a focus on end-user experience; a combination of network performance and end-user experience is an effective monitoring technology to ensure application performance in the hybrid IT environment.
Ulrica de Fort-Menares
VP of Product Strategy, LiveAction

9. ITOA

IT Operations Analytics technology detecting the latest state of the hybrid cloud environments and tracking history of the introduced changes is essential to creating visibility into an otherwise black box of the cloud.
Sasha Gilenson
CEO, Evolven

Read Sasha Gilenson's blog: ITOA - Essential for Hybrid Cloud

Hybrid cloud environments add another dimension of variability to the already diverse set of transaction types present in today's complex IT infrastructures. When attempting to diagnose application performance issues, it can be especially challenging to assess the impact caused by the various network paths present in hybrid cloud environments. To ensure optimal performance, IT Operations and DevOps teams must turn to advanced behavioral analytics to detect anomalies in various application performance metrics, and then statistically link them to other anomalies that may be present in various network and cloud-based performance metrics. Traditional network monitoring that looks only at aggregate bandwidth levels is insufficient to correlate with specific application level problems. Behavioral analytics employs automated machine learning algorithms to create baseline models of normal behaviors of all transaction types within the hybrid cloud network, detect unusual behaviors, and link them to other application anomalies, enabling speedy root cause identification of performance issues.
Mike Paquette
VP of Products, Prelert

10. LOG ANALYTICS

You need more than one tool and approach, but the one I recommend is a central log analytics solution. Hybrid cloud environments are complex systems with a variety of distributed components that make up your app. Each of these components (hopefully) writes log data, and only if you collect all those logs in one central place and have the ability to analyze it in meaningful ways you will be able to identify and eliminate performance bottlenecks. Consolidated and aggregated log files from all your components will give you a cohesive picture of that entire modular system. Do not allow blind spots!
Jon Gifford
Founder and Chief Search Officer, Loggly

Read 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 3, covering monitoring and visibility.

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 2

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 2 covers BTM, NPM and ITOA.

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

5. BUSINESS TRANSACTION MONITORING

The secret to ensuring app performance in hybrid cloud or other complex environments is to focus on the business transaction. Such transactions connect the end-user experience to the end-to-end performance of all relevant elements of the architecture as the transaction traverses whatever cloud environments and on-premise systems are necessary to achieve the business goal. In contrast, if you think of a hybrid cloud environment as separate environments that you've connected together, application performance can be difficult to understand, let alone optimize.
Jason Bloomberg
President, Intellyx

The foundation of any APM tool is the collection and analysis of transactional data captured at the application layer. In hybrid cloud environments, users must ensure that the APM deployment is able to instrument the nodes which support application traffic flows, regardless of where that code executes, in order to capture a complete view of the code execution path(s) and therefore any performance bottlenecks. Failure to do so ensures that performance engineers will have application performance blind spots – they will easily find the problems in the areas they can see, but inevitably, performance issues in the blind spots will go undetected. Having complete visibility across the stack enables organizations to proactively address performance issues before they impact the business and ensure a consistent, high quality end user experience.
John Maxwell
VP, Product Management, Dell Software

The main thing is to ensure that the flow of activity between the private and the public infrastructure and applications is maintained, as if both infrastructures reside in the same network. The application's end users should not be affected in any way by the hybrid approach. Transaction tracing, which must be completely transparent and agnostic to the underlying operating systems and the hardware (physical or logical) it utilizes, is the key to assuring performance. By constantly monitoring the flow from public-private-public-private etc. components, we can assure the overall solution is performing as designed.
Zvika Meiseles
CTO, Correlsense

6. AUTOSCALING

It isn't enough to just have end-to-end visibility into a business transaction deployed across a distributed hybrid cloud environment. The ability to auto-scale the cloud applications using health rules and policies based on a combination of application and infrastructure metrics is also important. For example, If the average response time deteriorates, auto-scaling an e-commerce application deployed in an AWS infrastructure will come in handy. This not only helps deliver exceptional end-user experience, but also minimizes the operational cost by preventing over provisioning of the infrastructure.
Anand Akela
Director, Product Marketing, AppDynamics

7. NETWORK PERFORMANCE MONITORING (NPM)

To network infrastructure teams, I recommend that they evaluate the tools they use for network performance management. They need to determine whether their existing tools can provide them with end-to-end visibility across internal, private cloud infrastructure and external, public cloud infrastructure, as well as visibility into the network connectivity that links the two.
Shamus McGillicuddy
Senior Analyst, Network Management, Enterprise Management Associates (EMA)

With hybrid cloud, application performance suddenly becomes dependent on network and Internet performance – something that most application developers do not consider when they design their apps. As such, anyone moving applications to hybrid cloud needs to have a tool for providing end-to-end network performance from the end-user through the WAN to the application.
Damian Roskill
CMO, AppNeta

8. NPM + END USER EXPERIENCE MONITORING

The tools you've been using to monitor applications in your data center can't see far enough into SaaS-based applications. Since the network is the resource that connects your user and your applications, wherever they may be hosted, network performance monitoring provides you the needed visibility as you transition to the cloud. Pick a platform with a focus on end-user experience; a combination of network performance and end-user experience is an effective monitoring technology to ensure application performance in the hybrid IT environment.
Ulrica de Fort-Menares
VP of Product Strategy, LiveAction

9. ITOA

IT Operations Analytics technology detecting the latest state of the hybrid cloud environments and tracking history of the introduced changes is essential to creating visibility into an otherwise black box of the cloud.
Sasha Gilenson
CEO, Evolven

Read Sasha Gilenson's blog: ITOA - Essential for Hybrid Cloud

Hybrid cloud environments add another dimension of variability to the already diverse set of transaction types present in today's complex IT infrastructures. When attempting to diagnose application performance issues, it can be especially challenging to assess the impact caused by the various network paths present in hybrid cloud environments. To ensure optimal performance, IT Operations and DevOps teams must turn to advanced behavioral analytics to detect anomalies in various application performance metrics, and then statistically link them to other anomalies that may be present in various network and cloud-based performance metrics. Traditional network monitoring that looks only at aggregate bandwidth levels is insufficient to correlate with specific application level problems. Behavioral analytics employs automated machine learning algorithms to create baseline models of normal behaviors of all transaction types within the hybrid cloud network, detect unusual behaviors, and link them to other application anomalies, enabling speedy root cause identification of performance issues.
Mike Paquette
VP of Products, Prelert

10. LOG ANALYTICS

You need more than one tool and approach, but the one I recommend is a central log analytics solution. Hybrid cloud environments are complex systems with a variety of distributed components that make up your app. Each of these components (hopefully) writes log data, and only if you collect all those logs in one central place and have the ability to analyze it in meaningful ways you will be able to identify and eliminate performance bottlenecks. Consolidated and aggregated log files from all your components will give you a cohesive picture of that entire modular system. Do not allow blind spots!
Jon Gifford
Founder and Chief Search Officer, Loggly

Read 28 Ways to Ensure Application Performance in the Hybrid Cloud - Part 3, covering monitoring and visibility.

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 ...