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3 Ways for Enterprises to Optimize APM and IPM Investments

John Kim

As enterprises leverage scarce hardware resources within increasingly virtualized environments, storage has proven to be a common culprit behind application performance issues. In response to this problem, sophisticated tools have emerged that allow IT organizations to understand and manage application storage performance requirements and deploy cost-effective infrastructure.

In parallel, public cloud proliferation is changing the traditional design methodology of the modern enterprise data center. While the long-term plans of many enterprises to migrate applications to public cloud remain intact, on-premise IT has exhibited tremendous resilience and undergone more of an evolution than revolution. New and innovative solutions have evolved to address the transition to the hybrid data center, and balancing the use of on-premise and cloud-based services will be another key challenge for IT organizations over the coming years.

A consistent theme is that control and visibility are essential to application performance assurance in any environment, and IT organizations continue to invest in both Application Performance Management (APM) and Infrastructure Performance Management (IPM) solutions. APM tools allow developers to optimize their code and enhance user experience, however, these tools often fail to examine and optimize the core infrastructure responsible for application delivery. IPM solutions have emerged as a meaningful complement to APM for this very reason.

1. Evaluate a Vendor's Ability to Scale

The complexity of infrastructure responsible for application delivery is quickly surpassing the limits of human comprehension, and performance problems are more difficult to find and less correlated to just the storage elements. Enterprises will need to rely on machine learning-based analytics and automation to identify and remediate root causes of performance degradation. APM/IPM solutions must work together seamlessly to make IT departments more efficient and to guarantee a positive experience for application users.

2. Insist on Real Time, End-To-End Performance Management

In a hybrid environment, end-to-end performance management is critical and must be truly end-to-end. Most claims of end-to-end infrastructure monitoring are driven by vendor marketing departments, while the products themselves only address a specific subset of the data center.

Enterprises must do their research. A solution that covers all key infrastructure components such as servers, network and storage offers a compelling option that can provide comprehensive data center performance monitoring in real time. Moreover, a performance management solution that covers current on-premise and future off-premise needs will be critical to the successful deployment of the hybrid data center.

3. Make Your Customer Demands Known

Customer requirements and feedback define product roadmaps and the evolution of IT infrastructure on-premise, in the cloud, or a combination thereof. Enterprise customers must make their needs and demands known to APM/IPM vendors in a clear and detailed fashion. This collaboration will not only help deliver products of value but also help vendors better design products to address real world problems.

The data center environment of today is more complex than ever. If you want your IT organization to operate efficiently, you must be able to proactively identify and remediate application performance issues within the infrastructure with the context of the application. By focusing in these three areas – scalability, end-to-end visibility and collaboration – enterprises will get the most out of APM and IPM solutions and drive a concurrent evolution in their ability to manage IT.

John Kim is a Co-Founder and Managing Partner of HighBar Partners, an investor in Virtual Instruments.

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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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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 ...

3 Ways for Enterprises to Optimize APM and IPM Investments

John Kim

As enterprises leverage scarce hardware resources within increasingly virtualized environments, storage has proven to be a common culprit behind application performance issues. In response to this problem, sophisticated tools have emerged that allow IT organizations to understand and manage application storage performance requirements and deploy cost-effective infrastructure.

In parallel, public cloud proliferation is changing the traditional design methodology of the modern enterprise data center. While the long-term plans of many enterprises to migrate applications to public cloud remain intact, on-premise IT has exhibited tremendous resilience and undergone more of an evolution than revolution. New and innovative solutions have evolved to address the transition to the hybrid data center, and balancing the use of on-premise and cloud-based services will be another key challenge for IT organizations over the coming years.

A consistent theme is that control and visibility are essential to application performance assurance in any environment, and IT organizations continue to invest in both Application Performance Management (APM) and Infrastructure Performance Management (IPM) solutions. APM tools allow developers to optimize their code and enhance user experience, however, these tools often fail to examine and optimize the core infrastructure responsible for application delivery. IPM solutions have emerged as a meaningful complement to APM for this very reason.

1. Evaluate a Vendor's Ability to Scale

The complexity of infrastructure responsible for application delivery is quickly surpassing the limits of human comprehension, and performance problems are more difficult to find and less correlated to just the storage elements. Enterprises will need to rely on machine learning-based analytics and automation to identify and remediate root causes of performance degradation. APM/IPM solutions must work together seamlessly to make IT departments more efficient and to guarantee a positive experience for application users.

2. Insist on Real Time, End-To-End Performance Management

In a hybrid environment, end-to-end performance management is critical and must be truly end-to-end. Most claims of end-to-end infrastructure monitoring are driven by vendor marketing departments, while the products themselves only address a specific subset of the data center.

Enterprises must do their research. A solution that covers all key infrastructure components such as servers, network and storage offers a compelling option that can provide comprehensive data center performance monitoring in real time. Moreover, a performance management solution that covers current on-premise and future off-premise needs will be critical to the successful deployment of the hybrid data center.

3. Make Your Customer Demands Known

Customer requirements and feedback define product roadmaps and the evolution of IT infrastructure on-premise, in the cloud, or a combination thereof. Enterprise customers must make their needs and demands known to APM/IPM vendors in a clear and detailed fashion. This collaboration will not only help deliver products of value but also help vendors better design products to address real world problems.

The data center environment of today is more complex than ever. If you want your IT organization to operate efficiently, you must be able to proactively identify and remediate application performance issues within the infrastructure with the context of the application. By focusing in these three areas – scalability, end-to-end visibility and collaboration – enterprises will get the most out of APM and IPM solutions and drive a concurrent evolution in their ability to manage IT.

John Kim is a Co-Founder and Managing Partner of HighBar Partners, an investor in Virtual Instruments.

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 ...