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Why Cloud Consumers Need “Objective” Application Performance Management

Jim Young

The long anticipated rise of cloud computing is finally taking hold, with analysts reporting more investment in public clouds than private clouds, and suggesting that half of all production applications will be running on public clouds in three or four years.

The allure of public clouds springs from advantages like improved service scalability, reduced operational costs, and an increased focus on business goals and strategies instead of the technology needed to pursue them. However, there is a cost to that flexibility and economy, in reduced visibility of application and infrastructure health. Without direct control over the cloud infrastructure itself, traditional application performance management (APM) tools may prove impractical to deploy and manage.

I recently read a story about a war of words between a leading platform as a service vendor and a disgruntled customer, who discovered that they weren’t actually getting the amount of virtual computing capacity that they had been told they were getting.

Putting aside the customer’s justifiable indignation at not getting the resources that they believed they were paying for, the real story for a cloud consumer here (or an APM Product Manager) is that the tools they were using to monitor their workloads didn’t really provide them with a complete story. Then, when the continued mystery warranted a deeper-dive tool, it appears that they were pressured or influenced into purchasing a particular cloud APM tool because of a relationship between that tool vendor and the PaaS provider.

This suggests (and logic supports) that customers are better off using objective APM tools when monitoring workloads on public clouds, whether those workloads are running on a Platform as a Service (PaaS) solution like Heroku, or an Infrastructure as a Service (IaaS) solution like Amazon or Rackspace.

We generally espouse such a practice to help a customer maintain a posture of portability, so they can nimbly move workloads around to different cloud platforms, yet maintain continuity in their real-time and historical view of application health, without having to train their eyes on a new health dashboard whenever they move their workloads. We can employ the slightly suspicious sounding argument that a customer should not necessarily rely on his service provider for monitoring tools, since that provider has a vested interest in painting a rosy picture. Even in the presence of SLAs, a cloud tenant with no access to the infrastructure is somewhat at the mercy of his provider for performance reporting. An APM solution that the customer can deploy and configure himself provides a level of “checks and balances” oversight.

It can be impractical for customers to deploy legacy monitoring tools when moving to public clouds, so there is a need for a solution that can be deployed within those public clouds, in their own little sphere of control where their application VMs reside. By adopting an elastic and scalable ­yet small and easy to deploy architecture, as well as the ability to embed additional monitoring technology into base VM images, this solution enables robust APM, even when users can only deploy simple Linux VMs to someone else's cloud.

Jim Young is Information Development Manager, IBM Cloud and Smarter Infrastructure

Related Links:

www.ibm.com

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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 ...

Why Cloud Consumers Need “Objective” Application Performance Management

Jim Young

The long anticipated rise of cloud computing is finally taking hold, with analysts reporting more investment in public clouds than private clouds, and suggesting that half of all production applications will be running on public clouds in three or four years.

The allure of public clouds springs from advantages like improved service scalability, reduced operational costs, and an increased focus on business goals and strategies instead of the technology needed to pursue them. However, there is a cost to that flexibility and economy, in reduced visibility of application and infrastructure health. Without direct control over the cloud infrastructure itself, traditional application performance management (APM) tools may prove impractical to deploy and manage.

I recently read a story about a war of words between a leading platform as a service vendor and a disgruntled customer, who discovered that they weren’t actually getting the amount of virtual computing capacity that they had been told they were getting.

Putting aside the customer’s justifiable indignation at not getting the resources that they believed they were paying for, the real story for a cloud consumer here (or an APM Product Manager) is that the tools they were using to monitor their workloads didn’t really provide them with a complete story. Then, when the continued mystery warranted a deeper-dive tool, it appears that they were pressured or influenced into purchasing a particular cloud APM tool because of a relationship between that tool vendor and the PaaS provider.

This suggests (and logic supports) that customers are better off using objective APM tools when monitoring workloads on public clouds, whether those workloads are running on a Platform as a Service (PaaS) solution like Heroku, or an Infrastructure as a Service (IaaS) solution like Amazon or Rackspace.

We generally espouse such a practice to help a customer maintain a posture of portability, so they can nimbly move workloads around to different cloud platforms, yet maintain continuity in their real-time and historical view of application health, without having to train their eyes on a new health dashboard whenever they move their workloads. We can employ the slightly suspicious sounding argument that a customer should not necessarily rely on his service provider for monitoring tools, since that provider has a vested interest in painting a rosy picture. Even in the presence of SLAs, a cloud tenant with no access to the infrastructure is somewhat at the mercy of his provider for performance reporting. An APM solution that the customer can deploy and configure himself provides a level of “checks and balances” oversight.

It can be impractical for customers to deploy legacy monitoring tools when moving to public clouds, so there is a need for a solution that can be deployed within those public clouds, in their own little sphere of control where their application VMs reside. By adopting an elastic and scalable ­yet small and easy to deploy architecture, as well as the ability to embed additional monitoring technology into base VM images, this solution enables robust APM, even when users can only deploy simple Linux VMs to someone else's cloud.

Jim Young is Information Development Manager, IBM Cloud and Smarter Infrastructure

Related Links:

www.ibm.com

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 ...