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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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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 Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...