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APM and Observability: Cutting Through the Confusion — Part 6

Pete Goldin
APMdigest

APM and Observability share a core use case: keeping applications running reliably and reducing mean time to resolution when issues occur, according to Rakesh Gupta, Head of Product Management at Observe.

Start with: APM and Observability - Cutting Through the Confusion - Part 5

Despite this similarity, however, the experts say that APM and Observability serve fundamentally different use cases. Some of this was covered in earlier parts of this series, but the experts delve deeper into the differences of use cases here:

Routine Health Checks vs. Deep Diagnostics

APM and Observability cater to fundamentally different, though related, use cases. APM is typically used for monitoring known application performance indicators, tracking service level objectives (SLOs), and quickly diagnosing common issues within predefined dashboards and workflows. Observability, conversely, shines when dealing with novelty and complexity, such as investigating system-wide issues, debugging unpredictable problems in distributed environments, and exploring hypotheses about system behavior that weren't anticipated during design or initial monitoring setup. One helps with routine health checks, the other with deep diagnostics of unfamiliar ailments.
Juraci Paixão Kröhling
Software Engineer, OllyGarden

Performance vs. the Big Picture

APM and observability have different use cases. APM deals with aspects such as tracking predefined metrics, alerting on thresholds, and providing dashboards and diagnostics that give a prescriptive look into application health. These tools should be utilized in cases where the end goal is helping software developers, testers, and quality assurance professionals quickly identify and resolve performance issues. They are also extremely useful in a production monitoring context when the application's behaviors are well understood, allowing you to monitor for signals that indicate potential problems that could lead to system degradation or failure.

On the other hand, observability is all about inferring the state of the application — a classic "we don't know what we don't know" scenario. Rather than honing in on just application performance itself, observability is focused on the larger challenge of understanding complete systems. These tools should be used to understand and troubleshoot particularly complex or unknown system behavior. As applications become increasingly AI-driven and/or AI-augmented, the discipline of observability will take a larger role.
Bryan Cole
Director of Customer Engineering, Tricentis

Knowns vs. Unknowns

APM and observability serve different, though often complementary, purposes. APM is typically focused on monitoring application health, tracking performance metrics, and ensuring adherence to service-level objectives. It's particularly effective for identifying and resolving common issues like slow response times or elevated error rates. Observability, by contrast, is designed for more complex environments — think distributed systems, microservices, and dynamic cloud-native architectures. It gives teams the ability to dig into system-wide anomalies, troubleshoot elusive or intermittent problems, and gain a deeper understanding of system behavior by correlating metrics, logs, and traces. In that sense, APM handles the known and expected, while observability equips teams to explore the unknown.
Arun Balachandran
Senior Product Marketing Manager, ManageEngine APM Solutions

APM is often used to track application health, SLAs, and response times. Observability supports broader use cases such as release validation, performance optimization across distributed systems, incident prevention, and cross-team collaboration. In short, observability is about exploring the unknowns, while APM focuses on managing the knowns.
Andreas Grabner
Fellow DevRel and CNCF Ambassador, Dynatrace

External vs. Internal

Modern, robust APM tools can test everything from individual database queries to API calls and beyond. However, the focus is on how those elements are experienced from an external point of view, rather than how it works from inside the application (or website, or whatever) itself. On the other side of the fence, if your decision was to address the imaginary issue with a solution that was observability-centric, it would indicate that your first concern was from an inside-the-code perspective; and that you were worried not so much about predictable ways the application (or website, or whatever) could fail, but rather on all the unpredictable things that might happen down the road. The so-called "black swan" events.
Leon Adato
Principal Technology Advocate, Catchpoint

Reactive vs. Proactive

They are related in that both are concerned with ensuring that infrastructure/applications are available and performing as expected, but there are two key differences: APM systems are typically reactive, based on predefined thresholds, and APM has specific capabilities around ensuring application availability and business process KPIs. Observability tools are focused on overall infrastructure health and are proactive, detecting anomalies and assisting with triage across the entire environment.
Paul Appleby
CEO, Virtana

Monolithic vs. Cloud

There's a lot of overlap in the use cases, but I think they shine brightest in different contexts. APM is often a fantastic tool for large monolithic web applications, e-commerce platforms, and mobile app performance. Observability is built specifically for complex cloud environments. Observability use cases extend to microservice architectures and other distributed systems, cloud-native environments, CI/CD workflows, incident response, and post-incident analysis.
Emily Nakashima
VP of Engineering, Honeycomb

VMs vs. Containers

It depends on the organization's specific situation. If an organization has only modern, containerized architectures, then observability tools alone might suffice. However, many organizations are likely still running a mix of older (VM-based) and newer (containerized) architectures. In those cases, they probably need both APM tools (for the older environments) and observability tools (for the newer ones) because the newer tools are not replacements in old circumstances.
Jeff Cobb
Global Head of Product & Design, Chronosphere

Applications vs. Network

APM is focused on applications and performance. Therefore, APM is not going to directly address, say, network reliability. While network reliability may be impacting your application performance (which it unfortunately often can and does), and an APM tool might catch networking issues as the underlying culprit, you will need a separate suite of network observability tools and highly different expertise to actually conduct packet-level analytics, or to diagnose why a network route keeps flapping, or to diagnose and mitigate other issues.
Peter Corless
Director, Product Marketing, StarTree

IT vs. Business

What's interesting is that executives increasingly see broader potential in unified observability data. Many organizations express interest in doing business analytics on their telemetry data or joining it with business metrics — which is a capability that traditional APM tools can't do well.
Rakesh Gupta
Head of Product Management, Observe

Start with: APM and Observability - Cutting Through the Confusion - Part 7, covering the roles that use APM and Observability.

Pete Goldin is Editor and Publisher of APMdigest

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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 ...

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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 ...

APM and Observability: Cutting Through the Confusion — Part 6

Pete Goldin
APMdigest

APM and Observability share a core use case: keeping applications running reliably and reducing mean time to resolution when issues occur, according to Rakesh Gupta, Head of Product Management at Observe.

Start with: APM and Observability - Cutting Through the Confusion - Part 5

Despite this similarity, however, the experts say that APM and Observability serve fundamentally different use cases. Some of this was covered in earlier parts of this series, but the experts delve deeper into the differences of use cases here:

Routine Health Checks vs. Deep Diagnostics

APM and Observability cater to fundamentally different, though related, use cases. APM is typically used for monitoring known application performance indicators, tracking service level objectives (SLOs), and quickly diagnosing common issues within predefined dashboards and workflows. Observability, conversely, shines when dealing with novelty and complexity, such as investigating system-wide issues, debugging unpredictable problems in distributed environments, and exploring hypotheses about system behavior that weren't anticipated during design or initial monitoring setup. One helps with routine health checks, the other with deep diagnostics of unfamiliar ailments.
Juraci Paixão Kröhling
Software Engineer, OllyGarden

Performance vs. the Big Picture

APM and observability have different use cases. APM deals with aspects such as tracking predefined metrics, alerting on thresholds, and providing dashboards and diagnostics that give a prescriptive look into application health. These tools should be utilized in cases where the end goal is helping software developers, testers, and quality assurance professionals quickly identify and resolve performance issues. They are also extremely useful in a production monitoring context when the application's behaviors are well understood, allowing you to monitor for signals that indicate potential problems that could lead to system degradation or failure.

On the other hand, observability is all about inferring the state of the application — a classic "we don't know what we don't know" scenario. Rather than honing in on just application performance itself, observability is focused on the larger challenge of understanding complete systems. These tools should be used to understand and troubleshoot particularly complex or unknown system behavior. As applications become increasingly AI-driven and/or AI-augmented, the discipline of observability will take a larger role.
Bryan Cole
Director of Customer Engineering, Tricentis

Knowns vs. Unknowns

APM and observability serve different, though often complementary, purposes. APM is typically focused on monitoring application health, tracking performance metrics, and ensuring adherence to service-level objectives. It's particularly effective for identifying and resolving common issues like slow response times or elevated error rates. Observability, by contrast, is designed for more complex environments — think distributed systems, microservices, and dynamic cloud-native architectures. It gives teams the ability to dig into system-wide anomalies, troubleshoot elusive or intermittent problems, and gain a deeper understanding of system behavior by correlating metrics, logs, and traces. In that sense, APM handles the known and expected, while observability equips teams to explore the unknown.
Arun Balachandran
Senior Product Marketing Manager, ManageEngine APM Solutions

APM is often used to track application health, SLAs, and response times. Observability supports broader use cases such as release validation, performance optimization across distributed systems, incident prevention, and cross-team collaboration. In short, observability is about exploring the unknowns, while APM focuses on managing the knowns.
Andreas Grabner
Fellow DevRel and CNCF Ambassador, Dynatrace

External vs. Internal

Modern, robust APM tools can test everything from individual database queries to API calls and beyond. However, the focus is on how those elements are experienced from an external point of view, rather than how it works from inside the application (or website, or whatever) itself. On the other side of the fence, if your decision was to address the imaginary issue with a solution that was observability-centric, it would indicate that your first concern was from an inside-the-code perspective; and that you were worried not so much about predictable ways the application (or website, or whatever) could fail, but rather on all the unpredictable things that might happen down the road. The so-called "black swan" events.
Leon Adato
Principal Technology Advocate, Catchpoint

Reactive vs. Proactive

They are related in that both are concerned with ensuring that infrastructure/applications are available and performing as expected, but there are two key differences: APM systems are typically reactive, based on predefined thresholds, and APM has specific capabilities around ensuring application availability and business process KPIs. Observability tools are focused on overall infrastructure health and are proactive, detecting anomalies and assisting with triage across the entire environment.
Paul Appleby
CEO, Virtana

Monolithic vs. Cloud

There's a lot of overlap in the use cases, but I think they shine brightest in different contexts. APM is often a fantastic tool for large monolithic web applications, e-commerce platforms, and mobile app performance. Observability is built specifically for complex cloud environments. Observability use cases extend to microservice architectures and other distributed systems, cloud-native environments, CI/CD workflows, incident response, and post-incident analysis.
Emily Nakashima
VP of Engineering, Honeycomb

VMs vs. Containers

It depends on the organization's specific situation. If an organization has only modern, containerized architectures, then observability tools alone might suffice. However, many organizations are likely still running a mix of older (VM-based) and newer (containerized) architectures. In those cases, they probably need both APM tools (for the older environments) and observability tools (for the newer ones) because the newer tools are not replacements in old circumstances.
Jeff Cobb
Global Head of Product & Design, Chronosphere

Applications vs. Network

APM is focused on applications and performance. Therefore, APM is not going to directly address, say, network reliability. While network reliability may be impacting your application performance (which it unfortunately often can and does), and an APM tool might catch networking issues as the underlying culprit, you will need a separate suite of network observability tools and highly different expertise to actually conduct packet-level analytics, or to diagnose why a network route keeps flapping, or to diagnose and mitigate other issues.
Peter Corless
Director, Product Marketing, StarTree

IT vs. Business

What's interesting is that executives increasingly see broader potential in unified observability data. Many organizations express interest in doing business analytics on their telemetry data or joining it with business metrics — which is a capability that traditional APM tools can't do well.
Rakesh Gupta
Head of Product Management, Observe

Start with: APM and Observability - Cutting Through the Confusion - Part 7, covering the roles that use APM and Observability.

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

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 ...