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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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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 ...

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

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...