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Legacy Application Performance Management (APM) vs Modern Observability - Part 1

Colin Fallwell
Sumo Logic

In this 3 part series, I will explore, contrast, and discuss the differences as well as the history of APM and the meteoric rise of Modern Observability, why these two are related but simultaneously are vastly different in outcome. Indeed, Modern Observability is disrupting the world, and organizations doing it right are realizing massive gains in innovation, reaping the benefits of higher performance and optimization across numerous dimensions including:

■ IT governance

■ Revenue growth

■ Vendor cost reduction

■ Tool Consolidation

■ DevOps toil and churn

■ Application performance and customer experiences

■ Reliability and Security

■ Employee satisfaction

■ Data Science and Business Analytics

■ AI-controlled automation (AIOps)

Modern Observability is becoming the foundation upon which organizations are able to reduce the toil and churn associated with capital spending across initiatives such as Cloud Migrations, App Modernization, Digital Transformation, and AIOps by leveraging new methodologies such as Observability-Driven-Development (ODD).

Traditional APM is a mature, vendor-led industry, and was built at a time when the world was developing monolithic, 3-tier architectures and when software was typically released once or twice a year. APM is a closed ecosystem, with patented protocols and agents which are deployed to run on every node, injected into runtimes with startup parameters, and have little to no impact on how software is designed or developed.

This is a good thing, right?

In contrast to Modern Observability, and for organizations moving to the cloud, APM is loaded with hidden costs and unintended consequences. From a process perspective, APM does not live within the developer ecosystem and has historically been funded by Ops teams or DevOps/SRE groups that have largely been out of the immediate workstream of software development. This nuance means developers have no real ownership interest in APM and don't feel compelled in taking responsibility for declaring what it means to make something "observable." What enterprises desire most are reliable pipelines of telemetry that provide accurate data inferring the internal state of systems including usage and behavioral insights of end-users, code execution, infrastructure health, and overall performance. Most developers have been poor adopters of APM.

A major characteristic of Modern Observability is in how it becomes designed into the fabric of the applications, services, and infrastructure by DevOps teams, implemented through models such as GitOps, which in turn provides numerous benefits to organizations that legacy APM really does not align to. It is within this point of view or context that I base my opinions on throughout this series. Many organizations still relying on APM vendors will struggle to increase the intrinsic value of data within the organization. It's my firm argument that the most important attribute of Modern Observability lies in its "programmable" nature, whereby the acquisition of telemetry becomes woven into the fabric of developing software and the services offered by anyone competing in this global software-driven economy.

There are many other dimensions of contrast, but I personally believe this to be the most important with respect to organizations embracing digital transformation, or for those that just want to improve maturity, growth, and innovation, or anyone wishing to own their own destiny when it comes to data intelligence.

In the next installment (Part 2) of this series, we dive into the history of APM and how it became a 6 Billion USD market and explore some of the challenges that come with APM.

Colin Fallwell is Field CTO of Sumo Logic

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Legacy Application Performance Management (APM) vs Modern Observability - Part 1

Colin Fallwell
Sumo Logic

In this 3 part series, I will explore, contrast, and discuss the differences as well as the history of APM and the meteoric rise of Modern Observability, why these two are related but simultaneously are vastly different in outcome. Indeed, Modern Observability is disrupting the world, and organizations doing it right are realizing massive gains in innovation, reaping the benefits of higher performance and optimization across numerous dimensions including:

■ IT governance

■ Revenue growth

■ Vendor cost reduction

■ Tool Consolidation

■ DevOps toil and churn

■ Application performance and customer experiences

■ Reliability and Security

■ Employee satisfaction

■ Data Science and Business Analytics

■ AI-controlled automation (AIOps)

Modern Observability is becoming the foundation upon which organizations are able to reduce the toil and churn associated with capital spending across initiatives such as Cloud Migrations, App Modernization, Digital Transformation, and AIOps by leveraging new methodologies such as Observability-Driven-Development (ODD).

Traditional APM is a mature, vendor-led industry, and was built at a time when the world was developing monolithic, 3-tier architectures and when software was typically released once or twice a year. APM is a closed ecosystem, with patented protocols and agents which are deployed to run on every node, injected into runtimes with startup parameters, and have little to no impact on how software is designed or developed.

This is a good thing, right?

In contrast to Modern Observability, and for organizations moving to the cloud, APM is loaded with hidden costs and unintended consequences. From a process perspective, APM does not live within the developer ecosystem and has historically been funded by Ops teams or DevOps/SRE groups that have largely been out of the immediate workstream of software development. This nuance means developers have no real ownership interest in APM and don't feel compelled in taking responsibility for declaring what it means to make something "observable." What enterprises desire most are reliable pipelines of telemetry that provide accurate data inferring the internal state of systems including usage and behavioral insights of end-users, code execution, infrastructure health, and overall performance. Most developers have been poor adopters of APM.

A major characteristic of Modern Observability is in how it becomes designed into the fabric of the applications, services, and infrastructure by DevOps teams, implemented through models such as GitOps, which in turn provides numerous benefits to organizations that legacy APM really does not align to. It is within this point of view or context that I base my opinions on throughout this series. Many organizations still relying on APM vendors will struggle to increase the intrinsic value of data within the organization. It's my firm argument that the most important attribute of Modern Observability lies in its "programmable" nature, whereby the acquisition of telemetry becomes woven into the fabric of developing software and the services offered by anyone competing in this global software-driven economy.

There are many other dimensions of contrast, but I personally believe this to be the most important with respect to organizations embracing digital transformation, or for those that just want to improve maturity, growth, and innovation, or anyone wishing to own their own destiny when it comes to data intelligence.

In the next installment (Part 2) of this series, we dive into the history of APM and how it became a 6 Billion USD market and explore some of the challenges that come with APM.

Colin Fallwell is Field CTO of Sumo Logic

Hot Topics

The Latest

Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

APMdigest's Predictions Series continues with 2026 Data Center Predictions — industry experts offer predictions on how data centers will evolve and impact business in 2026 ...

APMdigest's Predictions Series continues with 2026 DataOps Predictions — industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2026. Part 2 covers data and data platforms ...