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

Colin Fallwell
Sumo Logic
Learn more about 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
Learn more about 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

Related Links

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MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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Signs It May Be Time to Reassess Your IT Infrastructure Strategy

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Enterprise Edge AI Reaches Inflection Point

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

AI Is Hitting Operational Limits

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

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I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

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