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What You Should Be Monitoring to Ensure Digital Performance - Part 3

APMdigest asked experts from across the IT industry for their opinions on what IT departments should be monitoring to ensure digital performance. Part 3 covers the development side.

Start with What You Should Be Monitoring to Ensure Digital Performance - Part 1

Start with What You Should Be Monitoring to Ensure Digital Performance - Part 2

CODE ERRORS

Code-level issues are a common cause of application slowness and have fueled the need for distributed transaction tracing, which can help isolate the exact line of code with errors. This type of monitoring can also be effectively applied in both pre- and post-production environments, enabling us to prevent performance issues before they impact end users as well as help isolate them when they do occur.
When this type of application monitoring is done in context of infrastructure dependencies, it helps distinguish if there are other issues affecting application code processing, such as a bottleneck in the application server, long-running database queries, slow third-party calls, or other issues that may be associated with the application ecosystem. Applications are the heart of IT workloads, and application performance monitoring is critical to effectively ensure the performance of digital services.
John Worthington
Director, Product Marketing, eG Innovations

Digital performance is complex and can be measured in many ways, but one critical consideration is how well does the application do what it is supposed to do? Is it meeting a functional performance metric for customer expectations? To ensure this, organizations need to look at the "fingerprint" of each error in code to discern its importance as well as look at the number of critical errors per release. This dictates the overall functional reliability of the code. It also requires you to be code-aware, monitoring from inside the application at runtime, not surrounding it or listening to the exhaust.
Tal Weiss
CTO and Co-Founder, OverOps

Most people already know to monitor the obvious things, like total latency to response. But my favorite monitor comes from Anatoly Mikhaylov's talk at DASH this year. He spoke about finding massive infrastructure costs hidden in error codes. Adding APM monitoring to the errors in your endpoints can show costs you wouldn't otherwise see.
Kirk Kaiser
APM Developer Advocate, Datadog

APPLICATION RELEASE

When automating you application release, it's important to remember what you need to monitor. This will allow you to go as fast as possible, but also make sure you are doing it efficiently. Monitor your lead time, success vs failure rate and mean time to recovery will ensure you focus on value rather than on effort.
Yaniv Yehuda
Co-Founder and CTO, DBmaestro

API

One key area to make sure you monitor: API calls. There aren't many applications I come across these days that do not include some 3rd-party API, be it for authentication, analytics, storage, or customer relationship management. Such API calls can so greatly impact digital performance that not monitoring them to identify things such as performance slowdowns and dependencies is a prescription for pain.
Jean Tunis
Senior Consultant and Founder of RootPerformance

Cloud, containers and microservices are creating increasingly ephemeral, modular and volatile IT environments. In these dynamic environments, traditional monitoring approaches fail. A modern monitoring approach is required to provide complete visibility into the applications, containers, host and underlying supporting infrastructure. This includes having visibility into the performance of and data returning from APIs which have become a key component to any microservices architecture. A modern monitoring approach includes the analytics and intelligence to understand how changes might impact the overall user experience and flexible monitoring techniques that don't overload the containerized application environment.
Amy Feldman
Director, Product Marketing, CA Technologies

Finding a tool that fits seamlessly into your workflows, setting performance benchmarks, validating payloads, and getting visibility into the performance of API transactions is critical to help teams get rapidly identify and fix issues in production so that the delivered digital experience matches the vision for end-users.
Anand Sundaram
VP of Product, AlertSite UXM, SmartBear

APIs are the fundamental building block of modern software. While engineering teams have built extensive monitoring systems to check the health of code execution paths, they have little visibility into what's going on with APIs. An API failure can bring down systems and without proper monitoring in place, it can be very hard to debug what's going on.
Abhinav Asthana
CEO, Postman

CONTAINERS

The nature of development means systems are going to spring into existence and then back out again often, and that this rapid change is OK, which means your monitoring needs to be OK with it. The ability to monitor containers, ephemeral services, and the like, is a must.
Leon Adato
Head Geek, SolarWinds

MICROSERVICES

Real users who recently reviewed APM solutions in the IT Central Station community recommend monitoring microservices. Click here to learn more.
Russell Rothstein
Founder and CEO, IT Central Station

Let's go to the extreme and say you could only monitor one thing — that one thing would be microservice response time. In this brave new world, it's actually quite difficult to understand how well your revenue-critical application is performing. While traditional metrics still matter (CPU, memory, disk, etc), your response time on a microservice-by-microservice basis is the thing that matters the most. This single metric will tell you more about the customer experience than anything else. It will indicate downtime or more subtle performance problems in your application. While this metric alone will not tell you "why" something is going on, it will tell you "what" is happening and allow you to quickly isolate a problem to a handful of services or some set of underlying infrastructure.
Apurva Davé
CMO, Sysdig

IO PATH

As you evolve and enhance your company's hybrid data center infrastructure to keep pace with your industry, understanding your unique workload I/O DNA is paramount to success. Real-time monitoring of the I/O path – from the virtual server to the storage array – is essential to ensuring digital performance. For mission-critical applications, understanding the performance of each and every transaction is the cornerstone of customer satisfaction and revenue assurance.
Len Rosenthal
CMO, Virtual Instruments

Read Len Rosenthal's new blog on APMdigest: Infrastructure Monitoring for Digital Performance Assurance.

Read What You Should Be Monitoring to Ensure Digital Performance - Part 4, covering the infrastructure, including the cloud and the network.

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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

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 ...

What You Should Be Monitoring to Ensure Digital Performance - Part 3

APMdigest asked experts from across the IT industry for their opinions on what IT departments should be monitoring to ensure digital performance. Part 3 covers the development side.

Start with What You Should Be Monitoring to Ensure Digital Performance - Part 1

Start with What You Should Be Monitoring to Ensure Digital Performance - Part 2

CODE ERRORS

Code-level issues are a common cause of application slowness and have fueled the need for distributed transaction tracing, which can help isolate the exact line of code with errors. This type of monitoring can also be effectively applied in both pre- and post-production environments, enabling us to prevent performance issues before they impact end users as well as help isolate them when they do occur.
When this type of application monitoring is done in context of infrastructure dependencies, it helps distinguish if there are other issues affecting application code processing, such as a bottleneck in the application server, long-running database queries, slow third-party calls, or other issues that may be associated with the application ecosystem. Applications are the heart of IT workloads, and application performance monitoring is critical to effectively ensure the performance of digital services.
John Worthington
Director, Product Marketing, eG Innovations

Digital performance is complex and can be measured in many ways, but one critical consideration is how well does the application do what it is supposed to do? Is it meeting a functional performance metric for customer expectations? To ensure this, organizations need to look at the "fingerprint" of each error in code to discern its importance as well as look at the number of critical errors per release. This dictates the overall functional reliability of the code. It also requires you to be code-aware, monitoring from inside the application at runtime, not surrounding it or listening to the exhaust.
Tal Weiss
CTO and Co-Founder, OverOps

Most people already know to monitor the obvious things, like total latency to response. But my favorite monitor comes from Anatoly Mikhaylov's talk at DASH this year. He spoke about finding massive infrastructure costs hidden in error codes. Adding APM monitoring to the errors in your endpoints can show costs you wouldn't otherwise see.
Kirk Kaiser
APM Developer Advocate, Datadog

APPLICATION RELEASE

When automating you application release, it's important to remember what you need to monitor. This will allow you to go as fast as possible, but also make sure you are doing it efficiently. Monitor your lead time, success vs failure rate and mean time to recovery will ensure you focus on value rather than on effort.
Yaniv Yehuda
Co-Founder and CTO, DBmaestro

API

One key area to make sure you monitor: API calls. There aren't many applications I come across these days that do not include some 3rd-party API, be it for authentication, analytics, storage, or customer relationship management. Such API calls can so greatly impact digital performance that not monitoring them to identify things such as performance slowdowns and dependencies is a prescription for pain.
Jean Tunis
Senior Consultant and Founder of RootPerformance

Cloud, containers and microservices are creating increasingly ephemeral, modular and volatile IT environments. In these dynamic environments, traditional monitoring approaches fail. A modern monitoring approach is required to provide complete visibility into the applications, containers, host and underlying supporting infrastructure. This includes having visibility into the performance of and data returning from APIs which have become a key component to any microservices architecture. A modern monitoring approach includes the analytics and intelligence to understand how changes might impact the overall user experience and flexible monitoring techniques that don't overload the containerized application environment.
Amy Feldman
Director, Product Marketing, CA Technologies

Finding a tool that fits seamlessly into your workflows, setting performance benchmarks, validating payloads, and getting visibility into the performance of API transactions is critical to help teams get rapidly identify and fix issues in production so that the delivered digital experience matches the vision for end-users.
Anand Sundaram
VP of Product, AlertSite UXM, SmartBear

APIs are the fundamental building block of modern software. While engineering teams have built extensive monitoring systems to check the health of code execution paths, they have little visibility into what's going on with APIs. An API failure can bring down systems and without proper monitoring in place, it can be very hard to debug what's going on.
Abhinav Asthana
CEO, Postman

CONTAINERS

The nature of development means systems are going to spring into existence and then back out again often, and that this rapid change is OK, which means your monitoring needs to be OK with it. The ability to monitor containers, ephemeral services, and the like, is a must.
Leon Adato
Head Geek, SolarWinds

MICROSERVICES

Real users who recently reviewed APM solutions in the IT Central Station community recommend monitoring microservices. Click here to learn more.
Russell Rothstein
Founder and CEO, IT Central Station

Let's go to the extreme and say you could only monitor one thing — that one thing would be microservice response time. In this brave new world, it's actually quite difficult to understand how well your revenue-critical application is performing. While traditional metrics still matter (CPU, memory, disk, etc), your response time on a microservice-by-microservice basis is the thing that matters the most. This single metric will tell you more about the customer experience than anything else. It will indicate downtime or more subtle performance problems in your application. While this metric alone will not tell you "why" something is going on, it will tell you "what" is happening and allow you to quickly isolate a problem to a handful of services or some set of underlying infrastructure.
Apurva Davé
CMO, Sysdig

IO PATH

As you evolve and enhance your company's hybrid data center infrastructure to keep pace with your industry, understanding your unique workload I/O DNA is paramount to success. Real-time monitoring of the I/O path – from the virtual server to the storage array – is essential to ensuring digital performance. For mission-critical applications, understanding the performance of each and every transaction is the cornerstone of customer satisfaction and revenue assurance.
Len Rosenthal
CMO, Virtual Instruments

Read Len Rosenthal's new blog on APMdigest: Infrastructure Monitoring for Digital Performance Assurance.

Read What You Should Be Monitoring to Ensure Digital Performance - Part 4, covering the infrastructure, including the cloud and the network.

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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

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 ...