Skip to main content

4 Best Practices for APM in the Cloud

Moving your applications to the cloud has undeniable benefits. The cloud offers dynamic environments where you can spin up instances quickly, only consume what you need, and eliminate the costs of renting or purchasing expensive servers.

But the cloud also means developers are building software on a platform they don't "own", where things can change in an instant. There is no real-time insight into the performance and health of the infrastructure, to say nothing of the applications that run on it.

When things go wrong, it's hard to know where the bottlenecks are. Is the application consuming too much CPU? Is an unresponsive API causing it to time out? Is network latency degrading application performance? How do you know what you don't know?

These issues eat into your bottom line and negatively impact customer satisfaction. And while you can't anticipate every potential problem, you can be prepared to avoid many of them.

Here are four best practices to optimize your application performance monitoring:

1. Collaboration

Invite other team members to participate in your APM tool. You can set role-based access controls that grant edit privileges to admins, or restrict read-only users to see only systems with a certain tag. This gives users access to the features and data they need to do their jobs, without distracting them with superfluous information.

Some products offer deep URLs to facilitate information sharing across teams, and also allow you to annotate important events that correlate to subsequent performance changes.

2. Tagging

Alerts defined by tags allow you to customize how, when and why you're being notified about the performance of your applications. For instance, if you have a set of front-end application servers behind a load balancer, you may want to tag them "frontend" and create a unique alert for each set of performance metrics that you want to monitor.

When used correctly, include and exclude tags, process alerting and threshold alerts can help users react quickly and efficiently to solve performance issues.

3. Automation

The self-service, automated provisioning of IT resources has come of age and is rapidly becoming ubiquitous. Chef and Puppet are two popular orchestration engines that allow you to spin servers up and down in response to evolving business needs. Your APM solution should integrate tightly with existing automation tools, modifying automatically in response to operating environment changes, and providing real-time visibility into all your Chef- and Puppet-deployed applications and services.

4. Custom metrics

No two businesses have the same goals and objectives. The same can be said for application performance monitoring metrics. Custom metrics provide enhanced visibility into specific areas of an application where you want to collect, view or analyze additional information – such as page load time, web transaction response time or database query execution time. Setting these up should be as easy as modifying a simple script and creating a custom dashboard to display your data.

ABOUT Josh Stephens

As Vice President of Product Strategy at Idera, Josh Stephens brings nearly 20 years of experience in the technology industry. Prior to Idera, he founded a consulting and technology company focused on helping companies adapt their product and go to market strategies to take advantage of the high velocity, inside sales model focused around inbound marketing and social media. Previously, he was VP of technology at SolarWinds where he spent more than a dozen years helping to define and innovate their product and go to market strategies. Earlier in his career, Stephens spent time at Greenwich Technology Partners, International Network Services, and the United States Air Force.

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 ...

4 Best Practices for APM in the Cloud

Moving your applications to the cloud has undeniable benefits. The cloud offers dynamic environments where you can spin up instances quickly, only consume what you need, and eliminate the costs of renting or purchasing expensive servers.

But the cloud also means developers are building software on a platform they don't "own", where things can change in an instant. There is no real-time insight into the performance and health of the infrastructure, to say nothing of the applications that run on it.

When things go wrong, it's hard to know where the bottlenecks are. Is the application consuming too much CPU? Is an unresponsive API causing it to time out? Is network latency degrading application performance? How do you know what you don't know?

These issues eat into your bottom line and negatively impact customer satisfaction. And while you can't anticipate every potential problem, you can be prepared to avoid many of them.

Here are four best practices to optimize your application performance monitoring:

1. Collaboration

Invite other team members to participate in your APM tool. You can set role-based access controls that grant edit privileges to admins, or restrict read-only users to see only systems with a certain tag. This gives users access to the features and data they need to do their jobs, without distracting them with superfluous information.

Some products offer deep URLs to facilitate information sharing across teams, and also allow you to annotate important events that correlate to subsequent performance changes.

2. Tagging

Alerts defined by tags allow you to customize how, when and why you're being notified about the performance of your applications. For instance, if you have a set of front-end application servers behind a load balancer, you may want to tag them "frontend" and create a unique alert for each set of performance metrics that you want to monitor.

When used correctly, include and exclude tags, process alerting and threshold alerts can help users react quickly and efficiently to solve performance issues.

3. Automation

The self-service, automated provisioning of IT resources has come of age and is rapidly becoming ubiquitous. Chef and Puppet are two popular orchestration engines that allow you to spin servers up and down in response to evolving business needs. Your APM solution should integrate tightly with existing automation tools, modifying automatically in response to operating environment changes, and providing real-time visibility into all your Chef- and Puppet-deployed applications and services.

4. Custom metrics

No two businesses have the same goals and objectives. The same can be said for application performance monitoring metrics. Custom metrics provide enhanced visibility into specific areas of an application where you want to collect, view or analyze additional information – such as page load time, web transaction response time or database query execution time. Setting these up should be as easy as modifying a simple script and creating a custom dashboard to display your data.

ABOUT Josh Stephens

As Vice President of Product Strategy at Idera, Josh Stephens brings nearly 20 years of experience in the technology industry. Prior to Idera, he founded a consulting and technology company focused on helping companies adapt their product and go to market strategies to take advantage of the high velocity, inside sales model focused around inbound marketing and social media. Previously, he was VP of technology at SolarWinds where he spent more than a dozen years helping to define and innovate their product and go to market strategies. Earlier in his career, Stephens spent time at Greenwich Technology Partners, International Network Services, and the United States Air Force.

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 ...