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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.

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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 ...

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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

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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.