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When Is It Too Early to Think About Monitoring?

David Hickman

When is it too early to think about monitoring?

In a word, “NEVER.”

I’ve heard people say that they are super busy with production deployments and fast-moving projects. They haven’t had much time to think about a performance monitoring strategy for their new applications.

But waiting until your applications are already in production is a mistake. However, it’s one that you can avoid.

Develop, test and deploy your applications with monitoring already in place and save yourself a lot of headaches. Let me count the ways:

1. Using the same monitoring tools for both pre- and post-production

Obviously, you will be doing performance testing during development and QA. But if you use different tools before and after, you might get inconsistent results. And you’ll have to retrain the people that did the original testing on the new performance monitoring tools. After they’ve moved on to other projects.

Having a monitoring strategy in place ensures that people are using the same tools throughout the lifecycle. .So that you can expect consistent measurements and more efficient troubleshooting and response if necessary.

2. Monitoring – independent from administration – eases access and alleviates concerns

Many developers need access to performance metrics during testing and often that means providing administrative access to middleware platforms to get those metrics.

When you’ve implemented an independent monitoring solution then developers can self-service their performance monitoring needs without worrying about providing administrative access.

3. Monitoring best practices implemented at design-time means less effort at run-time

Correlation of metrics between related technologies is what allows you to see things in context. For instance, show me all the servers that support a particular application. That's significantly easier than wading through 1000’s of servers trying to figure out which ones support my “banking” application. This is especially helpful when time is of the essence. But how does your monitoring system know which instances are related?

Well, you could map it manually, often using naming conventions for your services and engines. The better you stick to well-defined naming conventions, the easier it is. This is another reason why including monitoring requirements in your application building process will pay dividends down the road. Because “automatically” is so much easier than “manually”.

4. Capacity Planning is easier if you’ve been collecting performance data all along

If your business is successful, your apps will get busier and you’ll need to ask yourself if you have enough capacity for future growth? Of course, it’s hard to answer that question if you have no baseline data.

However, capturing performance data from the start enables you to see the resource usage trends over time. Correlate that to expected traffic growth for your application and you'll be on stable ground as you analyze your resource requirements for the next 6-12 months.

5. You don’t have to worry about running out of steam

For a lot of companies, once an application is written, people celebrate and move on. Things that you promise you’ll get to later – never seem to be a priority anymore. Unless, of course, you are hit with a severity one outage and then all heck breaks loose. Then people start panicking because there is no visibility into what is breaking down and where.

Building monitoring requirements into the application development cycle ensure that these things are not forgotten in the hustle and bustle of the next project. In fact, some of our customers will not sign off on a project unless monitoring is ready to go BEFORE moving into production. They make it a priority from the start so that they don’t have to struggle for resources AFTER everybody thinks they are done and move on to something else.

So, the next time somebody asks you about your monitoring strategy for your new apps, instead of saying “I’m too busy to think about it right now,” you should say, “I’m way ahead of you”.

David Hickman is in Product Marketing at SL Corporation.

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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 ...

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

When Is It Too Early to Think About Monitoring?

David Hickman

When is it too early to think about monitoring?

In a word, “NEVER.”

I’ve heard people say that they are super busy with production deployments and fast-moving projects. They haven’t had much time to think about a performance monitoring strategy for their new applications.

But waiting until your applications are already in production is a mistake. However, it’s one that you can avoid.

Develop, test and deploy your applications with monitoring already in place and save yourself a lot of headaches. Let me count the ways:

1. Using the same monitoring tools for both pre- and post-production

Obviously, you will be doing performance testing during development and QA. But if you use different tools before and after, you might get inconsistent results. And you’ll have to retrain the people that did the original testing on the new performance monitoring tools. After they’ve moved on to other projects.

Having a monitoring strategy in place ensures that people are using the same tools throughout the lifecycle. .So that you can expect consistent measurements and more efficient troubleshooting and response if necessary.

2. Monitoring – independent from administration – eases access and alleviates concerns

Many developers need access to performance metrics during testing and often that means providing administrative access to middleware platforms to get those metrics.

When you’ve implemented an independent monitoring solution then developers can self-service their performance monitoring needs without worrying about providing administrative access.

3. Monitoring best practices implemented at design-time means less effort at run-time

Correlation of metrics between related technologies is what allows you to see things in context. For instance, show me all the servers that support a particular application. That's significantly easier than wading through 1000’s of servers trying to figure out which ones support my “banking” application. This is especially helpful when time is of the essence. But how does your monitoring system know which instances are related?

Well, you could map it manually, often using naming conventions for your services and engines. The better you stick to well-defined naming conventions, the easier it is. This is another reason why including monitoring requirements in your application building process will pay dividends down the road. Because “automatically” is so much easier than “manually”.

4. Capacity Planning is easier if you’ve been collecting performance data all along

If your business is successful, your apps will get busier and you’ll need to ask yourself if you have enough capacity for future growth? Of course, it’s hard to answer that question if you have no baseline data.

However, capturing performance data from the start enables you to see the resource usage trends over time. Correlate that to expected traffic growth for your application and you'll be on stable ground as you analyze your resource requirements for the next 6-12 months.

5. You don’t have to worry about running out of steam

For a lot of companies, once an application is written, people celebrate and move on. Things that you promise you’ll get to later – never seem to be a priority anymore. Unless, of course, you are hit with a severity one outage and then all heck breaks loose. Then people start panicking because there is no visibility into what is breaking down and where.

Building monitoring requirements into the application development cycle ensure that these things are not forgotten in the hustle and bustle of the next project. In fact, some of our customers will not sign off on a project unless monitoring is ready to go BEFORE moving into production. They make it a priority from the start so that they don’t have to struggle for resources AFTER everybody thinks they are done and move on to something else.

So, the next time somebody asks you about your monitoring strategy for your new apps, instead of saying “I’m too busy to think about it right now,” you should say, “I’m way ahead of you”.

David Hickman is in Product Marketing at SL Corporation.

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...