Skip to main content

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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...