Skip to main content

Monitoring as Code: Worth The Hype?

Hannes Lenke
Checkly

Configuring application Monitoring as Code (MaC) is the next logical step in modern software development. Today, configuring monitoring is often an overly manual process. It's a bottleneck that DevOps teams are addressing to ship code faster with greater confidence.

Before we explore the relatively new MaC concept, we should step back and discuss the "as Code" movement in general. The most prominent current example is Infrastructure as Code (IaC), which became the gold standard for infrastructure provisioning in recent years. IaC lets developers write files that define how servers should be set up. Building on that concept, IaC tools apply those configurations automatically, often fully integrated into the CI/CD process.

Bringing key aspects of the software development workflow closer to the application code enables developers to automate and ultimately ship their services faster and more often, continuously. Hence ‘as code' has become popular in recent years. However, continuous delivery (CD) requires more than infrastructure automation. It also requires automation of other software delivery aspects. Without this additional automation, how would DevOps teams be able to ship code updates dozens of times a day or even more often?

Next to automation, one key aspect of CD is that cross-functional DevOps teams are now responsible for their services from one end to the other. The motto "You build it; you test it; you run it!" rings true for teams not only tasked to ship often but to simultaneously test and operate those deployed services. It's vital for modern DevOps teams to embrace automation for other functions in their pipeline, including crucial aspects like monitoring. In that context, health and performance monitoring need to be described as code too.

Let's look at some key reasons why monitoring as code is here to stay.

Monitoring shouldn't become the bottleneck for software delivery

Creating checks for larger APIs or websites are often repetitive manual tasks that require a lot of time. In addition, the demand on DevOps teams to make daily — or even hourly — changes to target applications translates into exploding workloads and testing requirements.
In contrast, defining something as code enables you to replicate the actions you would usually do manually — using a UI or CLI — and automate these.

Lack of transparency makes cross-team collaboration harder

Traditional monitoring processes require manual provisioning, meaning users need to create tickets to have new monitoring resources provisioned for them or request permission to apply the changes themselves. In turn, central IT teams are often required to work through different UIs and flows.

This makes it difficult to maintain consistency across an entire infrastructure while simultaneously avoiding duplication of effort across teams. It also complicated the task of auditing changes, making it difficult to review wrongly configured monitoring checks, thereby lengthening an important feedback loop.

Monitoring should be CI/CD integrated

Eventually, the speed of checks-provisioning does not match the pace at which the target applications are evolving. This results from a mismatch of approaches: the CI/CD workflow through which the websites and APIs are iterated upon on one side vs. the fully manual approach on the other.

Applying lessons learned from IaC, MaC brings check definitions closer to the application's source code by having them written as code.

This method allows check definitions to live in source control, boosting cross-team visibility. Additionally, code is text, which is useful for version control and generating an audit trail of all changes. This makes it easier to roll back changes in case of incidents.

With software taking over the provisioning of monitoring checks, hundreds or thousands of checks can be created or edited in a matter of seconds. This is a game-changer for development, operations, and DevOps teams, allowing them to reallocate time spent on manual configuration toward improving the coverage and robustness of their monitoring setup.

To summarize, MaC is revolutionizing the way monitoring is configured by providing:

1. Better scalability through faster, more efficient provisioning

2. Increased transparency and easier rollbacks via source control

3. Unification of previously fragmented processes in a CI/CD workflow

Hannes Lenke is CEO and Co-Founder of Checkly

Hot Topics

The Latest

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Monitoring as Code: Worth The Hype?

Hannes Lenke
Checkly

Configuring application Monitoring as Code (MaC) is the next logical step in modern software development. Today, configuring monitoring is often an overly manual process. It's a bottleneck that DevOps teams are addressing to ship code faster with greater confidence.

Before we explore the relatively new MaC concept, we should step back and discuss the "as Code" movement in general. The most prominent current example is Infrastructure as Code (IaC), which became the gold standard for infrastructure provisioning in recent years. IaC lets developers write files that define how servers should be set up. Building on that concept, IaC tools apply those configurations automatically, often fully integrated into the CI/CD process.

Bringing key aspects of the software development workflow closer to the application code enables developers to automate and ultimately ship their services faster and more often, continuously. Hence ‘as code' has become popular in recent years. However, continuous delivery (CD) requires more than infrastructure automation. It also requires automation of other software delivery aspects. Without this additional automation, how would DevOps teams be able to ship code updates dozens of times a day or even more often?

Next to automation, one key aspect of CD is that cross-functional DevOps teams are now responsible for their services from one end to the other. The motto "You build it; you test it; you run it!" rings true for teams not only tasked to ship often but to simultaneously test and operate those deployed services. It's vital for modern DevOps teams to embrace automation for other functions in their pipeline, including crucial aspects like monitoring. In that context, health and performance monitoring need to be described as code too.

Let's look at some key reasons why monitoring as code is here to stay.

Monitoring shouldn't become the bottleneck for software delivery

Creating checks for larger APIs or websites are often repetitive manual tasks that require a lot of time. In addition, the demand on DevOps teams to make daily — or even hourly — changes to target applications translates into exploding workloads and testing requirements.
In contrast, defining something as code enables you to replicate the actions you would usually do manually — using a UI or CLI — and automate these.

Lack of transparency makes cross-team collaboration harder

Traditional monitoring processes require manual provisioning, meaning users need to create tickets to have new monitoring resources provisioned for them or request permission to apply the changes themselves. In turn, central IT teams are often required to work through different UIs and flows.

This makes it difficult to maintain consistency across an entire infrastructure while simultaneously avoiding duplication of effort across teams. It also complicated the task of auditing changes, making it difficult to review wrongly configured monitoring checks, thereby lengthening an important feedback loop.

Monitoring should be CI/CD integrated

Eventually, the speed of checks-provisioning does not match the pace at which the target applications are evolving. This results from a mismatch of approaches: the CI/CD workflow through which the websites and APIs are iterated upon on one side vs. the fully manual approach on the other.

Applying lessons learned from IaC, MaC brings check definitions closer to the application's source code by having them written as code.

This method allows check definitions to live in source control, boosting cross-team visibility. Additionally, code is text, which is useful for version control and generating an audit trail of all changes. This makes it easier to roll back changes in case of incidents.

With software taking over the provisioning of monitoring checks, hundreds or thousands of checks can be created or edited in a matter of seconds. This is a game-changer for development, operations, and DevOps teams, allowing them to reallocate time spent on manual configuration toward improving the coverage and robustness of their monitoring setup.

To summarize, MaC is revolutionizing the way monitoring is configured by providing:

1. Better scalability through faster, more efficient provisioning

2. Increased transparency and easier rollbacks via source control

3. Unification of previously fragmented processes in a CI/CD workflow

Hannes Lenke is CEO and Co-Founder of Checkly

Hot Topics

The Latest

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...