Monitoring as Code: Worth The Hype?

Hannes Lenke
Checkly
Learn more about 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

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Monitoring as Code: Worth The Hype?

Hannes Lenke
Checkly
Learn more about 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

Related Links

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When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...