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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

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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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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

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.