Managing resources-as-code brings with it many advantages, including templating and reusing code which is the backbone to ensuring operational consistency, and for reducing human error. With a staunch belief in Code to Cloud, the benefits of codifying resources are many, and this extends to monitoring systems as well (we’ve written about monitoring-as-code in the past), by transitioning your monitoring to as-code, you can supercharge your monitoring and observability for everything it consists of, from logging, to tracing, alerting and dashboarding alike.  

In this post, we’ll take a deeper look at all the reasons you should consider transitioning all of your SaaS platforms to as-code, below we’ll dive into Datadog-as-Code with tools like Firefly and Terraform. We’ll demonstrate the unique benefits of running Datadog-as-Code and the easiest path to making this happen practically.

Templating and Code Reuse Code in Monitoring

Monitoring systems are crucial as they oversee the health and performance of all engineering, R&D and production environments. Templating and reusing code significantly reduces the likelihood of errors, compared to manual configuration. 

Human error is one of the most common sources of issues when it comes to misconfigurations and other anomalies. This is compounded with tasks that require deep operational expertise and experience, which is a prerequisite when it comes to configuring monitoring systems to provide business critical metrics in real time.  

This includes common operational tasks like setting up alerts and rules through a UI, which can lead to mistakes and inconsistent configurations. By transitioning to Monitoring-as-Code, and specifically Datadog-as-Code, it’s possible to mitigate these risks by automating the process, ensuring that all configurations are applied uniformly and accurately.

Monitoring Infrastructure Maintenance as Code

Much of our ongoing monitoring system maintenance is eventually Infrastructure maintenance.  When we think about our Datadog ops and everything we expect from any monitoring platform it includes synthetic probing, dashboards, anomaly detection, on-call (read: Pagerduty ops), logs, alerts, where eventually all of this can be managed through Infrastructure-as-Code for greater efficiencies. 

A Datadog-as-Code operation makes it possible to automate common and repetitive tasks like installing agents on Kubernetes clusters and sending logs to SaaS platforms, configuring, maintaining and upgrading all of the many elements that good monitoring consists of––all can see immense value when migrating to as-code practices, by managing these operations using Terraform. 

One example is the deployment or upgrade of monitoring agents, that is a frequent monitoring maintenance operation. Terraform allows the deployment of these agents across the compute infrastructure, eliminating the need for manual configuration through a UI, which is not scalable, and often prone to mistakes. By embedding these agents into the application stack or infrastructure stack, organizations can ensure comprehensive log and metric collection with minimal errors.  This is true for any of the other elements from the probes to the logs, dashboards and alerts––this operational consistency improves efficiency.

Avoid Downtime through Automated Deployments and Updates

When engineering organizations start scaling up, managing your monitoring consistently becomes an even greater pain and challenge leading to many difficulties, including inconsistent installations, configurations, dashboards (across engineering teams and even environments) that can lead to production breakage, missed alerts, and lost dashboards over time, through unclear ownership between dev and infrastructure teams and much more.

Managing your Datadog-as-Code (or any monitoring as-code, honestly), allows teams to automate deployments and updates in a consistent and repetitive manner.  This can be leveraged to facilitate deploying a new region or site, by integrating Datadog as a submodule within the Terraform model ensures that monitoring agents are automatically configured and installed in a canonical manner whenever required. 

This approach guarantees that no configurations or installations are missed and streamlines the upgrading and updating of agents, along with all of their dependencies. What’s more this also adds compliance guarantees and coverage, ensuring that monitoring remains consistent across all environments.

Datadog-as-Code Reduces Misconfigured Alerts 

Automation in monitoring significantly reduces the occurrence of misconfigured alerts and false positives. By running monitoring rules and alerts as part of an automated IaC process, organizations can achieve more reliable and accurate monitoring. This is critical in maintaining a robust monitoring system that consistently provides the most realistic and actionable results.

Datadog is a leading monitoring provider supported by Firefly, offering comprehensive monitoring capabilities comparable to other platforms like Grafana Cloud. Key features in the monitoring domain include synthetic probing, dashboards, anomaly detection, on-call management (similar to PagerDuty), logs, and alerts. These functionalities are integral to any monitoring system and can be managed effectively using IaC practices.


In large engineering organizations, multiple teams often rely on shared monitoring dashboards to obtain a comprehensive view of system performance. However, this approach can introduce complexities related to ownership and access controls. 

Different teams may have varying priorities and require distinct perspectives on the same data, making it difficult to design shared dashboards that satisfy all requirements. Furthermore, managing permissions to ensure that only authorized individuals have access to sensitive information can be a complicated task.

All of these can be much more easily defined and tracked in a granular way when running a Datadog-as-Code operation - from the definition of ownership and change management policies, as well as access control and dashboarding templates, KPIs and metrics.  When managing Datadog-as-Code, you gain all of the same version control benefits that your entire as-code stack.

Datadog and Monitoring-as-Code for Business Continuity

Adopting Infrastructure-as-Code practices in monitoring offers significant benefits, including reducing human error, ensuring consistent configurations, and automating deployments and updates. Tools like Terraform and Firefly, integrated with Datadog, enable seamless and efficient infrastructure maintenance. 

By leveraging these practices, SREs and infrastructure managers can achieve greater stability and precision in their monitoring systems, ensuring the reliability and performance of their production environments. Eventually monitoring systems are there to ensure business operations continue uninterrupted even when systems are overloaded, and optimizing our monitoring infrastructure maintenance is no less critical than our entire operational infrastructure.  Automation and version control by transitioning to Datadog-as-Code delivers the efficiencies and reliability cloud native companies today require.