Skip to main content

Checkly Introduces Activity Log and Code Exporter

Checkly announced the launch of two features that will significantly enhance the user experience and streamline the monitoring process: the Checkly Activity Log and Checkly Code Exporter.

These features enable and empower users to adopt MaC practices more efficiently and effectively into their software development lifecycle, reinforcing Checkly's leadership in the MaC movement.

The Activity Log provides users with a comprehensive history of their Checkly resources. It offers detailed insights into the origin of a resource, including who created or updated it, when it was modified, and the method used for modification, be it the Web UI, Checkly CLI, Terraform, Pulumi, or the Checkly API. This feature enhances transparency and accountability, providing users with a clear understanding of their resource history.

Code Exporter is a feature that allows users to export their resources into Terraform HCL code or TypeScript code compatible with the Checkly CLI, providing a kickstart to their MaC practices. This feature is designed to simplify the adoption of the MaC workflow. It allows users to get started with the Checkly UI first, export their resources and scale their monitoring setup as code from their repository afterward.

Together, these features offer users an end-to-end workflow, making it easier than ever to adopt and benefit from MaC practices.

Checkly's approach to MaC, including the introduction of these new features, is founded on the company’s three key pillars of MaC: Code, Test, and Deploy. This approach allows teams to code their monitoring on their local machine, test their preview deployments in CI, and monitor in production. It's a code-first workflow that integrates seamlessly with modern software development practices and DevOps and SRE toolchains, enabling teams to collaborate, share, optimize, and increase the scalability of synthetic monitoring setups.

The Code Exporter and Activity Log are available for a wide range of resources, including API checks, browser checks, groups, alert channels, maintenance windows, dashboards, private locations, and environment variables. They are free to use on all plans, with data retention limits varying based on the plan type.

"Checkly is at the forefront of advancing Monitoring as Code, equipping users with innovative solutions that not only accelerate their adoption of MaC but also enhance their efficiency and productivity throughout their software development," said Tim Nolet, Checkly CTO and co-founder. "Our latest features show our unwavering commitment to drive the MaC movement, deliver an exceptional user experience, and provide tangible value to our users by simplifying their monitoring processes."

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Checkly Introduces Activity Log and Code Exporter

Checkly announced the launch of two features that will significantly enhance the user experience and streamline the monitoring process: the Checkly Activity Log and Checkly Code Exporter.

These features enable and empower users to adopt MaC practices more efficiently and effectively into their software development lifecycle, reinforcing Checkly's leadership in the MaC movement.

The Activity Log provides users with a comprehensive history of their Checkly resources. It offers detailed insights into the origin of a resource, including who created or updated it, when it was modified, and the method used for modification, be it the Web UI, Checkly CLI, Terraform, Pulumi, or the Checkly API. This feature enhances transparency and accountability, providing users with a clear understanding of their resource history.

Code Exporter is a feature that allows users to export their resources into Terraform HCL code or TypeScript code compatible with the Checkly CLI, providing a kickstart to their MaC practices. This feature is designed to simplify the adoption of the MaC workflow. It allows users to get started with the Checkly UI first, export their resources and scale their monitoring setup as code from their repository afterward.

Together, these features offer users an end-to-end workflow, making it easier than ever to adopt and benefit from MaC practices.

Checkly's approach to MaC, including the introduction of these new features, is founded on the company’s three key pillars of MaC: Code, Test, and Deploy. This approach allows teams to code their monitoring on their local machine, test their preview deployments in CI, and monitor in production. It's a code-first workflow that integrates seamlessly with modern software development practices and DevOps and SRE toolchains, enabling teams to collaborate, share, optimize, and increase the scalability of synthetic monitoring setups.

The Code Exporter and Activity Log are available for a wide range of resources, including API checks, browser checks, groups, alert channels, maintenance windows, dashboards, private locations, and environment variables. They are free to use on all plans, with data retention limits varying based on the plan type.

"Checkly is at the forefront of advancing Monitoring as Code, equipping users with innovative solutions that not only accelerate their adoption of MaC but also enhance their efficiency and productivity throughout their software development," said Tim Nolet, Checkly CTO and co-founder. "Our latest features show our unwavering commitment to drive the MaC movement, deliver an exceptional user experience, and provide tangible value to our users by simplifying their monitoring processes."

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...