Operations | Monitoring | ITSM | DevOps | Cloud

Transform your organization's observability strategy with open standards and simplified data collection Modern organizations face an unprecedented observability challenge. As systems grow more complex and distributed, traditional monitoring approaches are struggling to keep pace. With data volumes doubling every two years and systems spanning multiple clouds and technologies, organizations need a new approach to maintain visibility into their operations.

I recently talked to a customer of Checkly and he shared some thoughts about Monitoring as Code. Let’s call him Karl in this article. Karl and I talked about why Monitoring as Code (MaC) is becoming essential for teams operating at scale. As the Head of Platform Engineering at a major e-commerce company processing millions of transactions daily, his experience shows how MaC solves a lot of the messy challenges that come with traditional synthetic monitoring setups.

Artificial intelligence is no longer a futuristic concept—it is shaping how businesses operate, how researchers innovate, and how people interact with technology. Models like DeepSeek-R1 , a promising new entrant, alongside established players such as Llama 3 and GPT-4o, are at the forefront of this transformation. These tools are not just about technological advancement; they are about solving real-world problems and driving meaningful progress.

In the financial sector, where system reliability directly impacts customer trust and revenue, even minor IT inefficiencies can spiral into costly crises. For one of the world’s largest banks—supporting 25 million customers, 2,000 branches, and 3,000 ATMs—a hidden challenge threatened its reputation: unpredictable memory consumption in critical applications.

Digital-first customers who are always on the go expect a seamless experience. But let’s face it—100% uptime is a myth. Trying to achieve it can drain resources and stifle innovation. This is where error budgets come in. They help site reliability engineers (SREs) find the sweet spot between delivering reliability and development velocity. With error budgets, teams can focus on building a robust system without burning out over perfection.

Imagine being the new developer in a bustling tech company. Everyone is rushing to meet deadlines, and no one has time to explain the tangled web of services, databases, and messaging systems that make up the organization’s architecture. You search high and low for documentation, but the few diagrams you find are outdated or incomplete. Feeling lost? This is where metrics can come to the rescue.

Recently we’ve explored the evolving role of Kubernetes as a full ecosystem, rather than just a platform, diving into the power and complexity of add-ons. These tools, as highlighted previously, are key to augmenting Kubernetes core capabilities, and adding-on (as their name implies) essential capabilities not supported directly by Kubernetes itself.

Remember using SSH to access servers? It was the go-to method for troubleshooting or making changes to a system. But in the world of containers, SSH doesn't quite fit. Kubernetes and containers work differently; they're dynamic and spun up and down frequently. That’s where kubectl exec comes in. It lets you run commands inside a pod directly, without needing to rely on SSH or worry about the pod being ephemeral. It’s simple and fits the nature of modern, containerized environments.