Operations | Monitoring | ITSM | DevOps | Cloud

There is often a temptation to stretch on-call shifts to a month or longer, especially when incident volume is low. The logic seems sound. If the phone rarely rings, it feels unnecessary to hand off on-call duties every week. But looking strictly at incident volume often misses the human side of the equation. Being on-call isn’t just about answering pages. It is also a state of mind. Even when it is quiet, simply being on-call could create fatigue of its own.

Choosing an on-call rotation is about finding a rhythm that balances your team’s well-being and your system’s reliability. The right on-call rotation helps prevent burnout and makes on-call duties sustainable over the long run. This guide walks you through different on-call rotation patterns, from daily rotation to after-hours rotations. We’ll look at why you might choose a particular rotation and the challenges that often come with it.

Organizations are increasingly using agentic AI applications powered by large language models (LLMs) to automate analysis, decision-making, and operational workflows. As these AI agents take on more responsibility, they gain access to internal tools and services and can interact with them in unintended ways.

When to use JavaScript or CSS in frontend projects is a matter of continued debate among many frontend developers. JavaScript is often the default choice for frontend development, as it offers a robust collection of libraries custom-made for creating advanced UI features, such as data-based visualizations or complex animations. But JavaScript also comes with tradeoffs, particularly when it comes to performance, accessibility, and code complexity.

Event-driven systems are great at decoupling services, but they also make incidents harder to untangle. A single user request can turn into dozens (or thousands) of messages, multiple consumers, retries, and delayed acknowledgments. If your tracing only tells you that a message was sent or received, you still have to guess which upstream request produced the message, whether a batch publish fanned out cleanly, and where queue time is accumulating.

While there are many benefits to traditional cloud storage solutions, sometimes enterprises need a more scalable way to manage and access large amounts of unstructured data. So while cloud storage may be the perfect solution for small businesses, larger teams or enterprises should consider object storage to meet their storage needs without worrying about high costs, data loss, or compliance issues.

Kubernetes has become the default foundation for a lot of modern application infrastructure. It’s powerful, flexible, and widely supported, which makes it an obvious starting point for many teams building a cloud-native application platform (a standardized way for teams to deploy, run, secure, and operate applications in production). But there’s a distinction that often gets lost early in the decision process: Kubernetes is a framework. It is not a platform.

We’re delighted to announce a strategic partnership between Kosli and TeamTopologies - a collaboration that brings together SDLC Governance automation with the world’s leading framework for organizing business and technology for fast flow of value.

Exponential smoothing is a time series forecasting method that uses an exponentially weighted average of past observations to predict future values. In other words, it assigns greater weight to recent observations than to older ones, allowing the forecast to adapt to changing data trends. In this post, we’ll look at the basics of exponential smoothing, including how it works, its types, and how to implement it in Python.

By incorporating observability tools into your stack, you can better understand how your complex infrastructure operates, reduce downtime, and empower developers to identify and fix problems quickly. However, it now takes considerably more work, time, and money to build the best observability tools for your infrastructure and applications. According to a Splunk survey, over half of the firms polled employ eight or more observability tools.