There’s something wrong with the pricing of observability services. Not just because it costs a lot – it certainly does – but also because it’s almost impossible to discern, in many cases, exactly how the costs are calculated. The service itself, the number of users, the number of sources, the analytics, the retention period, and extended data retention, and the engineers on staff who maintain the whole system are all relevant factors that feed into the final expense.

The moment of launching something new at a game studio (titles, experiences, features, subscriptions) is a blockbuster moment that hangs in the balance. The architecture—distributed and complex, designed by a multitude of teams, to be played across a variety of devices in every corner of the world—is about to meet a frenzy of audience anticipation, along with the sky-high expectations of players, executives, and investors.

After years of helping developers monitor and debug their production systems, we couldn’t help but notice a pattern across many of them: they roughly know that metrics and traces should help them get the answers they need, but they are unfamiliar with how metrics and traces work, and how they fit into the bigger observability world. This post is an introduction to how we see observability in practice, and a loose roadmap for exploring observability concepts in the posts to come.

Credit: Unsplash What is monitoring? What is observability? Monitoring shows you how a Kubernetes environment and all of its layers are operating. Observability, on the other hand, is a measure of how well internal states of a system can be inferred from knowledge of its external outputs.

Observability vs monitoring, what is the difference? Monitoring is the what to observability’s why. Here we dig into the differences.

Two popular deployment architectures exist in software: the out-of-favor monolithic architecture and the newly popular microservices architecture. Monolithic architectures were quite popular in the past, with almost all companies adopting them. As time went on, the drawbacks of these systems drove companies to rework entire systems to use microservices instead.

Monitoring cloud-native systems is hard. You’ve got highly distributed apps spanning tens and hundreds of nodes, services and instances. You’ve got additional layers and dimensions—not just bare metal and OS, but also node, pod, namespace, deployment version, Kubernetes’ control plane and more. To make things more interesting, any typical system these days uses many third-party frameworks, whether open source or cloud services.