Operations | Monitoring | ITSM | DevOps | Cloud

This guide covers battle-tested chaos experiments for Kubernetes, AWS, Azure, and GCP to help you validate production resilience before real failures happen. Start with low blast radius experiments (pod-level) and gradually progress to higher impact scenarios (node/zone failures), always defining clear hypotheses and using probes to measure results. Building reliable distributed systems isn't just about writing good code. It's about understanding how your systems behave when things go wrong.

Heroku makes it easy to deploy and operate applications without managing servers, but understanding how your application behaves internally still requires instrumentation. Platform metrics like CPU usage, memory consumption, and router request/status counts are useful, but they don’t tell you how long your code takes to run, when your app throws errors, or whether users are interacting with key features.

The open-source Kubernetes platform is designed to help simplify application deployment through Linux containers. It supports tasks like deploying workloads in the form of pods, clustering nodes, managing container runtimes, and tracking resources. The Kubernetes microservices system has risen in popularity over the last several years as an easy way to support, scale, and manage applications.

You're in a planning meeting when someone asks a simple question. How long does it actually take your team to ship a feature? You've got spreadsheets, Git logs, and Jira exports scattered across three tabs, and you still can't give a confident answer. It's a question you should be able to answer instantly, but the data lives in too many places to stitch together on the fly.

AI coding assistants have quickly become part of everyday development. Teams now rely on them to explain unfamiliar code, suggest configuration files, debug errors, and accelerate delivery across the stack. But as these tools move from experimentation into real production workflows, a consistent pattern is emerging: AI breaks down at the platform boundary.

As IT organizations enter 2026, many of the assumptions around monitoring and observability have already been tested. Throughout 2025, infrastructure teams made it clear that visibility alone is not enough. Alerts without context, short data retention, and fragmented tools limited teams’ ability to explain behavior, validate changes, and plan with confidence. This article looks at what emerged from those experiences and how observability expectations continue to shift.

Discover how Qovery bridges the gap between developers and infrastructure with a "security by design" approach. From federated identities and unique encryption keys to real-time audit logs and SOC2 Type 2 certification - see how we protect your data while eliminating vendor lock-in.