Operations | Monitoring | ITSM | DevOps | Cloud

Back in 2024, I officially put a name to a concept at Civo we had been developing for many years. I called it cloud parity. When Civo was incepted, two completely different worlds existed, the public cloud dominated by Amazon, Microsoft and Google, and the private cloud dominated mainly by VMware.

Development teams depend on microservices to build, deploy, and scale features independently. Microservices have become the backbone of modern, scalable applications. Scaleway’s managed Kubernetes service (Kubernetes Kapsule) offers a powerful, cost-effective platform for running containerized workloads in the cloud. It’s a great fit for startups and solo engineers who want to focus on shipping features, not managing infrastructure.

Stop guessing on AI hardware. This DevOps guide details when to use TPUs vs. GPUs for optimal performance, cost, and framework compatibility in MLOps.

Machine learning pipelines are getting heavier by the day. From model training to large-scale inference and data preprocessing, compute demands are scaling faster than teams can manage. Kubernetes clusters groan under unpredictable job spikes. Static infrastructure wastes money when workloads slow down. The result? Organizations are perpetually chasing flexibility, automation, and cost efficiency. AWS has quietly built a solution to establish that balance.

If you’ve managed traffic in Kubernetes, you’ve likely worked with Ingress controllers. For years, Ingress has been the standard way to expose HTTP and HTTPS services. But in practice, it often came with trade-offs. Controller-specific annotations were required to unlock critical features, the line between infrastructure and application responsibilities was unclear, and configurations often became tied to the implementation rather than the intent.

In the era of cloud-native architectures, Site Reliability Engineering (SRE) has matured from a discipline focused purely on uptime to a sophisticated practice of efficient reliability. The key driver for this evolution is an undeniable truth: cloud spend has become intrinsically linked to system stability.

Observability in DevOps: Diagnose system failures faster. Learn how true observability differs from traditional monitoring. End context-switching, reduce MTTR, and resolve unforeseen issues quickly.

Simplify Kubernetes Deployment. Learn the difficult 6-step manual process for deploying Docker containers to Kubernetes, the friction of YAML and kubectl, and how platform tools like Qovery automate the entire workflow.