Operations | Monitoring | ITSM | DevOps | Cloud

Organizations are struggling with rising operational complexity, fragmented tools, and inconsistent security enforcement as Kubernetes becomes the foundation for modern application platforms. As a result of this complexity and fragmentation, platform teams are increasingly burdened by the need to stitch together separate solutions for networking, network security, and observability.

We’ve all been there. It’s 2 AM, your phone is buzzing with alerts, and you’re suddenly thrust into an incident war room with a dozen other bleary-eyed engineers. The production environment is on fire, customers are affected, and everyone’s trying to piece together what went wrong. But here’s what makes these moments fascinating from a systems perspective – it’s rarely just one person silently fixing the issue in isolation.

Managing Docker container logs is essential for debugging and monitoring application performance. Tailoring Docker logs allows for real-time insights, quick issue resolution, and optimized performance. This guide focuses on efficient methods for tailing Docker logs, with clear examples and command options to streamline log management.

Telecommunications networks are undergoing a cloud-native revolution. 5G promises ultra-fast connectivity and real-time services, but achieving those benefits requires an infrastructure that is agile, low-latency, and highly reliable. Kubernetes has emerged as a cornerstone for telecom operators to meet 5G demands.

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.