Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on Containers, Kubernetes, Docker and related technologies.

Monitor TAS and gang scheduling for AI training in Kubernetes

Distributed AI training workloads impose complex scheduling requirements that Kubernetes’s built-in scheduler can’t meet. Kubernetes schedules pods individually and independently, but distributed training introduces two requirements that break this model: Pods must land on hardware with the right inter-GPU bandwidth, and all pods must be scheduled simultaneously. If either requirement goes unmet, training stalls or runs far below the hardware’s potential.

Stop Building Your Own Agent Infrastructure. Meet Agent Tasks

Agent Tasks let you run one-time or scheduled AI agents on your own infrastructure, with network guardrails, centralized MCP access and custom agent environment. Alessandro leads product at Qovery. He drives the changelog, roadmap, and product strategy - turning customer feedback into platform capabilities.

Your existing kit just became more valuable

Hardware costs are rising. But Civo Product Director Russ Smith has a different take: your existing kit just became more valuable. The hyperscalers competing for the same DRAM and compute as you still have to pass that cost on eventually. At high utilisation rates, your resource rental overtakes purchase cost. Typically in under a year.

AI Agents on Kubernetes 101: From Laptop Script to Production Pod

In short, this is a beginner’s guide to deploying an AI agent on Kubernetes. You will containerize an agent, store its API key as a Kubernetes secret, write a deployment with health probes and resource limits, expose it with a service, and lock down its network egress, in that order, with a working manifest at every step. On a local kind cluster the whole walkthrough takes about an hour.

Container hardening isn't a substitute for artifact management

Hardened base images are a great secure foundation. They're minimal, security-vetted, and have few dependencies to worry about. But almost nobody ships a bare base image. Teams build on top of it. This video cover whys that "on top of it" layer is where the risk actually lives: Skip the base image hardening and you're building on a shaky foundation. Skip artifact management and you're leaving everything built on top of that foundation ungoverned. A strong posture uses both.

Railway vs Render vs Your Own Cloud Account: What Actually Fits a Scaleup Outgrowing Managed PaaS

An honest 2026 comparison of Railway, Render, Fly.io and deploying into your own AWS, GCP, Azure or Scaleway account - with the six measurable signals you have outgrown managed PaaS, a priced cost model, and a four-step framework with if-then verdicts. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

Making Shared GPUs Even Safer with Kubex and HAMi-core

Table of Contents A few months ago, we introduced Kubex support for the KAI Scheduler to improve GPU sharing for production inference workloads. The basic model is simple: The KAI Scheduler handles placement and GPU sharing. Kubex continuously observes usage and adjusts those allocations as demand changes. KAI provides the scheduling foundation. It lets multiple workloads share a GPU while accounting for the amount of GPU each workload requests. Kubex then closes the loop.

We renovated the Civo Community Slack: Here's what changed and why

The Civo Community Slack has become home to over 35,000 engineers, platform teams, students, and practitioners. It’s one of the things we’re most proud of, a genuine space where the people who use Civo and the people who built Civo are in the same room. Since starting the Civo Community Slack, we’ve shipped an entirely new brand, launched Konstruct, and expanded our AI infrastructure.

Moving Beyond OOM Kills: Introducing Memory QoS in Kubernetes 1.37

Table of Contents For most of Kubernetes’ history, memory management has been a blunt instrument. Cross your limit, and the kernel kills your container. There has been no equivalent to CPU throttling, no graceful backpressure, just a hard stop. With Kubernetes 1.37, that changes: Memory QoS, built on cgroups v2, graduates to Beta and is enabled by default.