Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

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.

Changing a Kubernetes resource request takes two numbers: what the workload requests, and what it uses. The CloudZero MCP server now returns both, by cluster, namespace, or workload. This gives you a number you can defend. Usage comes back as P95 over the date range you query, 30 days by default. When an engineering lead asks whether a service runs on a smaller request, that is the figure that settles it. Over-provisioning and under-provisioning show up on the same query.

Cycle recently went through a System and Organization Controls (SOC) 2 Type 1 audit and we've got the report. It’s an important step in our continuous commitment to data security and system availability. But are we just checking boxes for the sake of boxes or is there more behind it?

Every organization running AI agents has already made a hosting decision. Most made it by accident. The sales team switched on the agent built into their CRM. Engineering is piloting a coding agent in a vendor’s cloud. Someone on the data team deployed a LangGraph service to a VM with a database key in an environment variable, and someone else is running an agent framework on a laptop with production credentials in a dotfile. Each of these is a hosting decision.