
The Infrastructure Decisions That Are Hardest to Undo
Learn which infrastructure decisions are hardest to undo, from storage and networking to identity, integrations, data location, and APIs.
Read field noteField notes / Latest
Engineering notes from operating open infrastructure: the failures, design decisions, and upstream work that make open infrastructure better.
Browse all field notes
Learn which infrastructure decisions are hardest to undo, from storage and networking to identity, integrations, data location, and APIs.
Read field note
Learn how to safely upgrade production GPU infrastructure across NVIDIA drivers, CUDA, Kubernetes, firmware, containers, and AI workloads.
Read field note
Your cloud bill is only part of the cost. Learn how operations, licensing, data movement, utilization, and migration affect cloud TCO.
Read field noteTrends, best practices, and technical deep dives on open source cloud infrastructure.

Learn which infrastructure decisions are hardest to undo, from storage and networking to identity, integrations, data location, and APIs.

Learn how to safely upgrade production GPU infrastructure across NVIDIA drivers, CUDA, Kubernetes, firmware, containers, and AI workloads.

Your cloud bill is only part of the cost. Learn how operations, licensing, data movement, utilization, and migration affect cloud TCO.

Build an AI infrastructure landing zone for secure, repeatable GPU access across teams with standardized compute, storage, networking, quotas, and operations.

Compare dedicated GPUs, MIG, vGPU, and time-slicing for enterprise AI. Learn how isolation, performance, utilization, and workload type affect GPU allocation.

Why infrastructure flexibility matters and how open cloud technologies can help organizations adapt as workloads and business needs change.

A GPU cloud is only as reliable as the control plane underneath it. Seven staged verification gates, from Kubernetes health to physical acceptance testing, that isolate real hardware failures from software bugs before they reach production.

Learn how AI workloads change network design across GPU clusters, storage, east-west traffic, RDMA/RoCE, Kubernetes placement, and production inference.

Booting a GPU server isn't the same as making it production-ready. How Atmosphere unifies OpenStack, Ironic, and Kubernetes to turn H200 hardware into recoverable, reusable AI infrastructure.

Compare GPU VMs, bare metal, Kubernetes, and managed inference for AI workloads. Learn which architecture fits your performance, control, scaling, and operational needs.