Pricing is rarely the primary reason an organization picks a cloud provider, but it is always the reason they reconsider one.
Editorial team 
Kubernetes GPU Scheduling with Node Pools: The Setup
Kubernetes GPU scheduling with node pools comes down to one contract: a vendor device plugin registers the accelerators with the kubelet, the node advertises a schedulable extended resource such as nvidia.com/gpu, and your containers consume it through resource limits. The scheduler then treats a GPU like any other allocatable resource. …
AWS Console in Brazil: What Engineers Need to Know
Brazilian cloud engineers interact with the AWS Management Console daily, but the experience differs meaningfully from what counterparts in us-east-1 or eu
AWS Brazil Office: What Cloud Engineers Need to Know in 2026
Since AWS launched its first South America Region in São Paulo (sa-east-1), Brazil has evolved from an emerging market footnote into a strategic hub for cl
Azure for Enterprises: Architecture, AKS, and Multi-Cloud Strategy
Enterprise adoption of Microsoft Azure has shifted from experimental workloads to full-scale platform commitments.
GCP White Logo Usage Rules for Cloud Engineers
The Google Cloud Platform white logo appears on dark-themed monitoring dashboards, internal wikis, CI/CD pipeline visualizations, and multi-cloud architect
AWS Brazil Data Center Cooling: What Cloud Engineers Need to Know
Brazil’s tropical climate presents a distinct challenge for hyperscale data center operations: maintaining optimal server operating temperatures with high
GCP vs Firebase: What Cloud Engineers Need to Know
Google Cloud Platform and Firebase are not competing products in the traditional sense — Firebase runs on top of GCP.
Kubernetes GPU Autoscaling: Why LLM Inference Stalls
GPU autoscaling on Kubernetes breaks for LLM inference because the Horizontal Pod Autoscaler cannot see accelerators and the node layer takes minutes to add one. The fix is a four-layer stack — DCGM exporter, Prometheus Adapter, HPA, and KEDA over Karpenter — because better GPU allocation sharpens placement without touching …
K8s GPU Clusters Waste 95% of Capacity — Top Teams Don’t
Production Kubernetes GPU clusters across AWS, GCP, and Azure average just 5% utilization — with CPU at 8% and memory at 20%. CPU overprovisioning jumped from 40% to 69% year over year. GPU prices are rising for the first time since 2006. The top-performing clusters sustain 49% GPU utilization, proving …
AI SRE vs Rule-Based Automation: The Agentic Shift
Rule-based automation fires on fixed threshold crossings and executes manually authored playbooks. When CPU exceeds 80%, the script restarts the pod. When latency breaches SLO, the circuit breaker trips. This works for known failure modes but collapses when signals conflict or when root causes span multiple subsystems. A traditional alert …
Editorial team 
DRA Killed the GPU Device Plugin: K8s AI Scheduling in 2026
NVIDIA’s DRA Donation Ends GPU Blindness At KubeCon Europe 2026 in Amsterdam, NVIDIA killed the GPU device plugin model by donating its Dynamic Resource Allocation (DRA) driver for GPUs to the Cloud Native Computing Foundation. That single act retires the device plugin that has made Kubernetes treat your H100 identically …