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The latest News and Information on Cloud monitoring, security and related technologies.

control plane

Cloud disaster recovery strategies, RTO/RPO targets, and best practices

Aug 14, 2026 By Aykut Bulgu In Control Plane
Most DR plans have a fatal assumption baked in, namely that the cloud provider stays up. Your runbook says “failover to us-west-2.” That works until the provider’s control-plane services degrade and the credentials, APIs, or DNS management your failover workflow depends on become unreliable. Single-provider DR is disaster deferral.
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upsun

Shared context for AI coding agents beats better tooling

Aug 13, 2026 By Upsun In Upsun
The instinct when adopting AI coding agents is to optimize the agent. Compare models, tune prompts, argue about which editor has the better completion, and treat the agent as the thing that determines how fast the team moves. Then the commits go up and the product does not. The team building Upsun Dispatch took a different route, and the result is worth copying. They did not find a better agent.
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cloudzero

Shipped: Monthly cost comparison in Explorer gets a glow up

Aug 13, 2026 By Kaitlin Woo In CloudZero
Months have different numbers of days, and a monthly cost chart built on raw totals mixes that calendar difference into the trend. A 28-day February next to a 31-day March shows a 10.7% increase even when daily spend never moved. The same math works in reverse: real growth in a short month can look flat, hiding an increase worth investigating. That costs you time in two places. The first is triage.
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cloudzero

AI budgeting: how to plan and forecast AI spend

Aug 13, 2026 By Lyne Carolyne In CloudZero
AI budgeting is the process of planning, allocating, and forecasting an organization's AI spend: model and API costs, AI infrastructure, tooling, and the people running it all. It differs from traditional budgeting because AI spend is usage-based, scales with product success rather than headcount, and often spans multiple providers.
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Sharing GPUs without Flying Blind: Kubernetes Patterns for AI Inference

Aug 13, 2026 By Kubex In Densify
GPU sharing is quickly becoming a practical requirement for Kubernetes-based AI inference, as many modern workloads don’t need a full GPU to deliver value. But safely placing multiple containers on the same accelerator brings new challenges: scheduling, fairness, isolation, observability, and noisy-neighbor behavior. This 20 min session explore the GPU sharing landscape across Kubernetes: time-slicing, MPS, MIG, KAI Scheduler, and HAMi, and dives into the harder problem: operating shared GPUs in production, from tracking usage to enforcing fairness as demand shifts.
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Building an End-to-End Drone Ecosystem: The Technologies That Need to Work Together

Building an End-to-End Drone Ecosystem: The Technologies That Need to Work Together

Aug 13, 2026 By OpsMatters In OpsMatters
Commercial drone technology is rarely a single application running alongside an aircraft. A complete solution may include flight software, onboard sensors, telemetry, cloud infrastructure, web and mobile interfaces, data processing pipelines, analytics tools, and integrations with existing business systems.
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cloudzero

Shipped: Cost anomalies and savings recommendations, delivered into ServiceNow

Aug 12, 2026 By Kevin Lamb In CloudZero
If your engineering teams run on ServiceNow, incidents are where they get work done. Putting cost work into an incident gives it the same path to resolution as any other work item your team handles. When a cost anomaly arrives as an incident, your teams route it, assign it, and resolve it on their usual SLAs. When a savings recommendation arrives as an incident, an engineer owns it and acts on it. Now you can send either straight into ServiceNow.
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cloudzero

How to build the business case for AI

Aug 12, 2026 By Doug Bonderud In CloudZero
A strong AI business case ties a specific goal to a measured outcome and a fully-loaded cost. Most fail because they skip one of the three: no clear mandate, an over-broad "AI fixes everything" scope, or a cost estimate that ignores adaptation and error-correction. Build it in six steps: define goals, identify uses, break work into tasks, evaluate models, assess total cost, then launch and refine. Most companies are now spending on AI. Far fewer can show what they got back.
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cloudzero

How to right-size your existing Claude skills

Aug 12, 2026 By Djo Lopez In CloudZero
You shipped a skill. It worked. You closed the tab. That’s the whole problem. Model choice is a decision you make once, at the moment you’re least equipped to make it: before the skill is even authored. Then you never revisit it, because the skill stopped being interesting the day you got it working. So go back and check. Here’s how.
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jfrog

JFrog Artifactory Now Integrates Natively with Artifact Registry in Google Cloud

Aug 12, 2026 By Eldad Assis In JFrog
Teams running containerized workloads on Google Cloud have long relied on JFrog as their single source of truth for container images. The missing piece has been getting Google Cloud’s own runtime services — like Cloud Run and Google Kubernetes Engine (GKE) — to pull directly from JFrog for every container image pull. I’m happy to say that the gap is now closed. Artifact Registry in Google Cloud has introduced a new repository mode called Connector that addresses this requirement.
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