Operations | Monitoring | ITSM | DevOps | Cloud

In my previous posts, we discussed the where and the how of managing your ML assets. We showed you how JFrog Artifactory acts as a powerful, universal model registry (the “where”) and how the FrogML SDK serves as the gateway to get your models and metadata into it (the “how”). Now, let’s talk about the why.

Stop guessing on AI hardware. This DevOps guide details when to use TPUs vs. GPUs for optimal performance, cost, and framework compatibility in MLOps.

Welcome to December’s edition of CloudZero’s Cloud Economics Pulse — your monthly read on how cloud spend is shifting across providers, services, and AI workloads. No surprises here — November continued the quiet reshaping trend we’ve seen all year. Compute softened, data layers grew, and AI/ML hit its highest share yet. AWS extended its lead, Azure and GCP nudged upward, and the emerging “AI layer” of providers continued to take shape.

A few months back, a backend team at a fast-growing SaaS company shipped what seemed like a harmless feature. Just a simple request validation layer. No new service. No major dependencies. No architectural shock. Yet two months later, their cloud costs had climbed 38% without any significant increase in traffic, storage, or compute load. What they’d missed was that the validation layer triggered a fan-out pattern.

Traditional testing methods often delay the software development lifecycle, as we have grown used to these outdated processes without considering alternatives. Ephemeral environments introduce a more efficient solution. They allow for the quick creation and dismantling of isolated testing environments. These isolated environments approach leads to faster and more productive development cycles while still delivering high-quality software to users.

AI coding assistants have transformed how developers write and debug code. But there’s a gap: these assistants often can’t see what’s happening in your CI/CD pipelines. When a build fails, you’re still stuck switching tabs, hunting through logs, and copying error messages back into your editor. What if your AI assistant could talk directly to CircleCI? In this tutorial, you’ll learn how to connect Cursor, an AI-powered code editor, to CircleCI using the CircleCI MCP server.

On December 3, 2025, the React team announced a critical security vulnerability in React Server Components (RSC). Identified as CVE-2025-55182 (and covering the now-duplicate CVE-2025-66478), this flaw allows unauthenticated attackers to execute arbitrary JavaScript code on backend servers.