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While GPUs remain among NVIDIA's flagship products, they also offer a range of other compute products beyond the dedicated graphics cards for which they are known. If you are unfamiliar with the words DGX or HGX, this blog is for you. Throughout this blog, we will cover what these terms mean in practice and when you should be using them.

If you’re building or running AI-powered features in production, you need a clear understanding of inference costs. Get it right, and you can turn your AI investments into profitable growth. As Larry Advey, Director of Cloud Platform and FinOps at CloudZero and a member of the FinOps Foundation Technical Advisory Council, puts it: “AI investments will only continue to grow.

Earlier this month, we announced the CloudZero Claude Code Plugin and the CloudZero AI Hub — the first step toward putting your cloud cost data directly inside the AI tools your team already uses. The feedback from customers was clear. They said engineers and FinOps teams wanted more tools and more ways to get answers from CloudZero without switching context. Today, we’re delivering more.

For most of the history of software engineering, the primary constraint was production. Code was expensive, skilled engineers were scarce, and shipping features required concentrated human effort. Velocity was limited by how fast people could reason, implement, test, and deploy. That constraint shaped everything from team size, architecture, release cadence, through to how we thought about technical debt. When production is expensive, you optimise for output. You remove friction from shipping.