Operations | Monitoring | ITSM | DevOps | Cloud

Most people who open CloudZero care about one slice of the spend, like their team, their product, or their region. A View gives them that slice in one click, with the grouping and filters already set, so nobody has to rebuild the same Explorer query every week. Views now work for everyone in your organization, including people with scoped access.

Most AI spend comes in with no tags and no owner attached. Your provider console shows total spend, maybe broken out by API key or model. It won’t tell you that the sales team spent $1,700 on Claude this week, let alone what the work was. And the problem is growing. McKinsey found that 56% of organizations now use AI in three or more business functions. More teams means more spend, and most companies respond with a spending cap. Set it too low and you slow down the work you wanted AI to help with.

Most people who use CloudZero spend their time in one or two places. For some it’s AI Signals, and for others it’s Optimize or Anomalies. If Explorer isn’t one of those places, every sign-in starts with a click to get where you need to be. Dates and numbers are another friction. A date like 04/07 means April 7 in the US and July 4 in much of Europe. When the platform shows a format your team doesn’t use, you end up having to convert each value before you can work with it.

Discover why engineers ignore cloud cost governance and how to build developer cost accountability. Learn how Harness helps empower engineering teams. Engineers often overlook cloud costs due to friction in traditional FinOps tools and a lack of real-time visibility. By embedding automated guardrails and shift-left cost insights into developer workflows, organizations can drive accountability without slowing velocity.

AI spend now comes from every department, and it can double in a week without anyone deciding it should. The invoice arrives after the month closes. By then the usual response is a spend cap, which slows every team, including the ones getting real work done with AI. You need to know about spend that breaks its normal pattern while there’s still time to act. The alert should reach the person who can act on it, with proper context and detail.

On June 3, we made the new CloudZero experience the default for every customer. Since then, we’ve shipped around 30 improvements a week: side-by-side period comparisons in Explorer, budgets you can create and edit right in the app, threshold alerts on dashboard tiles, and Monitors, which flags AI and cloud spend that moves outside its normal pattern and shows you what changed. Pages load 28 to 61% faster. JavaScript execution is 85% faster.

AI cost allocation is the practice of attributing every dollar of AI spend to the team, product, feature, or customer that generated it. That spend includes API tokens, GPU compute, per-seat tools, and shared infrastructure. It's harder than cloud allocation because AI spend arrives untagged, spans vendors, and pools in shared resources. Four methods cover most cases: tag-based, key-based attribution, proportional split, and usage-telemetry.

Learn why engineers ignore cloud cost optimization and how to build a culture of FinOps governance. See how Harness helps. Engineers often overlook cloud costs due to lack of visibility, fragmented tooling, and competing delivery priorities. Organizations can fix this by embedding FinOps guardrails into developer workflows and providing real-time cost feedback during build cycles.

If you were anywhere near LinkedIn last week, you probably saw us launch AI Signals. We weren’t exactly quiet about it. (Press release, a couple of blog posts, and more social posts than we’d like to admit. Sorry about your feed.) We covered the why behind AI Signals already, but I wanted to actually walk you through what you’re seeing on the screen. Sooner or later someone asks what the company spent on AI last month.

If you set the budget for your team’s AI agent work, or answer to someone who does, you need a rough idea of what a job will cost before it starts. That’s hard to get. Stanford researchers found the same agent, given the same task, can use up to 30 times more tokens from one run to the next, and you usually find out afterward. Most developers just run the job.