Operations | Monitoring | ITSM | DevOps | Cloud

AI Signals adds a standard set of AI dimensions to your dimension picker, plus custom dimensions built from your own data. Not every team needs all of them. The ones you don’t use make the ones you want harder to find. Cleaning that up used to require an engineer to run a script every time an account team needed a change. We identified the bottleneck and automated it, so account teams can manage dimensions themselves and engineering stays focused on product work.

Every number in CloudZero comes from a billing connection. When a credential expires or a permission changes, that connection stops sending data, and your reports show less spend than you actually have. If you catch it early, you can fix the connection before month-end close. You also need to know what broke. For Azure and Snowflake connections, CloudZero now tells you which check failed, so you can send the fix to the right person without opening a support ticket.

Training an AI model costs anywhere from a few hundred dollars to several hundred million, depending entirely on what “train” means. Fine-tuning an existing model: tens to thousands of dollars. Pretraining a small model from scratch: tens of thousands to millions. A frontier run in 2026: hundreds of millions, with Epoch AI projecting the largest runs will pass $1 billion by 2027.

You can already break down cloud spend by team, product, or customer in Explorer. But AI spend has been harder to decompose. Which models is each team running? What activity is driving the cost growth you saw this month? Before AI Explorer, answering those questions meant exporting raw event data and building your own breakdowns, or waiting days for reconciled billing to land in Explorer.

Enterprises are putting hard ceilings on AI spend. Accenture now centrally funds $29 a month per person for Claude and charges anything above that to a project budget code. Uber used up its 2026 AI budget in four months and restricted access. A cap stops waste and useful work at the same rate. The alternative is seeing AI cost by team, by project and by outcome, then funding what returns. Companies spent the past year telling employees to use more AI. Now a lot of them are putting a lid on it.

AI agent costs accumulate along more dimensions than a model-call count. Spend is a product of both which models you call and how the agent is architected: how many turns the loop runs, how many tools it invokes, how much retrieved context it carries, and how much of that context is re-sent on every turn. Provider bills tell you how much you spent, but they’re keyed to API keys and models instead of the applications and workflow steps that generated the calls.

Most companies gave everyone AI access and thought they’d sort out the costs later. Engineering, support, product, marketing, and finance all got licenses to the best models. The early results looked good, and for a while it didn’t matter there wasn’t an explicit business case. Let’s call the first half of 2026 the free-for-all period for many companies. AI adoption spread across the org fast, people and teams loved it, and then the invoices started coming in.

When Monitors flags a cost spike, the fix usually belongs to an engineering team, and that team might track their work in Jira. Now you can hand the incident to them from CloudZero in one click. The Jira ticket and the CloudZero incident stay in step. When the engineer resolves the ticket, the incident resolves too. You track the work in one place, and the status you see in CloudZero matches what the team is doing.

Many CloudZero users ask the same few questions. What did my team spend this month? How is our AI spend trending over the last 30 days? What does one MCP tool call cost? Now you can save the exact Explorer setup behind each answer as a View and get back to it instantly. It’s not just filters and grouping anymore, but the full picture. You can share a link with a teammate or finance and they see the same View, filtered to the spend their role allows.

Your company talks about AI spend in its own terms, like projects, teams, and products. Now AI Signals can show AI spend in those terms, too. Write a CostFormation rule in CloudZero that groups AI spend by categories you care about, and the new breakdown appears in AI Signals the same day you decide to track it. There’s no waiting for a full processing cycle.