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Most cost questions get answered the same way. You read a chart and the table under it together. Maybe you spot a jump on the chart, and then you go looking in the table for the service, account, or team that caused it. When you can see more of that table while the chart is still in view, the whole review goes faster. You’re also a lot less likely to lose your place halfway through.

Activity categorization classifies every AI request by the work it performs, such as meeting prep, code review or account research, then assigns that spend to the team, person, model and provider behind it. It turns a provider invoice that shows only totals by model and API key into a breakdown finance can allocate, charge back and budget from. CloudZero AI Signals does this as the spend happens.

Amazon told its own engineers this week to stop tokenmaxxing. The memo circulated internally in September and Amazon published it on Tuesday, Oct. 27. Tokenmaxxing is the practice of using AI as heavily as possible with little regard for whether the output justifies the spend. The memo calls token usage, adoption rates, and code-change counts “deeply flawed” ways to measure a team.

Insights are how your team tracks interesting observations, analysis and optimization work in CloudZero. Each one records the opportunity and its status, so the work gets sized, assigned, and closed out. Teams now do a lot of this work in an AI assistant. For example, you can ask Claude which instances sat idle last month, and it will tell you. But if you have to switch to CloudZero and fill out a form to log that finding, some findings won’t get logged.

AI cost per customer is each customer's attributed share of your AI spend: API tokens, inference infrastructure, and per-seat AI tools, measured against that customer's revenue. It matters because AI usage is wildly uneven across customers, so healthy blended margins can hide individual customers you serve at a loss. It's cost to serve, not acquisition cost. Cost per customer is the metric CloudZero was built around.

AI cost per query is your total AI serving spend divided by the number of queries served over the same period. It combines model API charges (input and output tokens), retrieval and orchestration overhead, and shared infrastructure. Most production text queries cost between $0.0005 and $0.02. It's the base unit of AI unit economics, and the number finance needs before any ROI claim holds up.

Azure cost exports sometimes fail with a server error and then succeed on the next try. Some reports cover only part of the month. Large tenants get rate-limited when they request a lot of data at once. Each of these things can leave a gap in your Azure history. You normally find the gap when finance compares the Azure total in CloudZero to the Microsoft invoice.

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.