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In this video, Emily Allen, Director, Finance & Accounting at CloudZero, explains why AI capitalization is still unclear for most finance teams, and how CloudZero gives them a repeatable approach. As AI writes more of the code, finance needs to know which AI spend went to qualifying development work and which should be expensed. CloudZero records which project each AI coding session served, what kind of work it was, and what it cost, so finance has the evidence behind every decision.

In this video, Brady Lenahan, VP of Sales at CloudZero, shares what he’s hearing from customers when the cap hits before the month ends: employees who’ve built their workflows around AI stop working productively and wait for next month. Some go golfing, some clean the garage. Either way, finance loses both the productivity and the visibility. There’s a better option than capping AI spend you can’t see.

AI Budgeting for Finance: Last Year vs. Now In this video, Emily Allen, Director, Finance & Accounting at CloudZero, explains how AI planning changed in a single year. Instead of starting from a vendor commit or the current run rate, she’s treating AI spend as its own category and analyzing it bottom-up. That opens a new question for finance: “What does good look like?” With a year of data, finance teams can start benchmarking AI spend over time and check whether what they approved actually paid off.

We gave $10k worth of data to five different observability platforms. Which agent finds the true answer? In the first episode of our benchmarks series, Chris and Annie walk through how an agent performed on Coralogix, Datadog, New Relic, Dynatrace and Grafana Cloud across 10 injected incidents. They also explain why sampled data makes agents "hallucinate the gap". Subscribe to our channel for more benchmarks and insights into observability and AI.

Learn what a unit test is, how AI coding assistants can write them for you, and the three key conditions every unit test should check to ensure software quality. Learn more: speedscale.com.

Agentic IT operations (Agentic ITOps) deploy autonomous AI agents that can perceive, reason, plan, and act to investigate incidents, coordinate people, and improve with every incident they touch. Think of it as the next step in a progression, where monitoring surfaced the symptoms, AIOps separated signal from noise, and Agentic ITOps takes action on what it finds.