Operations | Monitoring | ITSM | DevOps | Cloud

600 People Still Isn't Fast Enough for IT Operations

What happens when even 600 people aren't enough to keep up with incident volume? In this clip from BigPanda's Project Zero conversation, Tal explains why adding more L1 headcount doesn't necessarily mean faster incident resolution. The challenge isn't just capacity. It's the manual operating model. Watch the full Project Zero conversation on our channel.

From Bottleneck to Breakthrough: Gain Control and Confidence in Your Monolithic Database Changes

This webinar explains how development teams can speed up database changes in a monolithic database without rewriting or replacing it, using Redgate Flyway for version control, automated code review, undo scripts, drift detection, and auditable deployments across SQL Server, Oracle, PostgreSQL, and MySQL.

How to Capitalize AI Spend Without the Gray Area

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.

The $600 AI Cap That Sent Employees Golfing

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 Is Forcing Finance to Redefine KPIs

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.