Operations | Monitoring | ITSM | DevOps | Cloud

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.

Remote work has changed more than where we work. It has reshaped how teams collaborate, how organizations manage performance, and how employees balance professional and personal responsibilities. Now, AI is adding another dimension to this transformation. From AI assistants that summarize meetings to automation tools that handle repetitive tasks, technology is making remote work more efficient in several ways.

Your employees have access to AI. They've completed the training. Their usage is climbing, but has the way they work actually changed?

For enterprise IT leaders, the question is no longer whether AI agents can help run IT operations, but how to scale them beyond pilots and into the everyday work of keeping the business online. It’s no coincidence that this year’s Gartner IT Infrastructure, Operations & Cloud Strategies Conference (IOCS) opens with a keynote dedicated to scaling agentic AI to enable autonomous IT operations. At BigPanda, we’ve seen a shift from the ITOps leaders we work with every day.

Artificial intelligence (AI) is changing how IT teams manage service requests, troubleshoot technical issues, and respond to incidents. As IT environments become increasingly complex, traditional service management processes can struggle to keep up with growing ticket volumes, recurring incidents, and rising expectations for faster resolutions.

“AI workflow tool” now describes products with very different build models. One may generate an executable workflow from a plain-language request. Another may add a chat panel or autocomplete to the same drag-and-drop experience it has offered for years. That difference matters to buyers because teams choose workflow tools to reduce automation backlogs, eliminate repetitive work, and extend orchestration across ITSM, cloud, network, identity, and infrastructure systems.

This is a guest post from Armin Ulrich, a fullstack developer, and founder of placid.app. He also created the MadeWith* network where he shares his projects and allows other developers to share theirs. Two people and one beagle run the project showcase madewithvuejs.com. The beagle is actually an AI agent that we have added to our workflows, but we’re very fond of her by now. Milly is part of the team, scouting for content, building features and sniffing for bugs.

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.