Operations | Monitoring | ITSM | DevOps | Cloud

Cost monitoring, exploration, investigation, and reporting used to live in separate places, so following a single cost question meant hopping between tools.

Dynamic Host Configuration Protocol (DHCP) activity changes faster than most IP inventory systems can keep up. Devices reconnect. Leases expire. Infrastructure changes constantly across servers, endpoints, and cloud environments. If your IP inventory cannot reflect those changes automatically, teams quickly lose confidence in the data they rely on to operate the network.

The default cloud choice for technically literate teams has shifted. It hasn't shifted dramatically; the major hyperscalers aren't going anywhere, and their enterprise position is still strong, but the conversation that used to start with "which hyperscaler" now genuinely starts with "what do we actually need." That's new.

Your AI calls already emit OpenTelemetry: your LLM gateway exports it, and it’s the open standard your own services can speak. But you don’t have anywhere to turn those spans into spend you can allocate to an outcome. Now you can. CloudZero exposes an OpenTelemetry endpoint that doesn’t care what’s on the other end.

GPU utilization is one of the most expensive metrics in cloud infrastructure to get wrong. A GPU running at 30% utilization costs the same as one running at 90%, but it's doing a third of the useful work. For workloads measured in tens of thousands of GPU-hours, the difference between average utilization in the 30s and average utilization in the 70s is hundreds of thousands of dollars across the life of the workload.

The last post in this series looked at how disconnected alerts can slow incident response and how stronger correlation helps teams investigate issues with more clarity. That same operational context has value beyond triage. It also plays an important role in resilience, service assurance, and the ability to maintain confidence across increasingly complex multi-cloud environments. Resilience depends on more than reacting well during an outage.

You can already split AI spend by team and by model. But that’s not what your CEO asks in the QBR. The question is what you got for it: what did it cost to ship that feature, to launch that campaign, to serve that customer. And is the AI bet behind it paying off? Now you can allocate AI spend to the outcomes you own: customer, product, feature, the strategic bet on the P&L. Not just the team that spent it.