CloudZero attributes the invoice. We gate the next change.
In May 2026 CloudZero repositioned as the AI ROI company and shipped a financial control plane that attributes spend by model, team, feature and token in real time. It is a good product and the allocation model is the right one. Attribution alone does not move traffic. Whoever swaps the model owns the quality risk. TensorCost shows the last 30 days, gates the next change behind judged proof, and proves it held against a control group of your own spend. If you also own or reserve GPUs, that chapter joins the same ledger, though it is not the opener.
At a glance
Where CloudZero is strong, this table says so.
Where we differ
Three structural differences. Not features: the underlying scope each product was designed for.
Hardware you own, costed like an asset
Token attribution by model, team and feature is no longer a difference between us. CloudZero shipped that in May 2026 and it works. The difference is what happens when the capacity is yours. A GPU you bought is not a line on an invoice; it is an asset with a purchase price, a useful life, power and cooling behind it, and an occupancy rate. TensorCost amortises it, reads real utilisation off the card, and gives you a cost per workload-hour you can compare against the rented alternative. Reading a cloud billing API cannot produce that number, because the number was never on a bill.
GPU fleet visibility with per-node telemetry
CloudZero reads from cloud billing APIs, so GPU spend is an instance-type row with no workload-level breakdown. TensorCost runs a read-only monitoring agent alongside your fleet: per-node utilization, partition health, spot eligibility, which AI workload owns which GPU slice. If five AI services each occupy a separate GPU at 15% utilization, CloudZero reports the five infrastructure lines. TensorCost surfaces the consolidation opportunity and estimates the dollar impact.
Automatic routing as active control
CloudZero measures AI spend as well as anyone, down to prompt pattern and in real time, and then hands the decision to you. There is no autonomous remediation claim anywhere in their product. TensorCost sits in the request instead: a repeat prompt is served from cache and costs nothing, a runaway agent run stops at its budget with streamed calls counted, and a model swap executes with the request shape translated between vendors. The live routing path is shipped and gated per policy. Nothing is ramped, so no customer traffic is being re-routed today, and everything else in that list is running.