Finout's agents act on your infrastructure. We act inside the call.
Finout is a genuinely good FinOps platform, and their Virtual Tags are the best answer in the market when your tagging is a mess. They now ship autonomous agents that detect, investigate and remediate — so "they only report" is not a thing anyone should say about them. What their loop acts on is the infrastructure bill, from outside your application: an idle node group gets rightsized. TensorCost acts one layer down, inside the inference request itself, because you put us there with one line of code. A repeat prompt served from cache costs nothing. A runaway agent run stops at its budget, streaming included. A model swap actually executes, because we translate the request shape between vendors.
At a glance
Where Finout is stronger, this table says so.
Where we differ
Three structural differences. Not features — the layer each product operates on.
Where the loop closes
This is the whole difference, and it is worth being precise about. Finout closes its loop on the infrastructure bill: an agent notices an idle node group and rightsizes it, from outside your application, with no code integration. That is a real capability with a real ceiling — the infrastructure bill. TensorCost closes its loop inside the request. A cache hit is a call that never costs anything. A budget breach refuses the run's next call. A model swap executes rather than being recommended. None of that is reachable from a billing feed, at any level of sophistication, because the decision has already been made by the time the money shows up on a bill.
One line of code, and the hardening behind it
Install @tensorcost/sdk from npm or tensorcost from PyPI and wrap your existing client. Observe mode ships metadata only — provider, model, token counts, latency, status — never prompt or completion content. Applied mode puts your traffic through our proxy, with the failure handling that has to exist before anyone sane would allow that: jittered backoff honouring Retry-After, typed timeouts, and a fail-open circuit breaker that routes straight to your provider after three consecutive proxy errors. If TensorCost is down, your calls still work.
GPU utilization, not GPU invoices
Finout allocates GPU cost from what the instance was billed at. That number is the same whether the card ran at 90% or 6%. Our agent reads the card itself, so an owned or reserved GPU becomes an asset with a purchase price, a useful life and an occupancy rate — and the cost per useful hour can be compared against renting the same work from an API. That comparison is the join we were built for, and it does not exist in a bill.