Vantage shows what you rented. We prove what you changed.
Vantage is a genuinely good developer-first cost platform, with native ingest across twenty-plus providers including OpenAI and Anthropic. It answers what you spent. It does not stamp the call by team and feature, gate a cheaper model behind judged proof, or prove the saving against a control group of your own spend. That is the LLM-first job TensorCost was built for. If part of your fleet is bought outright or reserved for a year, buy-versus-rent joins the same ledger later, though it is not the opener.
At a glance
Concrete capabilities, not adjectives. Where Vantage is strong, we say so.
Where we differ
Three capabilities Vantage does not have, and why they matter at scale.
GPU cost down to the agent
Vantage shows GPU spend as an instance line item. TensorCost runs a read-only monitoring agent alongside your fleet: per-node utilization, hardware partition health, spot eligibility, and which workload owns which GPU. If five AI services each occupy a separate GPU at 15% utilization, TensorCost surfaces the consolidation opportunity and the dollar impact.
One schema for all five inference providers
Vantage pulls from cloud billing APIs. Direct AI provider bills from OpenAI, Anthropic, and others are invisible to it. TensorCost's provider connections write every provider's spend to a single unified record. One view for total AI spend across all your providers, attributed by team, feature, model and user, reconciled daily against the actual invoice.
Routing, not just observing
Vantage tells you what you spent. TensorCost identifies what to change: which requests should route to a more cost-effective model (quality verified against your own test set), whether your committed AI capacity is correctly sized, and whether a job is running out of control. Inline routing shipped in August 2026, default-off on every policy until it has earned shadow proof on your own traffic.