Where did all the AI money go?
Every month you spend more on the GPUs you bought and the tokens you rent — and nobody can tell you what a given workload actually cost, or which team it belongs to. TensorCost prices your own hardware properly, joins it to token spend in one attribution model, and ties every dollar back to a team, a model and a feature. Then it tells you where you paid a vendor for something your own fleet could have run.
Two halves of one bill: the accelerators you own and the tokens you rent.
A training job holding a MIG slice and an inference workload spread across four providers are the same question asked twice — what did this cost, and would something cheaper have done it? Most tools answer one half. Joining them is the whole point.
Measure
An agent samples every GPU through NVML — utilization, memory, power — and labels what the card is actually doing: idle, loading data, forward pass, backward pass, checkpointing or evaluating. Alongside it, five managed-inference adapters and an SDK that tags calls with the workload they belong to.
What you get
Attribution, alerts, chargeback and anomaly detection, across every provider and the GPUs you run yourself.
Real-time cost tracking
Dashboards update the moment something happens, over a live connection. A budget tips past a threshold, an alert gets acknowledged — everyone on your team sees it without hitting refresh.
Cost allocation
Slice your spend by provider, service, model, team or tag, however you actually think about it. You also see what's still un-attributed, instead of pretending it's all accounted for. Save the views you care about and run them again next month.
Budget alerts
Give a team or project a budget. We watch the real burn rate, do the math on where it's headed, and warn you at 75, 90 and 100 percent. The point is to catch the runaway job before invoice day, not explain it after.
Multi-cloud support
One screen for OpenAI, Anthropic, Bedrock, Azure OpenAI and Vertex, plus the GPUs you run yourself. We pull each provider's real bill and check it against what we measured, so adding a provider doesn't add a blind spot.
Chargeback reports
At month-end, run the allocation once and we generate a clean PDF and CSV per team and email it to your admins. Engineering owns its own number. Finance gets to stop chasing people.
Anomaly detection
Every five minutes we compare your GPU usage to what's normal for that day of the week. A node stuck at 99% at 4am shows up with a severity score, instead of waiting to surprise you in the morning.
Up and running in minutes
Three steps and your AI bill starts making sense.
1. Connect your accounts
Give us read-only access to your billing APIs and an IAM role for AWS. No code changes, nothing touches your production path. Your first snapshot shows up within 48 hours.
2. Name your environments
Call them what you call them — production, staging, dev, preview. It's one account, but you can compare across them, so a pile of dev eval spend doesn't quietly inflate the production number.
3. Start looking around
Open the explorer and slice the spend however you want. Check the forecast for the next week or the next year, set a few budgets, and read the first recommendations — each one already checked for quality.