Last updated · Fusionative
Short answer
FinOps for AI applies cloud financial management to AI infrastructure: it measures who uses which GPUs, models and tokens, attributes the cost to teams or customers through showback or chargeback, and drives decisions that raise utilization and lower cost per training run or per token.
What to meter
- GPU-hours by GPU type, partition and tenant.
- Tokens or requests per model and per tenant for inference.
- Storage, network egress and supporting CPU and memory.
- Idle and reserved-but-unused capacity, the most expensive line in most AI estates.
Showback vs chargeback
Showback reports what each team used and what it cost, without moving money. Chargeback bills that cost back to the team's budget or, for a provider, to the customer's invoice. Most organizations start with showback to build trust in the numbers, then move to chargeback.
Turning metering into decisions
- Right-size: move small inference workloads to partitions or smaller GPUs.
- Schedule: queue batch work into idle hours and reclaim idle reservations.
- Price: set internal or external price books that reflect real cost and demand.
Where FusioNative fits
FusioNative meters CPU, memory, storage, GPU-hours by GPU model, VMs and egress per tenant, applies rate cards with GPU model pricing and commitment plans, generates monthly invoices, and adds budgets, anomaly detection and a provider revenue console.