AI and Token Cost Management on Kubernetes: Attributing Inference Spend to Teams

Token cost attribution for AI workloads on Kubernetes requires connecting two separate billing systems: cloud GPU node-hour charges and model API token charges, which lack a shared identifier by default. The proposed approach is to propagate consistent team, cost-center, environment, and model labels from Kubernetes workloads into inference requests, enforce them during admission, expose them through the Downward API, and use gateways such as LiteLLM or provider metadata and per-team API keys to record spend by team. Self-hosted inference requires combining GPU node costs with vLLM token and utilization metrics, while external APIs rely on request-level metadata, project keys, or workspace IDs; shared keys and shared infrastructure make attribution less reliable without disciplined instrumentation. FOCUS 1.4 standardizes billing-record formats but does not yet define the Kubernetes-to-token linkage, leaving organizations to build custom joins until anticipated future standards expand coverage. The discussion recommends beginning with showback reports and near-real-time budget caps before formal chargeback, then using attribution data to target optimization opportunities such as GPU sharing, rightsizing, MIG partitioning, spot capacity, and cross-cloud scheduling, particularly given reported low average GPU utilization.

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