AI FinOps shifts from dashboards to policy enforcement, spend attribution, and governance control points

Onaro and Stacklet Turn AI Spend Governance Into Enforceable Policy

Onaro’s Meridian and Stacklet’s AI FinOps benchmark standard push AI cost management from visibility into enforceable control. Meridian launches as a read-only AI cost layer that claims to track “every token, every agent, and every dollar,” attributing spend from model providers, LLM gateways, and platform billing to an owner, workflow, and business unit. It separates direct AI spend—model inference, training, fine-tuning, vector databases, GPU usage, and third-party AI platforms—from indirect costs such as cloud overages, duplicated experimentation, shadow subscriptions, and monitoring or remediation labor, while also surfacing budget variance, unattributed costs, cost drift, concentration risk, and chargeback exports for GL and ROI reporting.

Stacklet adds the policy layer with a benchmark standard for AWS, Google Cloud, and Microsoft Azure that defines “good” AI cost governance across GPU usage, foundation and custom models, storage, token thresholds, idle endpoints, stalled training jobs, unapproved models, and Terraform/IaC checks before deployment. Together, the two moves extend the FinOps shift from product-level metering into a finance-grade AI control plane with standardized measurement and remediation. That increases pressure on standalone dashboards and strengthens the case for infrastructure management layers that can both attribute spend and enforce policy before AI costs turn into margin leakage.