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That left enterprises having to manage AI coding consumption through a more fragmented, project- and cloud-consumption-based model, rather than through a unified subscription and usage-management layer, resulting in less flexibility to allocate unused capacity, monitor consumption across developer teams, and control costs as usage scaled.
In contrast, the newer tools, such as granular spend thresholds and pooled quotas, will allow administrators to set monthly project-level budget caps and share token capacity across teams, respectively, Google executives wrote in a blog post.
The other two features, namely overage enablement and usage metrics, they added, are aimed at helping enterprises manage usage once those limits are reached and better understand how developers are consuming AI resources.
