Early AI infrastructure bills are often dominated by egress, storage, orchestration overhead and idle capacity—not GPU hours alone.
The bill tells a story
Teams often budget for GPUs, then discover:
- Cross-AZ and egress charges from oversized object storage paths
- Checkpoint and dataset retention without lifecycle rules
- Idle clusters left running after experiments
A practical audit order
- Measure data movement and duplication (what crosses regions?)
- Rightsize non-GPU dependencies (queues, metadata stores, logging)
- Tie autoscaling signals to queue depth and latency, not averages alone
Governance that scales with AI
Tag workloads by project, environment and cost owner. Finance should not be the first detector of runaway spend.
Takeaway
Optimize the path to inference, not just the GPU SKU. The cheapest win is often deleting redundant copies of data and enforcing retention.
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