I’ve been watching how companies procure AI infrastructure lately, and it’s honestly a bit of a train wreck. Most procurement teams and CFOs are making decisions based on one single metric: $/GPU/hour.

The problem? The sticker price on a cloud pricing sheet is almost never the real cost.

I’m considering offering a specialized TCO (Total Cost of Ownership) Consulting Service for AI compute, and I want to see if there’s a real market for it. Based on my experience and some recent industry data, here is why a “cheap” cluster can end up costing $500k+ more than a “premium” one:

1. The “Performance-Adjusted” Trap (MFU & TFLOPS)

Most people assume a H100 is a H100 regardless of the provider. It’s not.

• The MFU Gap: Industry average Model FLOPs Utilization (MFU) is around 35-45%. A “true” AI cloud can push this significantly higher.
• The Math: If Provider A has 20% higher delivered TFLOPS than Provider B at the same hourly rate, Provider B would have to cut their price by ~20% just to match the value.
• Real-World Impact: In a 30B parameter model training scenario (1,000 GPUs), higher efficiency can save you thousands of dollars and hours of time on a single run.

2. The “Hidden” Support Infrastructure

This is where the CFOs get blindsided. They approve the GPU budget but forget the plumbing.

• Egress & Storage: Moving 20PB of data on a legacy hyperscaler can cost between $250k and $500k in hidden fees (write/read requests, data retrieval, and egress).
• Networking at Scale: If the network isn’t purpose-built for AI, you hit bottlenecks that leave your expensive GPUs sitting idle.
• Operational Drag: If your team spends a week just setting up the cluster instead of running workloads on “Day 1,” you’ve already lost the ROI battle.

3. The Intangibles (Speed to Market)

In AI, being first is a competitive advantage.

• Reliability = fewer interruptions.
• Better tooling = higher researcher productivity.
• Faster training = shorter development cycles.

My Pitch: I want to help companies stop looking at “sticker prices” and start looking at “Performance-Adjusted Cost.” I’d provide a full report comparing vendors (CoreWeave, Lambda, AWS, GCP, etc.) specifically for their workload, covering everything from MFU expectations to hidden data movement fees.

My questions for the community:

1. Is your procurement team actually looking at MFU/Goodput, or just the hourly rate?
2. Have you ever been burned by “hidden” egress/storage fees after signing a contract?
3. Would you (or your boss) pay for a third-party audit/report to save 20-30% on a multi-million dollar compute buy?

Curious to hear your thoughts.