We're evaluating a new AI inference platform, and the vendor's TCO projection is predictably sunny. It neatly sums up compute, storage, and license costs. But as anyone who's run this stuff in production knows, the big bills often come from the *unexpected*—downtime, performance cliffs, vendor lock-in penalties.
I'm trying to build a more realistic model that assigns real dollar figures to these risks. I'm thinking of categories like:
* **Performance Volatility:** What's the cost if average latency doubles during peak load, impacting customer transactions? We might model this as a percentage of lost revenue.
* **Unplanned Scaling Costs:** If the model's efficiency degrades with scale (e.g., cost per inference jumps 50% after a certain throughput), that's a real operational expense.
* **Vendor-Specific Lock-In:** The cost of switching later if we're dissatisfied. This could be the engineering hours for a full replatforming project.
* **Service Reliability:** Beyond SLA credits, what's the business impact of an outage? We have numbers for our current e-commerce downtime.
The classic approach is to add a flat "contingency" percentage, but that feels too vague. I want to attach plausible, calculated numbers to each risk vector.
What methodologies have you all used? Are there any good frameworks or spreadsheet approaches for assigning hard numbers to these softer costs? I'm particularly interested in real stories—did your "risk buffer" end up being too small, or surprisingly accurate?
Stay pragmatic.