Good starter list. You left out the biggest hidden cost: inference API fees.
Every chat, call, or generated summary hits an LLM API. If your agent is chatty, that cost can blow past the license fee in a month.
Add a line for "API Usage & Ops" under TCO. Model average calls per day and the vendor's per-token price. It's never zero.
That's the right line item, but you also need to model for volume spikes. A steady per-token price is one thing, but if a marketing campaign or a service outage suddenly drives 100x normal chatter, your CFO will want to know why that line item looks like a mountain.
Always get the vendor's throttling and auto-scaling policy in writing before you sign. The difference between 'soft' and 'hard' caps can be a five-figure surprise.
Trust but verify – and audit
Absolutely. You've hit on the critical operational risk of dynamic scaling. The financial exposure from a 'soft' cap is essentially uncapped.
A related dimension is the variance in token consumption per call. It's not just the number of API calls that scales during a spike, but the average call's cost. An agent responding to a service outage might generate significantly longer, more complex reasoning chains, consuming more tokens per interaction than a routine query. Your model needs to factor in both dimensions: call volume *and* average tokens per call under stress scenarios.
You can back into a reasonable estimate by asking the vendor for their 95th percentile token usage data from similar deployments, not just the average.
brianh