How to choose

Which way to buy inference? Five questions

1. Frontier or not? Classification, extraction, summaries and chat usually don't need it, and a small or open-weight model at a tenth of the price or less is close enough. Hard agentic and coding work still does.
2. Does it need to reason? Reasoning multiplies the bill by three to four on a chat-shaped task. Set the effort per feature, cap max_tokens and measure tokens per task as well as the price per token.
3. Can it wait? If an answer can come back within 24 hours, batch halves the bill. If it must be fast, priority or fast tiers cost 1.75-2 times list, and capacity for them may not be for sale.
4. What does the contract need? Residency (about 10% extra), zero data retention, an uptime SLA with credits and an IP indemnity each narrow the routes. A cloud platform usually adds an SLA with credits, which a lab's standard tier doesn't have; check that the indemnity covers the specific model.
5. When does it go away? Every route except self-hosting has a retirement clock, some as short as 60 days. With three labs in use, expect a forced re-test every few weeks.

My defaults: for the help-desk company I'd start on a mid-tier closed model through the cloud it already has a commitment with, with caching designed in and reasoning off unless an eval shows it helps. I'd put a gateway in front from day one, with per-team budgets, a fallback to another provider, and an open-weight model for the easy half of the traffic once our evals say it's good enough. I'd pin dated snapshots, keep a regression set of a few hundred real tickets that runs on every model change, and track cost per resolved ticket. I wouldn't self-host until volume is ten times higher or a customer requires it.