A crucial factor tech industry folks tend to ignore is how much executives value predictable costs. Cloud migrations got away with this, but still had to argue fiercely, because 'the cloud' and its serverless tech had the potential to significantly decrease overall spend for unpredictable, bursty workloads.

The usual counter-argument is the operational burden, but human capital is also a relatively fixed cost. A dedicated team of 3-5 FTEs could probably handle inference ops for a F500 company.

Meanwhile, the capability delta is shrinking fast. We have more evidence that local open-source is viable with the release of DeepSeek v4, and the industry is only trending further in this direction. Especially as we rely more on test-time compute and task-specific harnesses rather than model size.

So, if you're an executive looking at a marginal but fixed operations cost, added flexibility, and a rapidly closing gap in capability, why wouldn't you just run open-source models on your own infrastructure to get those highly predictable costs? Plus, you decrease the risk of one of the frontier