How are teams managing OpenAI costs once usage starts scaling?

We’re using Ope͏nAI across a few product and internal workflows now, mainly support automation, summaries, enrichment, and some customer-facing AI features. The tricky part is that the monthly bill is becoming less useful on its own. We can see total spend, but it’s harder to understand which feature is driving it, which team owns the usage, whether certain customers are creating bad margins, and whether we’re sending too many tasks to expensive models when a cheaper model would probably be fine. We can build some of this ourselves with logs, tags, and dashboards, but I’m wondering how far that approach actually goes once usage grows. I came across Fin͏out’s OpenAI integration, which seems focused on OpenAI cost allocation, anomaly detection, forecasting, and connecting AI spend back to teams, projects, features, and departments. That sounds close to the problem we’re running into, but I’d rather hear from people who have actually dealt with it in production. Has anyone here used Finout or a similar Fin͏Ops tool for OpenAI cost management? And if not, what are you doing instead to reduce token waste, avoid bill shock, and understand AI costs by product feature or customer?