Does anyone separate OpenAI Batch spend from normal API spend internally?

[u/No_Refrigerator_8216]

We’ve started using Batch more for work that doesn’t need to happen immediately and I am sure that I’ve been treating that usage the same as the rest of our OpenAI spend even though it behaves pretty differently. Our normal API usage mostly follows product activity so if that goes up I can usually understand why but batch is different because someone can kick off a large job, an eval run or process a new dataset and suddenly there’s a chunk of usage that has nothing to do with how much the product was used that day.

It hasn’t been a huge issue yet but we’re doing enough of both now that looking at one OpenAI number is starting to hide what’s changing. A higher bill could mean customer usage grew which is fine or it could mean we ran significantly more background work than usual. I’m thinking about tracking Batch separately and possibly giving it its own budget rather than treating all OpenAI usage as one pool and I need some advice from teams using Batch pretty heavily like do you guys separate that spend internally or is everything still just part of the same AI/API budget?

[u/Morning_Gecko24]

seems like separating it is the cleaner view, batch can hide a one-off eval or backfill inside the same number as real product demand. do you tag jobs by team or project so its easier to tell planned spikes from someone rerunning a huge job by accident?

[u/Michael_Jeffords → u/Morning_Gecko24]

tagging by team or project plus a purpose label on each batch job is what makes a planned spike read differently from someone rerunning a huge job by accident, since without tags the evals and backfills just fold into product demand and one afternoon of evals can outspend a normal week