Together AI open-sourced a recipe to fine-tune a Jev-style classifier for about $17 in 25 minutes.

together/Tev1-4B-experimental trains on 37,840 examples from eight sources: MultiNLI, BoolQ, Banking77, AG News, SST-5, 13,500 programmatic policy examples, 6,000 routing examples, 3,840 research-taxonomy examples (Together AI). No accuracy comparison against hosted Jev in the post, so it's a cost-of-entry data point rather than a quality claim. Alex Molas separately argues the category has a calibration problem: a model returning the same probability for everyone can be calibrated for one company's data distribution and badly off for another's, and he cites Jev giving 0.92 for a fair coin landing heads (alexmolas.com). His recommendation is to treat the scores as rankings and recalibrate on a few hundred of your own labeled examples. That's the right instruction regardless of which vendor you pick.