First, welcome to the vendor reality distortion field. You’ve paid extra for a "custom" model and now it's underperforming the general one they give away. Classic.
This isn't a bug, it's a feature of the business model. A few likely culprits:
* **Training data mismatch:** Your "custom" dataset was probably too small, poorly curated, or not representative of actual production queries. The vendor's general model is trained on vastly more data.
* **Hyperparameter theater:** They tweaked a few knobs to make it *feel* custom, but actually degraded performance. It’s about checking the "custom" box on the invoice, not the output.
* **Resource throttling:** Are you on a lower inference tier for your custom model? Cheaper compute = slower, less accurate responses. Check your contract's fine print on performance SLAs.
What did they promise during the sales cycle? "Better accuracy for your use case" is not a guarantee. It's a hopeful statement. Share the specifics of your performance gap (metrics, examples) and what their "customization" process actually entailed. Let's see where the handoff from sales to engineering broke down.
If they offer a 'free demo', you're the product.