The gap between fine-tuning API and weights-available is much more significant than you give it credit for.

You can take the weights and train LoRAs (which is close to fine-tuning), but you can also build custom adapters on top (classification heads). You can mix models from different fine-tunes or perform model surgery (adding additional layers, attention heads, MoE).

You can perform model decomposition and amplify some of its characteristics. You can also train multi-modal adapters for the model. Prompt tuning requires weights as well.

I would even say that having the model is more potent in the hands of individual users than having the dataset.