If you have some data and want to train or run a small custom model but don't have powerful enough hardware for training, fine-tuning services can be a good solution. Once training (requiring more resources than inference) is done, the custom model can then run locally. For larger models, there is also (for some providers) the option to run inference with the custom model using their services.
To get a better overview of the currently existing landscape, I did some benchmarking and experiments on cost, speed and user experience. The space is moving quickly, with new providers arriving even while I was testing, so what’s “best” really depends on your use case. For function-calling specifically, Nebius had some useful capabilities that made iteration more efficient.
Full write-up with details, methodology, and comparisons here: https://vintagedata.org/blog/posts/fine-tuning-as-service
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