> this approach is not scalable (that's why the high cost)

It may be opposite, most of the amps follow some classic schematic (e.g. jtm, plexi, princeton) with insignificant changes, so after building digital copies of some limited number of classical amps they can add new one rather fast.

As result, fractal has about 100 high quality models already (average guitarist probably uses 5?).

> the ML approach doesn't require any knowledge

ML approach requires you to capture training data: which is different sound samples with all possible knobs positions (8 knobs per amp in average) + different types of speakers and mics, which is very huge number of variations.

> that's why the high cost

cost is driven by market, ML profiling competitors (kemper, neuraldsp) charge about the same for their devices.