Well phrased question and thinking.
Some researchers (Chris Chapman formerly of Microsoft and now at Google) is well experienced with using CBC studies to guide business decisions. He is fond of saying that he sometimes learns enough from 20 to 40 well sampled respondents to make many key business decisions, if the differences shown in the conjoint study (market simulator) are large enough to easily surpass some go/no go threshold.
But, if the business decisions are more complex and involve a more subtle discovery of differences in the conjoint results that require larger sample sizes to detect, then certainly larger sample sizes are needed.
MBC software would also treat your problem as a series of separate binary logits (estimated either via aggregate logit or HB). If you feel you can manage those models on your own and link them together to build your simulator, then there really isn't a need to buy the software. That's what the MBC software does (though it does go beyond that to do some nice combinatorial selections simulations, which are harder to pull off on your own).
You have to decide how much comfort you have with 200-300 representations of an item in the design across respondents. You can generate some dummy data, and run aggregate logit, and get some feel for the margins of error on the parameters of interest. The models will work, the worry is just how much precision you require.