Yes, this is what I am trying to say. It looks as if the use-cases, if not the market-size itself, is a lot less ambitious if you choose to use these services.
But also - what happens when you wish to expand and grow? My guess is, if this capability was developed in house, you would almost certainly find it easier to not just scale without incurring a "success tax", but most importantly you are now exposed to the deep bowels of the ML process which should certainly give you far greater insight into potential tangentially related services.
An example: you use a natural language processing API and get all the brand mentions in some text the customer has submitted - certainly a useful service for competitive research. As your company scales, now you are more or less beholden to the ML service - if nothing else at least for the consistency of results, which is likely to make you super cautious about rolling your own. But you don't know how the results (brand mentions) were actually inferred. If you were to ask, "Hmm.. what other types of entities can I extract and have it be more useful for our customers? What about only getting brand mentions if it is in the same paragraph as a complaint?" - you might face some options which you might not really like - for one, the way the ML API is structured might actually make this operation expensive (after all, the data is pretty adjacent before being sent to the ML API, but not necessarily so once it is processed). Also, since you never understood how the brand mentions were extracted, it is very likely you are missing out on a ton of insight you could gain by rolling out your own capabilities.
And not to mention: if the ML capability has been outsourced, and if ML is indeed going to drastically impact your industry, then you would certainly not want to be in the position where your competitor can easily disrupt you merely because they spent the time to develop such capabilities in house.