1. common algorithms (sorting, graph algorithms, etc) - are commodity and there are tons of free libraries that have good enough implementations.
2. generic data dependent algorithms with many parameters (classification, NLP, neural networks, etc) - many many parameters to tune and they need to be tuned for each type of dataset. Unless you understand the algorithm, there is no real value in playing around with parameters. But if you understand algorithm then you probably want to implement or modify it to your specific needs (speed, complexity, etc). Only people who just want to play around with data might want to try off the shelve stuff to see how it performs but I don't see much market for serious users. I might be wrong here though.
3. Very specialized algorithms (3D surface reconstruction out of points, antenna analysis, etc) - this includes above algorithm types(common and generic data driven) that were specifically tuned to specific purpose. Some companies sell libraries that do that and those companies would probably benefit from Algorithmia.