The 9th DIMACS Implementation Challenge from 8 years ago (see http://www.dis.uniroma1.it/challenge9/ ) served as a way to gather the best algorithms for the shortest path problem. I don't see how this project can be significantly better, or even close to, a traditional effort like that.
It doesn't have to be better at producing good algorithms. It just has to be better at making those algorithms implementations easy to use.
But I still don't think the business model is viable.
The Boost Graph Library includes three different shortest path implementations, and I have that already downloaded. That library likely fits my needs.
In the DIMACS challenge papers at http://www.dis.uniroma1.it/challenge9/papers.shtml you'll see "Single-Source Shortest Paths with the Parallel Boost Graph Library".
Indeed, the Parallel Boost Graph Library library is easily available, and contains two parallel shortest-path implementations.
The flip question is, what if it doesn't fit my needs? Well, then the question is "what do I need"?
The other DIMACS challenges include 1) pre-computation, which is useful for road-based graphs, 2) supercomputer parallelism to search billions of nodes, 3) out-of-core graph searches, for when you don't have enough RAM, and 4) k-shortest path search.
It's very unlikely that this proposed algorithm service provider will implement these alternate algorithms.
The first language we implemented was Java/Scala, largely because of the excellent libraries like Mahout and Weka easily available in Maven Central. But even though the code is there in maven, it is still not trivial to deploy those algorithms as a service. We are trying to make that easier, while at the same time supporting the developers of those libraries in the process.
Being able to run the code locally is in the plans. For the most part, the algorithms in our system are already simply maven packages, or ruby gems, npm packages, etc. Being able to distribute them in binary form is definitely possible. We're still figuring things out right now, but our focus is always on doing right by the algorithm development community.