Most significant is the network speed. Small instances share a machine with other small instances so they can get more than their "fair share" of network capacity. So because of this the environment maybe effectively heterogeneous if some of the nodes are relatively starved for I/O because they are on a busy machine. Storing all the data on S3 makes this particularly relevant.
Since one of the defining features the the MapReduce model (as detailed by the 2004 Google paper: http://labs.google.com/papers/mapreduce.html) is that "moving computation is cheaper than moving data" the implementation of S3 becomes salient as well, where exactly is the data?
In a normal implementation of Hadoop one would run HDFS, which stores the data on the same machines that run the job. The scheduler attempts to minimize transfer AND transfer is still often the bottleneck when the data is shuffled for the reduce job. So interesting to see how this service compares.
EDIT: I haven't tried the new service though, but I will for sure after I get some sleep.