* Veri works as a cluster that can hold a Vector Space with fixed dimension and allows easy querying of k nearest neighbour search queries and also querying a sample space to be used in a machine learning algorithm.

* Veri keeps the average (Center) and a histogram of distribution of data with the distance to the center (Euclidean Distance). Every instance continue, exchanging data as long as their average and histogram are not close enough.

* Veri has a different way of approaching high availability. Veri as a cluster try to use all the memory it is allowed to use. If there is enough memory, all the data is replicated to every instance. If there is not enough memory, data is split within instances using histogram balancing. If memory is nearly full, Veri will reject insertion requests. So if you want more high availability, use more instances.