When comparing Cassandra vs Cloudant, the Slant community recommends Cloudant for most people. In the question“What are the best databases for building social network like apps?” Cloudant is ranked 6th while Cassandra is ranked 10th. The most important reason people chose Cloudant is:
You can choose to host your database on a single cloud provider or you can replicate it over several different providers.
Ranked in these QuestionsQuestion Ranking
Pros
Pro Highly scalable
Cassandra is very scalable and achieves the highest throughput for the maximum number of nodes compared to other alternatives. Unfortunately this also brings rather high write and read latencies.
Pro Familiar to developers used to SQL
The query language that Cassandra uses (CQL) is similar to SQL even though it's a NoSQL database.
Pro Can replicate the database across several hosts
You can choose to host your database on a single cloud provider or you can replicate it over several different providers.
Pro Runs on both bare-metal and virtual machine
Users can choose whether their database instance will run on bare-metal or a virtual machine
Pro Crash friendly
The database behind Cloudant, CouchDB uses an append-only file for it's data. To restore already used up space, a compaction must happen. When this happens is up to the database maintainer.
Pro Cloud agnostic
Cloudant hosts databases with a lot of different cloud hosting providers including Amazon, Rackspace, SoftLayer and Microsoft Azure. This way customers can choose where their database is hosted.
Cons
Con Not for newbies
If your dataset is in order of gigabytes then maybe consider a toy database, not a serious one like Cassandra.
Con No JOINS
Cassandra has no support for JOINS.
Con Can only achieve consistency through replication and verification
Since CouchDB is considered an AP (Available, Partition-Tolerant database management system), it is not really consistent (not all clients can have the same view of the data consistently) and the only way to achieve some "eventual consistency" is through replication and verification of data.