Not the grandparent, but I have a lot of experience with DynamoDB as well.  I migrated a self-managed, sharded redis store to DDB close to a year ago, and what it really depends on are your read and write patterns, and whether or not you can live with the opacity of what DDB is doing behind the scenes.<p>An example: if you provision X capacity units, you&#x27;re actually provisioning X&#x2F;N capacity units per partition.  AWS is mostly transparent (via documentation) about when a table splits into a new partition (I say &quot;mostly&quot; because I was told by AWS that the numbers in the docs aren&#x27;t quite right), but you&#x27;ll have no idea how the keys are hashed between partitions, and you won&#x27;t know if you have a hot partition that&#x27;s getting slammed because your keys aren&#x27;t well-distributed.  Well, no, I take that back -- you will know, because you&#x27;ll get throttled even though you&#x27;re not consuming anywhere near the capacity you&#x27;ve provisioned.  You just won&#x27;t know how to fix it without a lot of trial and error (which isn&#x27;t great in a production system).  If your r&#x2F;w usage isn&#x27;t consistent, you&#x27;ll either have periods of throttling, or you&#x27;ll have to over-provision and waste money.  There&#x27;s no auto-scaling like you can do with EC2.<p>Not trying to knock the product: still using DDB... but getting it to a point where I felt reasonably confident about it took way longer than managing my own data store... and then they had a 6-hour outage one early, early Sunday morning a couple months ago.  Possibly solution: dual-writes to two AWS regions at once and the ability to auto-pivot reads to the backup region.  Which of course doubles provisioning costs.<p>Ok, maybe I&#x27;m knocking it a little.  It&#x27;s a good product, but there are definitely tradeoffs.