Our own Alteryx Gallery is running in AWS, configured in a multi-node deployment spread across several instances.
We certainly recommend following the tech spec guidelines, which does indeed place you in the m4.xlarge range, but your experience/needs may vary. Alteryx Server will still function on lower hardware specs (such as a c3.large as you've referenced), but there may be a degredation in performance depending on the types of workflows and load that you expect to run.
The workflow type (data being consumed, tools being used, etc.) will dictate if the workflow leans more towards being compute-intensive, RAM-intensive, HD-intensive, network-intensize, or a combination of the 4. For example, if you're doing a lot of drivetime processing, that's typically more compute-intensive, wheras a lot of sorting and joining will be more memory intensive. Large data reads or writes to a local datasource may find the disk being the bottleneck, and reading/writing across a network, or consuming web services, will often show the network being the slowest point.
