For the most part, if you are writing for the RDBMS/SQLish market, the number one question you should probably ask is, "What do I already know about? What does my staff know about?" If you have an answer for that, then you should probably pick that SQL engine first. My inner database geek cringes at this answer, but the truth is that unless your developers are among the tiny fraction that really get relational databases anyway, you're going to use the same deep ruts of standard database mistakes as everyone else, and the main question is going to be whether you can get your system to go fast enough.
This is probably true if you've swallowed a pitcher of the NoSQL beverage of choice as well, since there too you have to pick the thing you understand.
If you are already in a position to understand all these differences, however, then you will understand that the answer is "it depends". The usual four dimensions come down to these: execution speed for a given workload profile (this is a matter of whether the database is excellent at the particular kind of problem: some are faster for lookup, for instance, where others are better under high concurrency writing); SQL conformance in the target areas (e.g. Oracle has funny -- i.e. wrong -- NULL handling, MySQL is all over the map, Postgres wraps unquoted identifiers to lower case); money cost both immediately and over the long haul (include hardware requirements, costs of hiring people, licenses); and maybe features you want (if you want Oracle's RAC, you have to buy Oracle).