Bayesian uses a broader prior -- a distribution over possible prior distributions of facts, instead of assuming a single possible prior (Gaussian). In that sense you could sort of squint and say that that's the contrast.
Technically, you're allowed to use whatever prior you like. A beautiful thing about Bayesian inference is that it tells you how to update your prior, without making any assumptions about what it may be.
Frequentist approaches do not use any prior at all. Indeed some of the issues that frequentists wind up getting genuinely concerned about, such as repeated significance testing errors, are clear logical fallacies if you're doing any sort of Bayesian analysis.
