Depends on the number of unknowns, sparsity of the matrix, whether you want 3 correct digits or 16, etc.
Direct methods as Gaussian elimination with pivoting are proven to be stable. Iterative methods are not but can be a lot cheaper in computational costs. Also they can stop when a certain relative residual is reached, unlike direct methods.
If iterative methods like Krylov subspace methods where stable, then they could actually be seen as direct methods themselves, as the Krylov subspace has at most a dimension of N, where N is the number of unknowns; so after at most N iterations the solution could be extracted exactly from the search space. In practice this is not the case due to rounding errors.
Direct methods as Gaussian elimination with pivoting are proven to be stable. Iterative methods are not but can be a lot cheaper in computational costs. Also they can stop when a certain relative residual is reached, unlike direct methods.
If iterative methods like Krylov subspace methods where stable, then they could actually be seen as direct methods themselves, as the Krylov subspace has at most a dimension of N, where N is the number of unknowns; so after at most N iterations the solution could be extracted exactly from the search space. In practice this is not the case due to rounding errors.