People often use PCA (or a similar dimensionality reduction that compresses the information of the variable genes into lower dimensions) to explore how the samples behave and whether distances between replicates of the same group are similar across all groups. If similar that would argue for keeping all data together into a single analysis, which also makes downstream analysis somewhat more feasable as you only would have a single DESeqDataSet to deal with. If on the other hand some groups have notably larger spread that others then this might somewhat affect the results of individual comparisons where dispersion is actually lower than estimated for the total dataset. DESeq2 has a plotPCA function for this, or the Bioconductor PCAtools which is way more versatile. PCA also helps identifying potential outliers due to technical issues or batch effects that might need to be addressed. It (imho) should always be a standard step in the initial QC/data exploration.