Why use Variational Autoencoders VAE instead of Autoencoders AE in Anomaly Detection?
Variational autoencoders encourage the model to generalize features and reconstruct images as an aggregation of those features.  This is what the latent space encodes, a compressed feature vector.

Vanilla autoencoders memorize the input and map to the output without the generalization.  If you want to extrapolate from your dataset, variational is the way to go.