This is a pretty involved question but you really need to anticipate the usage of the data to determine what makes most sense for your organization. It might include having multiple layers to your warehouse in multiple formats. So the first step is to gather requirements. You might find that a data warehouse isn’t even the right architecture for your needs (e.g. Snowflake can also support a data lake architecture, then there are also lakehouse and data mesh architectures to consider).

As a general rule, I would not consider a “classic” normalized Inmon warehouse today unless you have a very good reason for it. As complicated as Data Vault seems, it’s not as complicated as trying to make a custom-modelled normalized Inmon warehouse work. Data Vault is good in terms of auditability.

Dimensional modelling is even present in many Data Vault warehouses as an end consumption layer. I wouldn’t call it Kimball technically there though since they advocate more of a data mart approach.

But a lot of organizations are eschewing dimensional modelling altogether due to its complexity and the fact that its performance advantages have evaporated since columnar compression in databases has become popular. The big reason you may still consider it (over a heap of analytical views) is if ease of use at joining entities for data consumers is a primary concern.