Google’s TurboQuant compresses LLM KV caches and embedding vectors to 3-4 bits with no training and no codebook, and still beats methods that need both. The clever part is not the quantizer, it is what it refuses to store. Here is the intuition, with runnable NumPy you can paste and check.
In a single-tenant RAG you trust the index. In a multi-tenant one, a shared index plus one missing filter leaks Tenant A’s documents into Tenant B’s answer. Here is the unified mental model that ties the metadata-filtering mechanics and the authorization layer together, treat the tenant filter as a policy decision, not app code, with the same architecture mapped across AWS, Azure, and Google Cloud (diagrams and Python included).