In a hybrid AI setup, RAG data should generally live wherever the source data itself already lives and is subject to the strictest applicable compliance requirement, rather than being centralized purely for convenience, since moving sensitive documents into a vector database in a different environment recreates the same data residency and access control questions as the source system. For enterprises with regulated or highly sensitive documents, an on-premise vector database keeps embeddings and retrieved text within the same trust boundary as the underlying data, while the LLM performing generation can still run in the cloud if the retrieved context passed to it does not itself violate residency requirements. Conversely, less sensitive knowledge base content can live in a managed cloud vector store integrated with Bedrock Knowledge Bases, Azure AI Search, or Vertex AI Search for lower operational overhead. Data gravity matters practically too, since embedding generation and retrieval work best with low latency to the source documents, so co-locating the vector store near wherever documents are updated most frequently reduces sync lag. Nanobase AI designs hybrid RAG architectures that place vector data according to actual sensitivity and update patterns rather than default cloud convenience.
RAG is four components, not one decision
Deciding where RAG data lives in a hybrid setup gets easier once the pipeline is broken into its separate components instead of treated as a single placement decision. A hybrid RAG architecture places embedding generation, the vector store, the retrieval service, and the generation model independently, based on each component's own sensitivity and latency needs, rather than moving the entire pipeline to whichever environment feels more convenient. Some organizations end up with all four components in the same place; many end up split, and that split is not a compromise, it is usually the correct design.
Component-by-component placement guide
| RAG component | On-prem fits when | Cloud fits when |
|---|---|---|
| Embedding generation | Source documents are highly sensitive or under strict residency rules | Documents are less sensitive, or a managed embedding API's convenience outweighs the transfer |
| Vector store | Embeddings themselves reveal sensitive content and must stay in the source trust boundary | Content is less sensitive; a managed store like Bedrock Knowledge Bases or Azure AI Search reduces operational load |
| Retrieval service | Needs to sit close to the vector store to minimize latency | Same principle; co-locate with wherever the vector store lives |
| Generation LLM | Retrieved context itself is sensitive enough to restrict which environment can process it | Retrieved context is not restricted by residency once retrieved |
The generation model is often the most flexible of the four, since it can run in the cloud even when the vector store and retrieval sit on-prem, as long as whatever context gets passed into the prompt does not itself violate a residency requirement. This is a meaningfully different question from whether the source documents can leave the on-prem environment at all.
Why data gravity matters as much as data sensitivity
Embedding generation and retrieval work best with low latency to the source documents they operate on, so co-locating the vector store near wherever documents are updated most frequently reduces sync lag between a source system and its searchable index. A vector store in a different environment from its source documents recreates a data pipeline problem: someone has to keep embeddings current as source content changes, and that sync job becomes another thing to secure and monitor. For source systems that update constantly, such as a live document management system, this pull toward co-location is often stronger than the pull toward centralizing everything in one place for convenience.
A practical placement process
- Classify source documents by sensitivity and applicable residency requirement before deciding anything about the pipeline.
- Place embedding generation and the vector store in the same trust boundary as the most sensitive source documents that feed them.
- Decide separately whether the generation LLM can run in the cloud based only on what gets passed into its prompt, not on where the source documents live.
- Revisit the split whenever a new document source is added, since a single sensitive collection can change the right answer for the whole pipeline.
None of these four steps require picking a single environment for the whole pipeline up front, which is precisely why treating RAG as one placement decision produces a worse architecture than treating it as four.
Frequently asked questions
Can the vector store be in the cloud while the generation LLM runs on-prem?
Yes, this split is workable when the vector store's content is not sensitive enough to require on-prem hosting but there is still a reason, such as latency or existing infrastructure, to keep the generation model on-prem; the two components can be placed independently based on their own constraints.
Does using a managed vector store like Bedrock Knowledge Bases create a data residency problem?
It can, if the underlying source documents are sensitive, since a managed cloud vector store moves those documents' embeddings into a different environment than the source system, recreating the same residency question the source system already had to answer earlier.
How often does the vector store need to sync with source documents?
This depends entirely on how frequently source documents change and how stale an answer the application can tolerate; systems with fast-moving source content need near-real-time sync, while stable reference material can sync on a much slower schedule without noticeable impact.
Is it more expensive to run RAG across a hybrid split than fully in one environment?
Usually somewhat, since a split introduces sync infrastructure and cross-environment networking that a single-environment setup avoids, but this cost is typically smaller than the compliance or latency cost of forcing sensitive data into an environment that does not fit its requirements.
How Nanobase AI helps
Nanobase AI designs hybrid RAG architectures that place vector data, embedding generation, and the generation model according to actual sensitivity and update patterns rather than default cloud convenience. This work builds on the RAG versus fine-tuning decision guide and connects to keeping data in the EU using cloud AI services for teams with regional residency requirements.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.