A vector store is one retrieval component—not the full data layer an AI application needs. A practical architecture connects source systems to ingestion and preparation, stores durable records and derived indexes, retrieves evidence using the right mix of search and query methods, enforces access rules, and serves traceable results to the application. The right arrangement depends on the workload; no single storage pattern fits every AI application.
What belongs in an AI application data layer?
Think of the data layer as the path from information an organization owns to evidence an application is allowed to use. It includes the pipelines that prepare data, the stores that preserve source records and derived representations, the retrieval logic that finds relevant material, and the controls and interfaces that govern how applications access it.
A vector index holds or indexes embeddings so a system can find content that is semantically similar to a query. It does not, by itself, replace durable source records, business data, authorization, or the operations that keep indexes current. Microsoft’s Fabric SQL guidance, Use SQL database in AI applications, illustrates combining vector matches with structured columns such as product category. AWS Prescriptive Guidance’s Data Layer describes a different pattern, keeping source documents and graph relationships alongside vector-search data.
A common retrieval-augmented generation (RAG) flow is: ingest source data, parse and prepare it, divide content into chunks where appropriate, generate embeddings, index derived data, retrieve evidence for a query, assemble context, and pass that context to a generative model. Google Cloud’s Architecture Center separates ingestion and serving in its Generative AI with RAG reference architectures. That flow is a useful map, not a requirement to use a particular vendor or set of services.
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How does data move from source to an answer?
1. Connect to the systems that own the data
Start with the authoritative sources: application databases, document repositories, object or file storage, event streams, and external catalogs. Track each item’s source identity and timestamps, and classify content so later stages can apply the right handling and access rules. Ingestion should also account for validation, retries, and asynchronous work when extraction or parsing takes time.
For example, AWS describes object-storage events feeding an asynchronous queue to trigger processing; Google Cloud shows an object upload triggering a message and processing function. These are implementation examples, not mandatory components. Choose event-driven, scheduled, or other ingestion according to how quickly changes must reach the application and how the source system exposes them.
2. Prepare content and create derived data
Preparation may include extraction, normalization, chunking, metadata enrichment, and entity or relationship extraction. Embeddings and indexes are derived representations, not substitutes for the source. Retain enough source and version information to rebuild them when source content, parsing rules, embedding models, or indexing strategies change.
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AWS’s example describes normalized chunks, extraction metadata, concept graphs, and links back to source chunks. Google Cloud’s RAG architectures describe chunking and embedding content before creating an index. Neither establishes one universally best chunk size or embedding model. Treat those as design choices and evaluate them against the application’s actual retrieval tasks.
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Keep durable source records in the systems or storage designed to own them. Add or connect retrieval structures according to the query needs: vectors for semantic similarity, relational data for structured constraints and joins, lexical search for exact terms, and graphs for connected relationships. A lakehouse or federated approach can provide governed access where data remains distributed across environments.
These structures can coexist. A database that combines operational records and vector capabilities may simplify joins or reduce synchronization for some workloads. A dedicated vector service may suit workloads centered on large-scale similarity search. The cited vendor architectures describe options, but do not establish neutral cost, latency, or corpus-size thresholds for choosing among them.
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4. Retrieve evidence within the caller’s permitted scope
At query time, interpret what the request requires: semantic matching, exact terms, structured filtering, joins, relationship traversal, or a combination. Apply tenant, role, sensitivity, and other relevant policy filters as part of retrieval rather than relying only on controls applied when data was first indexed. Return provenance with each result so the application can identify the source record or document behind a chunk or fact.
AWS’s graph example links extracted entities to document chunks and source documents. Its two-tier approach distinguishes a comprehensive layer containing candidate extractions from a curated semantic layer containing validated knowledge that can be traced to evidence. This is a useful design when facts need review before they are used in reasoning.
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The application should receive the retrieved evidence it is permitted to use, with source information and relevant metadata, rather than broad credentials to underlying databases. Make the retrieval interface explicit about query intent, filters, permitted data scope, and evidence returned. If an agent can call tools or query data, mediate access through governed interfaces. Google Cloud’s lakehouse example describes a governed data agent and an MCP interface; those are specific components in that architecture, while controlled access is the general design principle.
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Which retrieval primitive should a query use?
Choose retrieval methods from the question the application must answer, not from the assumption that every request is a vector-search problem.
| Primitive | Useful when the query needs | Design consideration |
|---|---|---|
| Relational query | Business records, exact structured constraints, joins, or transactional context. | SQL predicates can constrain vector results using relational attributes. Microsoft’s Fabric SQL guidance documents this combined pattern. |
| Vector similarity | Semantically related passages or content that may use different wording from the query. | Embeddings and indexes are derived data; preserve the source and the information needed to rebuild them. |
| Lexical or full-text search | Exact names, identifiers, phrases, or keyword matches. | Evaluate it where exact matching matters. The cited architecture guidance does not provide a provider-neutral lexical-search benchmark. |
| Graph traversal | Connected entities, relationships, or multi-hop context. | AWS documents combining vector matching with graph traversal; graph structure can add context that similarity alone may not return. |
| Lakehouse or federated access | Governed analysis across distributed operational and analytical data. | Google Cloud’s Build a borderless open data lakehouse describes open formats and federation to analyze data in place. |
For instance, a request for “similar products in this category” may need semantic retrieval constrained by a structured category value. A question about a specific identifier calls for exact matching rather than relying on semantic similarity alone. A question about how two entities are connected may require graph traversal after an initial semantic match. These methods are complementary; the application’s query mix determines whether to combine them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you choose a storage arrangement?
Compare architecture patterns against the same representative workload. These are options documented by the cited providers, not a vendor ranking or a neutral head-to-head benchmark.
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| Pattern | What it brings together | Questions to test |
|---|---|---|
| Vector capability alongside operational data | Similarity search and structured records in a database context; Microsoft’s Fabric SQL example combines vector operations with SQL filtering. | Do the required joins and filters work for the real query mix? How will derived vectors be updated alongside source changes? |
| Dedicated managed vector search | A service focused on large-scale similarity matching; Google Cloud documents this as one RAG architecture option. | What synchronization, update, security, and operational work is required between the source systems and the search service? |
| Graph-enhanced retrieval | Vector matching plus connected entities and relationships; AWS documents a semantic and GraphRAG data-layer pattern. | Which relationships need extraction or curation, and how will retrieved facts trace to source evidence? |
| Governed lakehouse or federated access | Shared metadata and governed access across data in multiple environments; Google Cloud documents open-format and federated approaches. | Can the needed data be accessed within its security boundaries, and does the design meet application freshness and latency needs? |
Record the same evaluation dimensions for each candidate:
- Data shape: Is the corpus mostly documents, relational records, linked entities, or a mix?
- Query mix: Do requests need semantic similarity, exact terms, structured filters, joins, multi-hop relationships, or analytics?
- Freshness: How soon must a source change affect answers, and are updates event-driven or batch?
- Governance: Can each result be filtered for tenant, role, sensitivity, and residency requirements?
- Scale and latency: What corpus size, concurrency, ingestion rate, and tail-latency target must be supported?
- Operations: Who owns parsing, re-embedding, index rebuilds, backups, recovery, and schema changes?
- Portability: Do storage formats and interfaces align with the organization’s migration needs?
- Cost: What is the full cost of storage, indexing, queries, data movement, and engineering effort?
How do governance, provenance, and freshness fit in?
Enforce access rules during retrieval
Authorization needs to travel with the query into every retrieval path the application uses, including vector, graph, SQL, and agent or tool access. Preserve tenant and source metadata so those paths can apply the relevant policy context. Microsoft and Google Cloud discuss identity, security, encryption, residency, and sensitive-data controls in their platform guidance; AWS documents source-linked graph data. Validate that the chosen design applies its rules consistently to retrieved results.
Keep evidence traceable and correctable
Attach provenance to retrieved chunks and structured facts so a result can be traced to its originating document or record. Plan how corrections and deletions move through derived data. A removed or changed source should not remain indefinitely in an embedding index, graph, cache, or other retrieval structure. The cited guidance supports data-protection controls and source links, but does not prescribe one vendor-neutral deletion-propagation procedure; define and test that procedure for the systems in use.
Measure freshness end to end
Freshness depends on the whole path: source-change detection, processing delay, embedding and index updates, cache lifetime, and query-time policy. Measure the time from a source update to its availability in search on the actual update paths. The reference architectures describe event-driven processing and timely availability as goals, but do not establish one general service-level target.
What should you benchmark before committing?
Test the complete data path, not just nearest-neighbor query speed. The official references are useful implementation guidance for their respective platforms; they do not supply workload-independent thresholds or neutral performance and cost rankings.
- Build a representative test set. Include the real mix of semantic questions, exact terms, structured constraints, joins, and relationship questions the application must handle.
- Use realistic permissions. Test with tenant and role filters in place, including requests that should return no authorized evidence.
- Exercise ingestion and change handling. Measure processing failures, retries, updates, deletions, re-embedding, and index rebuilds as well as initial loading.
- Evaluate results and operations. Track retrieval quality, provenance, stale records, access denials, latency, and the work needed to keep each candidate running.
- Compare full costs and constraints. Include storage, indexing, queries, data movement, and engineering effort, then check regional availability, security boundaries, and operational ownership for the specific products under consideration.
That evidence makes the shortlist useful: it shows which design meets the application’s query, freshness, and governance needs without treating one provider’s reference architecture as a universal prescription.
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