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Scaling RAG beyond a pilot means designing a dependable data and serving system—not simply choosing a larger vector database. Production architectures separate source files, metadata, chunks, embeddings, indexes, retrieval, application serving, and evaluation. Agentic systems may also need conversation state and governed short- and long-term memory. The right storage arrangement depends on the workload and operating constraints; official architectures document several workable patterns, not one universally best backend.
What changes when a RAG pilot becomes a production system?
A pilot can make retrieval look like a single operation: embed a collection, place the vectors in an index, and search it when a question arrives. A production system has two connected paths. An ingestion path turns authoritative source data into searchable material and keeps it current. A serving path receives a request, applies the caller’s permissions and query context, retrieves appropriate evidence, and returns an answer that can be monitored and evaluated.
That separation matters because the vector index is only one representation of the data. Original files, metadata, embeddings, indexes, access rules, logs, and evaluation records have distinct roles and may live in different systems. The design must account for how they are created, updated, protected, and served—not just where vectors are stored.
Ingestion: turn source data into governed retrieval material
Start with authoritative sources and a repeatable ingestion process. Depending on the architecture, files may first land in object storage; processing then extracts or parses content, creates metadata, divides content into chunks, generates embeddings, and updates a searchable index. Keeping source material and generated retrieval artifacts distinguishable helps teams trace what an answer came from and refresh derived data when a source changes.
Metadata is operationally important, not just descriptive. It can identify a document’s owner, source, freshness, classification, or access scope, and can be used to filter what a particular request is allowed to retrieve. A semantically relevant chunk is not automatically an authorized one.
Serving: retrieve within the request’s context
At query time, the application may construct filters from the user’s identity, request context, or other policy inputs before invoking retrieval. The retrieved material is then provided to the model as context. A production design should preserve enough provenance to determine which sources informed an answer and should provide a way to assess response quality rather than treating a successful index lookup as proof of a good answer.
Google’s managed Gemini Enterprise/Agent Platform architecture illustrates this separation: source files are staged in Cloud Storage, generated metadata is held in a separate bucket, and a managed datastore parses and chunks content, generates embeddings, and maintains a searchable vector index. A backend can construct filters for requests before the RAG flow runs. Google’s architecture page was last reviewed on November 10, 2025.
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There is no single backend requirement in the documented architectures. A managed datastore, a relational database with a vector extension, or a modular deployment with a pluggable vector-search component can each be part of a production design. Compare the full operating model: where source and derived data live, who runs ingestion and index maintenance, how authorization and filtering work, and what monitoring and evaluation are available.
| Pattern | Documented data path | Operational characteristics shown by the source | Important qualification |
|---|---|---|---|
| Managed searchable datastore | Cloud Storage stages source files; a separate bucket holds generated metadata; the managed datastore parses and chunks data, generates embeddings, and maintains a searchable vector index. | A backend can construct retrieval filters. Index and embedding operations are part of the managed datastore architecture. | Google’s architecture example; it does not establish comparative cost, speed, or retrieval quality against other patterns. |
| Relational database with vector extension | Cloud Storage sources trigger processing; content is chunked and embedded, then stored in AlloyDB for PostgreSQL using pgvector. | The design includes serving logs and an evaluation subsystem for response factual accuracy and relevance. | Google specifies that query and source embeddings must use the same model and parameters. This is a documented design, not a claim that every relational deployment has the same capabilities. |
| Modular deployment with vector-search choices | NVIDIA’s blueprint uses S3-compatible object storage (SeaweedFS by default), hybrid dense and sparse retrieval, metadata filters, and a vector database; it names Elasticsearch as the default and Milvus as an optional backend. | The blueprint also includes reranking, authorization, observability, and RAGAS evaluation scripts. NVIDIA’s enterprise guide describes separate Kubernetes components for a RAG server, extraction and embedding services, a vector database, agents, models, monitoring, and tracing. | These are vendor reference architectures. Their listed components are not universal requirements or neutral evidence of superiority. |
Managed datastore: reduce the amount of infrastructure you operate
A managed index pattern can put parsing, chunking, embedding, and searchable-index maintenance inside a provider’s datastore service, while leaving source files and generated metadata in object storage. It may suit teams that want a provider-managed retrieval path and can accept the service’s data model and governance boundaries. Confirm how updates, metadata filters, permissions, residency, observability, and recovery work for your particular deployment; the reference architecture alone does not settle those requirements.
Relational database: keep vector retrieval alongside relational data
A relational design can store vectors in PostgreSQL through pgvector, as in Google’s AlloyDB example. This is a supported pattern, not proof that every existing relational database is an appropriate vector-serving system at any scale. The embedding compatibility rule is especially consequential: source and query embeddings need the same model and parameters. If that representation changes, the application must account for the mismatch rather than silently comparing vectors from incompatible configurations.
Modular vector search: choose components and operate the stack
A modular deployment can combine object storage, dense and sparse retrieval, metadata filtering, reranking, authorization, and evaluation around a vector database. NVIDIA’s blueprint documents Elasticsearch by default and Milvus as an option; this illustrates pluggability rather than a universal backend recommendation. NVIDIA’s enterprise guide describes separately deployed services in Kubernetes, which gives teams component boundaries but also means they must plan how those components are deployed, tuned, monitored, and scaled together.
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Enterprise knowledge retrieval and agent memory are related but different needs. A knowledge layer retrieves governed information from organizational sources. An agent may additionally need access to tool context and a way to retain conversations or insights as short- or long-term memory. AWS’s enterprise agent architecture describes both memory needs and knowledge-base mechanisms that may use vector stores or graph storage, with role-based access control.
That architecture does not prescribe a particular memory database, retention interval, or memory policy. Treat those as application and governance decisions: decide what the agent may retain, what purpose retention serves, who can access it, how it can be corrected or deleted, and when it expires. Avoid treating every conversation as durable memory by default.
Keep permissions attached to the data and the request across retrieval and tool use. A memory store does not replace the authorization rules governing enterprise documents, and a knowledge index does not automatically supply the conversation state an agent requires.
How should production RAG handle security, provenance, and quality?
RAG can give a model access to current, context-specific information without putting that information into model parameters. That separation is useful, but it does not make sensitive data safe automatically. AWS Prescriptive Guidance identifies risks including data exfiltration, poisoned data sources, unauthorized access, sensitive output disclosure, and missing provenance. It recommends layered controls, including metadata filtering, access control, and redaction.
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Enforce authorization separately from relevance
Vector similarity answers whether material appears relevant to a query; it does not determine whether the requesting user is entitled to see it. Apply identity- and policy-aware filters before or during retrieval, and ensure the filter cannot be bypassed by a prompt or agent action. AWS’s agent guidance describes least-privilege role-based controls for knowledge bases. Test access boundaries with users and records that have different permissions, not only with a privileged test account.
Preserve provenance and freshness
Keep source identity and useful metadata with chunks so an answer can be traced to the underlying material. Track whether sources are current and whether ingestion has processed updates or removals. Provenance helps investigate unsupported answers and poisoned or stale content; similarity scores alone do not establish that a retrieved source is trustworthy or current.
Evaluate the answer, not just the search operation
Monitor both system behavior and response quality. Google’s AlloyDB design includes serving logs and an evaluation subsystem that scores factual accuracy and relevance. NVIDIA’s blueprint includes observability and RAGAS evaluation scripts; its enterprise guide describes monitoring and tracing components. These examples support treating observability and evaluation as part of the serving design, while they do not establish a neutral quality ranking among systems.
Use evaluation to inspect the full chain: whether the right sources were ingested, whether retrieval respected filters, whether the retrieved evidence supports the answer, and whether the final response is relevant and factual. Logging also needs governance: define what is recorded, who can inspect it, and how long it is retained, especially where prompts or retrieved passages may contain sensitive information.
How do you size the stack for your workload?
Size each layer from its own workload rather than extrapolating a single vector-count figure. NVIDIA’s Enterprise RAG Deployment Guide gives one configuration example: one million embeddings at 2,048 dimensions in FP32, with MinIO object storage using 500 GB of disk and separate data/index and query nodes. Those values describe that guide’s stated deployment context; they are not a general storage-per-million-vectors rule or a cross-vendor benchmark.
A vector count alone does not specify original document storage, metadata volume, index overhead, replicas, retention, query concurrency, or ingestion behavior. The reviewed vendor architectures do not establish cross-vendor performance, cost, or retrieval-quality results for those variables. Build estimates from measurements and requirements for your own system.
Questions to answer before choosing capacity
- Source corpus: How much authoritative content exists, how quickly will it grow, and how much original-file storage must be retained?
- Embedding representation: What dimensions and model versions will be used, and how will re-embedding be handled when the model or parameters change?
- Metadata and index: How much metadata is attached to chunks? Which index method and replica policy are required?
- Ingestion: How often do sources change, and what update rate or processing backlog is acceptable?
- Serving: How many concurrent retrieval requests are expected, and what latency targets apply to retrieval and the full response?
- Retention: How long must source artifacts, conversation or agent memory, logs, and evaluation records be kept?
- Governance: What access boundaries, redaction rules, audit needs, and data-residency constraints apply to each data type?
How should you choose between the patterns?
Use the architecture that fits the team’s operating constraints and data governance rather than assuming a vector database is the central decision. A managed service can reduce responsibility for some indexing operations but places the design within that provider’s service model. A relational vector extension can align retrieval with a PostgreSQL-based design, subject to the system’s workload and embedding requirements. A modular deployment exposes component choices and integration points, while requiring the team to run and coordinate more of the stack.
Before committing, document the end-to-end flow for source updates, permission changes, retrieval filters, answer provenance, failure monitoring, evaluation, and retention. Then validate that flow with realistic data volumes and access patterns. The available vendor architectures show feasible arrangements, but they do not provide a neutral cost, speed, quality, or total-cost-of-ownership comparison.
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