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What an embedding store does on AWS
An embedding store holds vector representations of content and lets an application retrieve relevant items by similarity. In a retrieval-augmented generation (RAG) application, that usually means finding context for a model’s response. The store may also need to handle text search, metadata filters, transactional records, graph relationships or document data.
On AWS, “embedding store” describes an architectural role, not a single product. AWS’s database decision guide and vector database comparison cover services with distinct capabilities. The shortlist should reflect the application’s data shape and search requirements, not just whether a service is labeled a vector database.
Which AWS embedding store should you evaluate?
Use these options as a shortlist, not a performance ranking. Availability, supported features and limits can vary by service, configuration and Region.
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| Option | Evaluate it when… | What to check |
|---|---|---|
Amazon OpenSearch Service |
You need vector similarity alongside full-text search, or search-oriented controls are central to the application. | Compare managed cluster and Serverless approaches, indexing and hybrid retrieval requirements, expected throughput, and operational fit. AWS’s vector database options describe OpenSearch and other services; AWS also discusses an OpenSearch Serverless vector-search path alongside DynamoDB in its database decision guide. |
Amazon Aurora PostgreSQL or Amazon RDS for PostgreSQL with pgvector |
Your application already uses PostgreSQL, or relational and transactional data should live alongside vectors. | For the documented Aurora Knowledge Base path, check engine compatibility, pgvector version, RDS Data API, credentials and schema before implementation. Details are in AWS’s Aurora PostgreSQL guide for Amazon Bedrock Knowledge Bases. |
Amazon MemoryDB |
In-memory access is a priority for the workload. | Confirm that its memory-oriented cost and operating characteristics suit your requirements. MemoryDB is an in-memory database with vector-search support, not a general answer to every latency-sensitive workload. See AWS’s vector database options. |
Amazon Neptune Analytics |
Retrieval depends on relationships among entities, such as graph-oriented queries or GraphRAG. | Make sure graph relationships contribute meaningfully to retrieval; a graph platform is not necessary just because the application uses embeddings. See AWS’s vector database options. |
Amazon DocumentDB |
The application’s central data model is MongoDB-compatible documents and vector retrieval belongs with that data. | Check compatibility and the current index and dimensional limits for the version you plan to use. See AWS’s vector database options. |
Amazon S3 Vectors |
You have a large vector collection and its storage and request economics and access pattern fit the application. | Validate quotas and query requirements; AWS documents an export path to OpenSearch for use cases needing higher query throughput and lower latency. See the S3 Vectors integrations guide. |
If PostgreSQL, OpenSearch or another suitable platform is already part of your AWS architecture, that existing investment and the team’s experience with it are relevant selection criteria. AWS’s comparison guidance also treats setup complexity and team expertise as factors, rather than ranking services by a single universal measure.
Should Bedrock Knowledge Bases manage retrieval?
Amazon Bedrock Knowledge Bases is both an orchestration option and an integration point: it can connect data sources, create chunks and embeddings, store vectors in supported services, and retrieve context for generative-AI applications. Its setup flow lists quick-create paths that include OpenSearch Serverless, Aurora PostgreSQL Serverless, Neptune Analytics and S3 Vectors. Those choices do not mean the underlying store is interchangeable; confirm that the selected service supports the needed data source and workflow in the current documentation. AWS describes the setup flow in Create a knowledge base by connecting to a data source.
Source choice can narrow the available store options. In the cited setup flow, AWS documents OpenSearch Serverless as the only supported vector store for Confluence, Microsoft SharePoint and Salesforce sources. Supported integrations evolve, so verify the current choices when configuring a Knowledge Base.
A managed Knowledge Base is a fit when its source connectors, ingestion workflow, retrieval controls and operating model meet the application’s needs. A custom RAG pipeline may be more appropriate when you need a database or workflow that Knowledge Bases does not support, or you need greater control over retrieval. That control also makes your team responsible for ingestion and updates, indexing, access control, observability and ongoing operations. AWS outlines these considerations in its guide to choosing a RAG option on AWS.
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Check S3 Vectors limits before choosing it
AWS’s S3 Vectors limitations page, checked on September 30, 2026, lists these service quotas:
- Up to 10,000 vector buckets per Region per account.
- Up to 10,000 indexes per bucket.
- Up to 2 billion vectors per index.
- Vector dimensions from 1 through 4,096.
The same page lists metadata and API request limits. Check the current limitations and restrictions for those details and any updates. These are documented limits, not a promise that a particular index will deliver a given latency or throughput.
AWS also documents exporting a snapshot of an S3 vector index to OpenSearch for high-query-throughput, low-latency search. That suggests a tiered architecture to evaluate when many vectors are infrequently queried but a smaller working set needs more responsive search. Before relying on this design, verify that the export flow’s freshness and update behavior meet your application’s needs; the integration documentation describes the S3 Vectors export and service integrations.
For Aurora Knowledge Bases, verify the integration prerequisites
AWS documents the following requirements for using Aurora PostgreSQL as a Bedrock Knowledge Base:
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- A compatible Aurora PostgreSQL cluster and pgvector version 0.5.0 or higher.
- RDS Data API.
- A user-managed secret in AWS Secrets Manager.
- A suitable table schema for record IDs, text chunks, embeddings and metadata.
Check AWS’s Aurora PostgreSQL Knowledge Base instructions for the current compatible engine-version list and setup details. These prerequisites apply to the documented Aurora integration, not automatically to every PostgreSQL-based vector architecture.
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There is no reliable universal “cheapest” choice without workload assumptions. AWS describes different cost dimensions across services, including instance or node hours, storage, capacity units, requests and data transfer. Knowledge Base costs also depend in part on the underlying vector service and usage. The AWS cost comparison explains these differences; use current regional rates and the AWS Pricing Calculator for an estimate rather than comparing billing models in isolation.
Model the whole retrieval path, not just vector storage: ingestion and embedding, index and compute capacity, queries, updates, backups or snapshots, data transfer and any Bedrock usage. For operations, identify who will own schema and metadata changes, re-indexing, backup and restore, scaling, monitoring, access policies, network boundaries and regional recovery.
A meaningful evaluation needs a representative corpus and consistent assumptions for embeddings, filters, top-k results, update patterns, concurrency, Region and retrieval-quality target. Measure the latency and quality your application requires under that workload; AWS’s published guidance does not establish a universal p95 latency or total cost for your application.
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- Start with the data model. Decide whether the workload is primarily relational, search-heavy, graph-oriented, document-based, in-memory or a large vector collection.
- Check integration constraints. If using Bedrock Knowledge Bases, verify that its supported store and source combination fits the application. If using Aurora, confirm the documented engine, extension, API, secret and schema requirements.
- Write down workload targets. Record vector count and dimensions, update frequency, query volume and concurrency, latency target, filtering needs and any full-text or graph requirements.
- Estimate and test the full design. Use current regional pricing and a representative workload, including ingestion, operations and recovery—not only storage and query charges.
- Confirm current limits and regional availability. Recheck the AWS service documentation for the intended Region and configuration before deployment.
AWS guidance and quotas cited here were checked on September 30, 2026. Service capabilities, integrations, limits, regional availability and prices can change.
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