Choosing the Right Vector Store for Amazon Bedrock Knowledge Bases

Retrieval-Augmented Generation (RAG) has emerged as the standard pattern for enterprise artificial intelligence, enabling Large Language Models (LLMs) to answer questions grounded in private organizational data. Amazon Bedrock Knowledge Bases abstracts away much of the boilerplate infrastructure needed to build RAG pipelines by fully managing ingestion, chunking, embedding generation, and vector retrieval.

However, the performance, operational cost, and latency profile of your RAG architecture rely heavily on the underlying vector storage engine you select. When configuring Amazon Bedrock Knowledge Bases, architects must choose between vector database options, including Amazon OpenSearch Service Serverless (AOSS), Amazon Aurora PostgreSQL with pgvector, and emerging cloud-native options like Amazon S3-backed vector index storage. When routing traffic to backend LLMs such as Claude 3.5 Sonnet or DeepSeek-V3, API latency and provider reliability become paramount. Platform developers often leverage unified API aggregators like n1n.ai to streamline multi-model fallback and maintain sub-100ms API gateway latency alongside their vector retrieval pipelines.

This guide provides a deep-dive comparative benchmark, architectural analysis, and practical selection framework to help enterprise developers pick the optimal vector store for Amazon Bedrock Knowledge Bases.

The Core Candidates: Architectural Overview

Amazon Bedrock Knowledge Bases decouples the embedding generation and LLM orchestrator from the underlying vector database. Below are the primary vector engine choices natively integrated into AWS RAG workflows.

1. Amazon OpenSearch Serverless (AOSS)

Amazon OpenSearch Serverless is the default, fully managed vector engine for Amazon Bedrock Knowledge Bases. It leverages k-Nearest Neighbor (k-NN) search built on Apache Lucene, supporting HNSW (Hierarchical Navigable Small World) and IVF (Inverted File) indexing algorithms.

• Best For: High-throughput enterprise RAG, hybrid search (keyword + vector), and dynamic auto-scaling workloads.
• Storage Mechanism: Serverless Search Collections governed by OpenSearch Compute Units (OCUs).
• Indexing: HNSW vector index with cosine similarity, Euclidean distance, or dot product.

2. Amazon Aurora PostgreSQL with pgvector

For workloads already running on relational databases, Amazon Aurora PostgreSQL with the pgvector extension allows storing vector embeddings alongside existing transactional business data. Aurora supports both HNSW and IVFFlat indexes.

• Best For: Enterprise applications requiring ACID transactional compliance, joint relational-vector metadata filtering, and cost optimization on existing database clusters.
• Storage Mechanism: Aurora Storage Engine with partitioned table indexes.
• Indexing: pgvector HNSW index or ivfflat index.

3. Amazon S3 Vectors & Low-Cost Object Storage RAG

With the rapid evolution of object-storage vector indexing engines (such as LanceDB on S3 or vector index formats optimized for cloud storage), storing embeddings directly in S3 backed by lightweight query engines represents the ultra-low-cost frontier for asynchronous or batch RAG pipelines.

• Best For: Cold-data querying, massive archive RAG pipelines, prototyping, and non-real-time analytical applications.
• Storage Mechanism: Amazon S3 object storage with memory-mapped vector indexes.
• Indexing: DiskANN or serialized Flat/IVF indices loaded on demand.

Performance and Latency Benchmarks

To evaluate vector store efficiency, we tested vector retrieval across three distinct RAG workloads using 1,000,000 document chunks embedded with Amazon Titan Text Embeddings v2 (1024 dimensions).

Benchmark Scenarios

1. Scenario A: High Concurrency Customer Support (Query rate: 500 QPS, Metadata filtering: Moderate, Target Latency: < 50ms).
2. Scenario B: Deep Enterprise Knowledge Management (Query rate: 20 QPS, Metadata filtering: Heavy enterprise RBAC, Target Latency: < 200ms).
3. Scenario C: Batch Document Analytics (Query rate: 2 QPS, Large payload, Budget prioritized over speed).

Performance & Cost Comparison Matrix

When coupling vector retrieval with fast inference models via n1n.ai, overall end-to-end RAG latency is dominated by vector retrieval time and initial token generation (TTFT). Choosing an index like AOSS HNSW or Aurora HNSW ensures vector retrieval stays below 30ms.

Architectural Deep Dive & Selection Framework

Use Case 1: OpenSearch Serverless for High-Scale Enterprise RAG

If your organization requires Hybrid Search (combining full-text keyword matching like BM25 with dense vector similarity), OpenSearch Serverless is the premier choice. Amazon Bedrock Knowledge Bases supports hybrid search out of the box with AOSS.

Key Advantages:

• Automated Provisioning: Bedrock can automatically provision the AOSS collection, vector index, and access policies during setup.
• Hybrid Search: Combines keyword search and vector embeddings using Reciprocal Rank Fusion (RRF) to increase retrieval precision.
• Independent Scaling: Compute (OCUs) scales independently from data storage.

Trade-offs:

• Cost Floor: AOSS requires a minimum of 2 OCUs (1 for indexing, 1 for search), leading to a baseline cost of roughly $350/month even for low-traffic projects.

Use Case 2: Aurora PostgreSQL (pgvector) for Relational & Filter-Heavy RAG

When vector search must be strictly tied to transactional business data—such as filtering documents by user_id, tenant_id, or granular relational permission tables—Aurora PostgreSQL with pgvector excels.

Key Advantages:

• Relational Consistency: Perform vector similarity queries directly inside standard SQL SELECT statements with complex WHERE clauses.
• Cost Efficiency for Existing Workloads: If you already operate an Aurora cluster, adding vector capabilities incurs zero baseline infrastructure cost overhead.
• Index Control: Fine-tune m and ef_construction parameters for HNSW indexes directly via SQL.

Implementation Code Snippet (Boto3 & LangChain / SQL Setup):

import boto3
import psycopg2
from pgvector.psycopg2 import register_vector

# Establish connection to Aurora PostgreSQL
conn = psycopg2.connect(
    dbname="knowledge_base