Amazon Bedrock Knowledge Bases now lets you run the vector store component yourself, offering three distinct back‑ends instead of the default fully managed option. This shift matters because the choice of store directly influences query latency, storage cost, and the operational footprint you must manage.
Supported Customer‑Managed Backends
The service currently integrates with:
- Amazon OpenSearch Service – Available as either a managed cluster or a serverless deployment. It keeps data in memory for fast access, accepts high‑dimensional embeddings, and provides k‑NN as well as hybrid lexical‑vector search.
- Amazon Aurora PostgreSQL with pgvector – Couples Aurora’s relational engine with the pgvector extension. It supports IVFFlat and HNSW indexing methods, multiple distance metrics (L2, cosine, inner product), and vectors up to 2,000 dimensions in single‑precision format.
- Amazon S3 Vectors – Extends S3 object storage with native vector handling. Designed for large‑scale, cost‑effective storage, it delivers sub‑second query times for vector look‑ups.
Performance and Cost Trade‑offs
OpenSearch’s in‑memory design typically yields the lowest latency, which is useful for high‑throughput RAG workloads. Aurora adds the overhead of a relational engine but may simplify scenarios where you already store metadata alongside vectors. S3 offers the cheapest per‑gigabyte storage, making it attractive for massive corpora, though its latency is higher than the other two options.
All three back‑ends accept the same embedding dimensions, but the maximum supported size (2,000 dimensions) is only explicitly mentioned for Aurora. When evaluating cost, consider both compute (cluster or serverless capacity) and storage pricing; OpenSearch clusters and Aurora instances incur compute charges, while S3 charges are primarily storage‑based.
Operational Implications
Moving to a customer‑managed store introduces responsibilities that were previously abstracted away:
- Provisioning and scaling – You must size OpenSearch clusters or configure serverless scaling policies, and choose Aurora instance classes or Aurora Serverless v2 capacity.
- Monitoring and alerting – Each service exposes its own metrics (e.g., OpenSearch search latency, Aurora CPU utilization, S3 request latency) that need to be integrated into your observability stack.
- Backup and durability – Aurora provides automated snapshots; OpenSearch clusters can be backed up via snapshots; S3 relies on its inherent durability model but you may need lifecycle policies for old vector objects.
- Security boundaries – Access control is enforced per service. You will configure IAM policies or resource‑based policies for each store, keeping them separate from the Bedrock Knowledge Base permissions.
Related CloudNinjas coverage: AWS.
What This Means For Practitioners
When selecting a vector store for Bedrock Knowledge Bases, evaluate the following:
- Latency requirements: choose OpenSearch for sub‑millisecond response, S3 if sub‑second is acceptable.
- Data volume and cost: S3 scales cheapest for petabyte‑scale embeddings; OpenSearch and Aurora add compute overhead.
- Existing ecosystem: if you already run Aurora or OpenSearch, reusing those services reduces integration effort.
- Operational bandwidth: assess whether your team can manage cluster sizing, scaling policies, and backup routines for the chosen backend.
By aligning the backend choice with workload characteristics and operational capacity, you can balance performance, cost, and management overhead while leveraging Bedrock Knowledge Bases for Retrieval‑Augmented Generation.
