Vector databases for building RAG, semantic search, and AI agents on AWS
Find the right vector database for your agents running in AWS — RAG, semantic search, and long-term memory
Three problems that vector storage solves
RAG pipelines: Ground your LLM in your data
Retrieval-Augmented Generation feeds relevant documents to a model before it generates a response. The vector database is the storage and query layer. Given a user’s question, it finds semantically similar chunks from your knowledge base, and hands them to the model. Without it, you’re relying on the model’s training data alone — outdated, or missing your domain entirely.
Agentic memory: Give your AI agent persistent context
AI agents that reason across multi-step tasks need memory that survives between sessions. Tool selections, conversation history, user preferences — these need semantic lookup, not just key-value retrieval. A vector database gives your agent the ability to recall relevant prior context. No replaying entire conversation histories.
Semantic search: Match intent, not just keywords
Traditional search returns results based on term frequency. Semantic search returns results based on meaning. A user searching “how to reduce cloud costs” should find your document titled “Optimizing AWS spend” — even though the words don’t overlap. Vector storage makes this work by comparing embeddings rather than strings.
How to choose a vector database for your AI use-case
There’s no single “right” vector database. There’s the right one for what you’re building on Amazon Bedrock, the team you have, and the infrastructure you already run. Spec sheets won’t help — vendors optimize benchmarks for their own strengths. Here’s a framework based on what actually matters when you’re building on AWS.
- Does the database support hybrid queries natively, or do you need to chain separate lookups?
- What’s the latency impact of adding filters to a vector query?
Pure vector search works well when your data is semantically rich and queries are open-ended. But production applications almost always need structured metadata filtering too — dates, categories, user IDs. Hybrid search combines vector and keyword/filter in one query. No running two systems. No post-filtering results client-side.
Questions to ask:
- How does the database handle index updates while serving queries?
- What’s the memory/storage trade-off at your target scale?
- Does pricing scale linearly with data volume?
Most vector databases perform well in demos. The differences emerge at scale — when your index grows, query concurrency increases, and you need to update embeddings without downtime. Some databases scale horizontally by adding nodes. Others require index rebuilds. That distinction matters less for prototypes. It matters a lot for production.
Questions to ask:
- What’s your team’s capacity for database operations?
- Does the managed service run in your AWS Region?
- What does the migration path look like if you start managed and later need more control (or vice versa)?
Fully managed databases handle provisioning, scaling, patching, and backups. Self-hosted options (or open-source deployments on Amazon EKS) give you more control over configuration and data locality, but your team owns the ops. There’s a real cost to both choices — managed services charge a premium, self-hosted options charge your team’s time.
Questions to ask:
- Does the database integrate with Amazon Bedrock’s embedding models directly?
- What ingestion APIs are available (batch, streaming, real-time)?
- How do you handle embedding version updates across your index?
Your embedding pipeline — the workflow that takes raw documents, generates vector embeddings, and loads them into the database — needs to work with your existing data infrastructure. Teams using Amazon Bedrock for embedding generation need a database that accepts vectors through standard APIs without custom adapter code. Teams with existing ETL pipelines need batch loading support.
Questions to ask:
- Does the database support pre-filtering (filter before ANN) or only post-filtering?
- What data types can be stored as metadata?
- Are there limits on the number of metadata fields or filter complexity?
Metadata filtering isn’t glamorous, but it’s critical for multi-tenant applications and access-controlled datasets. If your application serves multiple customers, you need to filter by tenant ID before running similarity search — not after. Post-filtering wastes compute and returns fewer results than requested. Pre-filtering requires the database to support filtered approximate nearest neighbor (ANN) search natively.
Questions to ask:
Vector databases available in AWS Marketplace
Two approaches: purpose-built vector databases designed from the ground up for vector workloads (Pinecone, Zilliz Cloud), and data platforms your team may already operate that add vector capabilities (Elastic Cloud, Redis Cloud, MongoDB Atlas). We’ve seen both approaches ship to production — the right choice depends on whether you’re building from scratch or extending what you have.
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