S3 Vectors vs. Dedicated DBs

Amazon S3 Vectors (GA 2025) integrates vector storage and similarity search directly into S3, challenging the assumption that RAG pipelines require a dedicated vector database (Pinecone, Milvus, Weaviate). Engineers building AI retrieval systems now face a fundamental architecture choice: use S3 Vectors for simplicity and cost, or a dedicated vector DB for performance and features. The answer depends on scale, latency requirements, and operational preferences.

• Topics: Vector Indexing on Object Storage, LLM-Assisted Data Systems
• Technologies: Amazon S3 Vectors, AWS S3, LanceDB
• Standards: S3 API, Apache Parquet
• Architectures: Separation of Storage and Compute
• Pain Points: Vendor Lock-In, Cold Scan Latency

1. Understand the fundamental trade-off:

   • S3 Vectors: Zero infrastructure to manage, pay-per-query, vectors live next to your data. But: higher latency (100ms+ vs. <10ms), limited query features, AWS-only.
   • Dedicated vector DBs (Pinecone, Milvus, Weaviate): Sub-10ms latency, advanced filtering, hybrid search, multi-modal. But: separate infrastructure, data synchronization overhead, higher base cost.

2. Choose S3 Vectors when:

   • Your RAG workload is cost-sensitive and latency-tolerant (100ms+ is acceptable).
   • You want to avoid operating vector database infrastructure.
   • Your vectors and source data both live in S3, and you want to eliminate egress and synchronization.
   • Your scale is moderate (millions of vectors, not billions).
   • You are already committed to AWS.

3. Choose a dedicated vector DB when:

   • You need sub-10ms query latency for real-time applications.
   • You need advanced features: metadata filtering, hybrid search (vector + keyword), custom distance metrics, multi-tenancy.
   • You operate at massive scale (billions of vectors) where specialized indexing matters.
   • You need multi-cloud portability for your vector infrastructure.

4. Consider LanceDB as a middle path:

   • LanceDB stores vectors as Lance-format files on any S3-compatible storage (portable, open format).
   • Self-hosted, no vendor lock-in, better latency than S3 Vectors for hot workloads.
   • Trade-off: you manage the infrastructure, but avoid both AWS lock-in and dedicated DB costs.

5. Cost comparison framework:

   • S3 Vectors: ~$0.10/million queries + storage. No base infrastructure cost.
   • Dedicated vector DB: $0.05-$1.00/hour for infrastructure + per-query costs. Fixed base cost even when idle.
   • Break-even: S3 Vectors is cheaper at low query volumes; dedicated DBs win at high, sustained throughput.

• Early RAG architectures (2023) assumed a dedicated vector database was mandatory. Pinecone, Weaviate, and Milvus dominated.
• LanceDB (2023-2024) demonstrated that vector search could run on commodity object storage using an open format, challenging the "you need a specialized DB" assumption.
• S3 Vectors preview (late 2024) and GA (2025) brought vector capabilities directly into the storage layer, reducing the cost floor for RAG.
• The trend is clear: vector search is moving from specialized infrastructure toward the storage layer, following the same pattern as SQL (compute moves to where data lives).