Will Amazon S3 Vectors Kill Vector Databases–Or Save Them? (zilliz.com)

AWS’s recent launch of Amazon S3 Vectors introduces a cost-effective vector storage and querying solution integrated directly into Amazon S3, positioning itself as a low-cost alternative to traditional vector databases like Milvus, Pinecone, and Qdrant. Targeting the increasing explosion of embedding data driven by AI applications, S3 Vectors leverages the massive scalability and low storage costs of object storage, offering over a 10x reduction in vector storage expenses. This makes it particularly attractive for use cases with low query per second (QPS) demands and tolerant of higher latency, such as archival storage, small-scale retrieval-augmented generation (RAG) implementations, and cost-sensitive prototyping. However, S3 Vectors comes with notable trade-offs in performance and features. It supports cold query latency around 500-700ms, capped write throughput under 2MB/s, limited recall precision (~85-90%), and lacks advanced functionalities like hybrid search, multi-tenancy, and fine-grained filtering. These constraints make it unsuitable for high-performance, real-time search or recommendation systems that demand sub-50ms latency and high throughput. Architecturally, S3 Vectors likely relies on strategies such as 4-bit product quantization, multi-tier caching, dynamic index updates, and distributed microservices to balance cost, availability, and scalability. Rather than signaling the demise of dedicated vector databases, S3 Vectors underscores the evolving paradigm toward tiered vector storage—combining hot, warm, and cold layers optimized for speed, cost, and scale. In this ecosystem, specialized vector databases continue to serve latency-sensitive applications, while solutions like S3 Vectors provide economical cold storage, ultimately complementing each other to address the diverse demands of modern AI workloads.