# storage

> Browse 3 items tagged with storage.

## Items

- [Amazon S3 Vector Search](https://vectordb.ever.works/items/s3-vector-search) — Leveraging Amazon S3 as a storage layer for vector databases, enabling 70-95% cost reduction for certain use cases. S3's low storage costs make it attractive for large-scale vector datasets with appropriate access patterns.
- [Breaking the Storage-Compute Bottleneck in Billion-Scale ANNS](https://vectordb.ever.works/items/breaking-storage-compute-bottleneck) — A 2025 research paper presenting a GPU-driven asynchronous I/O framework for billion-scale approximate nearest neighbor search. The system addresses the fundamental bottleneck of data movement between storage and compute in large-scale vector search.
- [Vector Database Cost Optimization](https://vectordb.ever.works/items/vector-database-cost-optimization) — Strategies for reducing vector database costs including quantization, dimension reduction, efficient indexing, storage tiering, and choosing cost-effective deployment options.

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