Vector Indexing on Object Storage
Summary
What it is
The practice of building and querying vector indexes over embeddings derived from data stored in S3.
Where it fits
This topic connects the LLM side of the index to the storage side. Embeddings are generated from S3-stored content, indexed for similarity search, and the results point back to S3 objects.
Misconceptions / Traps
- Vector indexes are not a replacement for structured queries. They answer "what's semantically similar?" not "what matches this predicate?"
- Storing vector indexes on S3 (e.g., LanceDB) is viable but query latency is higher than dedicated vector databases with in-memory indexes.
Key Connections
scoped_toObject Storage, S3 — vectors are derived from and point to S3 data- LanceDB
scoped_toVector Indexing on Object Storage — S3-native vector database - Embedding Model
scoped_toVector Indexing on Object Storage — produces the vectors - Hybrid S3 + Vector Index
scoped_toVector Indexing on Object Storage — the architectural pattern - Embedding Generation
scoped_toVector Indexing on Object Storage — the capability that feeds vectors
Definition
What it is
The practice of building and querying vector indexes over embeddings that are derived from data stored in S3.
Why it exists
Semantic retrieval (finding content by meaning) requires vector representations. When the source data lives in S3, the vector index must bridge the gap between unstructured storage and structured similarity search.
Relationships
Outbound Relationships
scoped_toInbound Relationships
Resources
AWS Architecture Blog describes a production-grade 1B+ vector search solution built on LanceDB with S3 as the storage layer.
LanceDB documentation for this serverless vector database built on the Lance columnar format, designed for S3-native storage.
Milvus is the leading open-source vector database with S3-backed storage support; its architecture docs explain how vector indexes are persisted to object storage.