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Vector DBFreemiumOpen Source
TILEDB
Universal storage engine with multi-dimensional array and vector search support
MIT
ABOUT
AI applications often need to store and search vectors alongside rich structured metadata, requiring separate systems for vector search and data storage. TileDB solves this with a unified array storage engine that handles both dense and sparse multi-dimensional data — including embeddings — with native vector search, time-travel versioning, compression, and multi-cloud storage support. This eliminates the complexity of managing separate vector and metadata stores while providing robust data management features.
INSTALL
pip install tiledb-vector-searchINTEGRATION GUIDE
1. Store and search AI embeddings with their associated metadata in a single unified storage backend
2. Build production retrieval-augmented generation pipelines with time-travel data versioning and cloud-native scalability
3. Manage large-scale scientific and geospatial arrays alongside vector embeddings for multi-modal AI applications
4. Replace separate vector database and data warehouse with a single storage engine for simplified infrastructure
TAGS
vector-searchembeddingsstorage-enginemulti-dimensionaldataframessparse-arrayscloud-storage