Deep Lake
Database for AI that stores tensors and embeddings
Deep Lake is profiled here as a Vector Database tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Deep Lake is a database for AI from Activeloop, founded by Davit Buniatyan, that stores datasets as versioned tensors and doubles as a vector store for retrieval. It keeps images, video, text, and embeddings in one columnar format on object storage, so teams can version data, stream it into training, and run vector search over the same tables. This combination suits workflows that move between managing training data and serving a retrieval layer for AI applications.
Key Capabilities:
Tensor storage that holds images, video, text, and embeddings in one format
Vector search for retrieval-augmented generation over stored embeddings
Dataset versioning with branches and time-travel to earlier states
Streaming that feeds data directly into training without local copies
Storage on object stores that separates compute from data
Query and visualization tools for exploring multimodal datasets
Alternative tools
- Marqo
Open-source vector search with built-in embedding inference
- Chalk
Feature platform for real-time machine learning data
- Hopsworks
Feature store and ML platform for batch and real-time data
- turbopuffer
Serverless vector and full-text search on object storage
- LanceDB
Embedded multimodal vector database on the Lance format
- pgvector
Vector similarity search as a Postgres extension