Deep Lake
Vector database and data lake for AI that stores text images audio video and embeddings in one place with fast dataloaders and RAG friendly tooling.
Overview
Deep Lake by Activeloop combines the strengths of a data lake with the retrieval speed of a vector database so teams can store raw multimodal data and embeddings together and serve them to LLM applications without complex glue code. You can ingest PDFs images audio and video, chunk and embed, run similarity search with metadata filters, and stream batches directly into PyTorch or TensorFlow for training. The format supports versioning and time travel so experiments remain reproducible and lineage stays clear.
For product teams building RAG, Deep Lake provides namespaces, permissioning, and scalable storage pricing, plus an SDK that unifies indexing and query. Pro tiers include generous included storage and token bundles with per unit overages so costs are predictable. Enterprises can deploy through marketplaces and request private networking.
By keeping data and vectors together, Deep Lake reduces pipelines, accelerates iteration, and simplifies moving from notebook to production.
Key features
- Multimodal storage for text images audio video and embeddings in one dataset
- Vector search with metadata filters for precise retrieval at scale
- Native dataloaders for PyTorch and TensorFlow to stream training batches
- Dataset versioning and time travel for reproducibility and audits
- Namespaces roles and tokens to isolate apps and teams
- Python SDK and REST that unify ingest index and query
- Hybrid cloud options and marketplace listings for procurement
- Integrated metrics to monitor ingestion tokens and storage
Best for
- Build RAG assistants grounded in governed documents
- Fine tune vision language models with streamed tensors
- Centralize product FAQs PDFs and images for support bots
- Prototype semantic search across tickets and chats
- Keep training and inference data in one lineage aware store
- Migrate from brittle pipelines to unified multimodal datasets
- Serve evaluations that compare retrievers and prompts
- Support analytics teams with searchable annotated media
Capabilities
Multimodal Datasets
Save text images audio video and embeddings with schema and lineage so training and RAG share one source of truth.
Vector Search
Query by embedding with metadata filters to ground assistants and analytics in relevant context.
Zero copy Dataloaders
Stream tensors directly to GPUs from the store which speeds iteration and reduces boilerplate.
Versioning and Quotas
Use time travel namespaces and included quotas so teams control cost and can audit experiments.
Frequently Asked Questions
How does pricing start?
The pricing page lists Free at $0 per seat with limits and Pro at $40 per seat per month with included storage and tokens, enterprise is custom.
Is there a marketplace option?
Yes, listings on AWS Marketplace and others allow monthly contracting with managed storage bundles.
Can I bring my own embeddings?
You can generate embeddings with your preferred model and store them alongside raw data for unified retrieval.
Is it open source?
Client libraries and formats are documented, check the docs for current open components and licenses.
Does it work for images and video?
Yes, datasets handle frames and media with tensor streaming for computer vision workloads.
