Deep Lake
ActiveloopDeep Lake is a database for AI built on a storage format designed for deep-learning access patterns, holding embeddings alongside the audio, text, video, image, DICOM and PDF records they were derived from. It serves two jobs from one store: vector and metadata retrieval for LLM applications, and dataset management and streaming for training. Activeloop develops it, with a C++ core, a Postgres integration and Python bindings in the repository.
Openness
5 high confidence- license
- Apache-2.0(OSI)
- source
- public
- core features withheld
- no
Deep Lake's Apache-2.0 core is public, unarchived and builds the whole product, with no paid tier or enterprise edition gating it. The repository publishes the C++ engine along with its Postgres and Python bindings, and the managed offering is a service sold beside that core.
- https://docs.deeplake.ai/latest/core/ recorded 2026-09-30
Core docs page says "The open-source Deeplake library provides a multi-modal AI database for dataset management, vector search, and deep learning training."
- https://raw.githubusercontent.com/activeloopai/deeplake/main/LICENSE recorded 2026-09-30
LICENSE file is the Apache-2.0 text: "Apache License" and "Version 2.0, January 2004" and "TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION".
- https://raw.githubusercontent.com/activeloopai/deeplake/main/postgres/README.md recorded 2026-09-30
The repository postgres directory README calls pg_deeplake a "PostgreSQL extension for vector similarity search, full-text search, and hybrid search using DeepLake" and gives "Building from Source" steps.
- https://raw.githubusercontent.com/activeloopai/deeplake/main/README.md recorded 2026-09-30
README describes "a Database for AI powered by a storage format optimized for deep-learning applications" and installs with "pip install deeplake"; searched the body for enterprise edition, paid tier and license key: none (the word enterprise appears only as "enterprise-grade LLM-based products").
Adoption
3 high confidenceDeep Lake's adoption reading comes directly from PyPI downloads of the deeplake package, since that package is the storage and query library itself rather than a client for a separate service. That is a direct signal of use, though it counts only PyPI installs and not use through other package managers or vendored copies.
- https://pypistats.org/api/packages/deeplake/recent recorded 2026-08-18
last_month downloads = 128,377 for deeplake
Capability
3 medium confidenceDeep Lake combines a multimodal storage format with vector, BM25 and hybrid querying, dataset versioning and streaming into training loops. None of its first-party documentation describes a cluster or distributed serving mode, so it works as a single store rather than one that scales across machines, two steps below Vespa.
- https://docs.deeplake.ai/latest/core/ recorded 2026-10-08
Core docs page lists vector search with "cosine similarity, BM25 text search" and says to "Store datasets locally or on any cloud".
- https://raw.githubusercontent.com/activeloopai/deeplake/main/postgres/README.md recorded 2026-10-08
The Postgres extension README lists full-text search "with BM25 scoring on text columns" and hybrid search "combining vectors and text".
- https://raw.githubusercontent.com/activeloopai/deeplake/main/README.md recorded 2026-10-08
README says Deep Lake offers "storage for all data types" with "querying and vector search, data streaming while training models at scale, data versioning and lineage" and "it is serverless"; it describes no cluster or distributed serving mode.
Verified 2026-09-24