Infrastructure / Storage & Retrieval

Deep Lake

Deep 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.

Verified 2026-08-18 via the GitHub API and the LICENSE body.

Openness

5 high confidence

5.0

license: Apache-2.0(OSI)
source: public
core-gated: ungated

Apache-2.0 license body confirmed. The repository is public and unarchived and builds the whole product, and the README describes no paid tier, enterprise edition or license-gated build beside it, so source is public and the core ungated. The repository carries the cpp/, postgres/ and python/ trees, so the engine and its bindings are all published; the managed offering is a service beside the Apache-2.0 core.

https://github.com/activeloopai/deeplake/blob/main/LICENSE recorded 2026-08-18
LICENSE file is the verbatim Apache-2.0 text
https://api.github.com/repos/activeloopai/deeplake recorded 2026-08-18
Repo metadata - license spdx_id Apache-2.0, private false, archived false, default branch main - for activeloopai/deeplake.
https://github.com/activeloopai/deeplake/blob/main/README.md recorded 2026-08-18
README describes a database for AI on a storage format optimized for deep learning, used both for storing and searching data plus vectors in LLM applications and for managing datasets while training, over embeddings, audio, text, video, images, DICOM and PDFs, with no paid tier, enterprise edition or license-key-gated build beside it.

Adoption

3 high confidence

3.0

128,377 PyPI downloads of `deeplake` in the trailing 30 days, which lands in the 100K-1M band of the software usage scale, level 3.

https://pypistats.org/api/packages/deeplake/recent recorded 2026-08-18
last_month downloads = 128,377 for deeplake

Capability

4 medium confidence

4.0

Banded on the category feature matrix as complete retrieval system, distributed or cluster-capable. Placed one band below the vespa anchor, on a matrix that bands on how much of the persist-index-retrieve path a product provides itself, at what scale and over how many retrieval modes.

https://github.com/activeloopai/deeplake/blob/main/README.md recorded 2026-08-18
README still documents a database for AI on a storage format optimized for deep learning, used both for storing and searching data plus vectors in LLM applications and for managing datasets while training, over embeddings, audio, text, video, images, DICOM and PDFs.

Verified 2026-08-18