Deep Lake

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").

Deep 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

Deep Lake combines a multimodal storage format with vector and metadata querying, dataset versioning, streaming into training loops, and a Postgres surface. Vespa covers more of the same persist-index-retrieve path across a broader range of retrieval modes, which keeps Deep Lake one step behind it.

• https://docs.deeplake.ai/latest/core/ recorded 2026-09-30

  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 2026-09-30

  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 2026-09-30

  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".