Infrastructure / Storage & Retrieval

Lance

Lance is a columnar table format for multimodal data that keeps random access fast enough to train and query directly against stored rows, rather than requiring a scan-oriented layout. Vector indexes, full-text indexes and dataset versioning are part of the format, and it interoperates with Pandas, DuckDB, Polars, PyArrow, Ray and Spark. LanceDB is built on top of it.

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.

https://github.com/lance-format/lance/blob/main/LICENSE recorded 2026-08-18
LICENSE file is the verbatim Apache-2.0 text
https://api.github.com/repos/lance-format/lance recorded 2026-08-18
Repo metadata - license spdx_id Apache-2.0, private false, archived false, default branch main - for lance-format/lance.
https://github.com/lance-format/lance/blob/main/README.md recorded 2026-08-18
README describes an open lakehouse format for multimodal AI with vector search, full-text search, random access, feature engineering and data versioning, compatible with Pandas, DuckDB, Polars, PyArrow, Ray and Spark, with no paid tier, enterprise edition or license-key-gated build beside it.

Adoption

4 high confidence

4.0

4,157,433 PyPI downloads of `pylance` in the trailing 30 days, which lands in the 1M-10M band of the software usage scale, level 4.

https://pypistats.org/api/packages/pylance/recent recorded 2026-08-18
last_month downloads = 4,157,433 for pylance

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/lance-format/lance/blob/main/README.md recorded 2026-08-18
README still documents an open lakehouse format for multimodal AI with vector search, full-text search, random access, feature engineering and data versioning, compatible with Pandas, DuckDB, Polars, PyArrow, Ray and Spark.

Verified 2026-08-18