How Lance format and LanceDB's Enterprise feature engineering platform make VLM fine-tuning faster by materializing expensive multimodal features once and training from those columns.

How late materialization in Lance Spark keeps large binary data as lightweight references through query plans, materializing bytes only on write.

Introducing a new LanceDB-backed memory plugin that gives Hermes Agent durable, semantic recall across sessions, with benchmarks and a hands-on remember/recall/forget walkthrough.

A Rust benchmark comparison of Lance, Delta Lake, and Apache Iceberg on S3 and S3 Express, and why Lance is optimized for object storage metadata.

How LanceDB uses the Lance format's flexible data evolution features to enable scalable feature engineering for multimodal datasets.

Introducing stable-worldmodel, an open-source platform for reproducible world model research, evaluation, and benchmarking under visual and physical distribution shifts.

Learn how LanceDB turns raw multimodal data into reproducible, training-ready datasets with search, filtering, deduplication, sampling, and versioned curation workflows.

A hands-on walkthrough showing how LanceDB and DuckDB work together to query multimodal data in SQL, join against multiple tables, and materialize results back into LanceDB.

Choosing a vector database usually comes down to a tradeoff between a full search service and an in-process library. This post showcases benchmarks that compare OpenSearch and LanceDB on the COCO 2017 images embedded with SigLIP. We measure ingestion throughput, query cost, storage layout, and overall infra cost.