LanceDB

LanceDB customers feature teams building production AI applications with vector search and multimodal data.

LanceDB offers white papers, guides, and ebooks about multimodal AI, RAG, and search, with examples and production patterns.

Most vector databases require three systems: raw data in a lake, metadata in a warehouse, embeddings in a vector index. Three copies of state. Three things to keep in sync. LanceDB stores everything in one table on object storage. Compute nodes are stateless. No RAM constraints at scale.

Blogs, tutorials, benchmarks, and case studies on RAG pipelines, agent memory, semantic search, and multimodal databases with versioning and object storage.

Turn information-rich PDFs into a local, inspectable, searchable evidence store with LlamaIndex's LiteParse library and LanceDB.

Benchmark Lance vs Delta vs Iceberg on S3 metadata performance, update blob rows without reading bytes, and train world models directly from object storage, plus upcoming events and enterprise and community updates.

The page describes building an OCR pipeline for handwritten medical notes using DSPy, GEPA, and LanceDB to manage images, labels, outputs, metrics, and retrieval.

The post compares OpenSearch and LanceDB benchmarks on COCO 2017 images embedded with SigLIP, measuring ingestion throughput, query cost, storage layout, and overall infrastructure cost.

LanceDB offers an embedded experience and disk-native storage that scales to petabytes and production workloads.

The page contrasts Elasticsearch's JVM tuning, shard strategy, index templates, and re-indexing with LanceDB, an AI-native storage engine with native vector search.

The page compares Milvus, Zilliz Cloud, and LanceDB. Milvus uses a multi-service K8s stack, Zilliz Cloud uses pod-based billing, and LanceDB is a lightweight alternative that runs embedded or serverless.

Explore GraphRAG: hierarchical approach to retrieval-augmented-generation with practical insights and expert guidance from the LanceDB team.

The company responds to form submissions within 48 hours.

The multimodal lakehouse for AI, accelerating large-scale data curation and feature engineering so teams can build better models faster.

The multimodal lakehouse for AI, accelerating large-scale data curation and feature engineering so teams can build better models faster.

GitHubRepository owner

LanceDB builds open-source database software for multimodal AI models that work with images, videos, and text. The company raises $8 million in a seed round led by CRV, Essence VC, and Swift Ventures, bringing its total funding to $11 million. Its Lance Format is designed to handle large-scale multimodal data, and its customers include Midjourney, Character.ai, WeRide, and Airtable.

May 2024 · TechCrunchNews article