LanceDB
LanceDB is a serverless vector database built in Rust that runs embedded or in the cloud with native DataFrame integration for AI workloads.
LanceDB is a serverless, open-source vector database built in Rust for modern AI data workloads.
Key Features:
- Serverless Architecture - Run embedded in your application or in the cloud with zero server management.
- Multimodal Storage - Store and search text, image, video, and audio embeddings in a unified platform.
- Native Versioning - Track data changes with built-in versioning for reproducible AI pipelines.
- S3-Compatible Storage - Build on object storage for cost-effective, scalable persistence.
- DataFrame Integration - Work natively with Pandas and Polars for seamless data science workflows.
Whether you're building RAG over scraped content, training models on extracted data, or running multimodal search, LanceDB provides a simple, scalable foundation for your AI workloads.
Web scraping workflow
How to use LanceDB in a web scraping pipeline
LanceDB supports storing multimodal data and embeddings for search and retrieval. For a scraping pipeline, it can organize extracted text or media references together with the vectors used to find relevant content.
A practical workflow
- A separate crawler captures pages and extracts text chunks or media references with stable source identifiers.
- Your ingestion code generates or configures embeddings and writes rows with content, source URL, capture time and model information into a LanceDB table.
- A retrieval application searches that table and links matches back to the collected source material.
When it fits
Use this pattern when a collected corpus needs semantic retrieval while keeping data and derived features together. Recording the embedding model and content version makes later rebuilds easier to reason about.
Boundaries
LanceDB's embedded library and enterprise platform have different deployment scopes; choose the relevant documentation. Neither replaces the crawler or extraction validation. Chunking, embedding generation, refreshes and removal of obsolete content require explicit pipeline steps, and retrieved similarity is not a guarantee of factual relevance.
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Our productRepository
lancedb/lancedb
Stars
11.7KForks
1.1KLast commit
2026-10-15License
Apache-2.0Language
Rust
Metrics recorded 2026-10-16
Repository details
License source: Repository license file
Frequently asked questions
What does LanceDB do?
LanceDB provides a data layer for multimodal data, vector retrieval and search. Its OSS offering is an embedded retrieval library built on Lance; commercial offerings add managed or enterprise deployment capabilities.
Sources (checked ): LanceDB documentation; LanceDB OSS FAQ
Is LanceDB free?
LanceDB OSS is Apache-2.0 licensed and can run embedded in your application. Infrastructure and model services remain separate costs. Commercial Cloud and Enterprise offerings have their own plans and deployment terms.
Sources (checked ): LanceDB OSS FAQ; LanceDB repository
What are the differences between LanceDB and Lance?
Lance is a columnar lakehouse format with file, table and catalog specifications. LanceDB uses that foundation to provide retrieval and data-management interfaces. The storage format and the database/library built on it serve different roles.
Sources (checked ): Lance format; LanceDB OSS FAQ
What are the key differences between DuckDB and LanceDB?
DuckDB provides analytical SQL querying over tables and files. LanceDB is built around multimodal data and retrieval on Lance, including vector search. Both have embedded options, but compare their query interfaces and data-management roles for your application.
Sources (checked ): DuckDB introduction; LanceDB documentation; LanceDB OSS FAQ
What are some alternatives to LanceDB?
Chroma and Weaviate also support vector retrieval. Turbopuffer provides an object-storage-backed search service. Compare embedded versus hosted deployment, filtering, indexing and the required data formats rather than assuming identical interfaces.
Sources (checked ): LanceDB OSS FAQ; Chroma documentation; Weaviate platform; Turbopuffer documentation
Is LanceDB a better choice than Turbopuffer?
LanceDB includes an embedded OSS library and commercial platform options. Turbopuffer documents an object-storage-backed service. Choose according to deployment control, data layout, retrieval requirements and measured workload costs; neither is universally better.
Sources (checked ): LanceDB OSS FAQ; LanceDB documentation; Turbopuffer documentation
Sources
Verified against the lancedb/lancedb repository and the PyPI package.