Lance Format

Lance Format is an open lakehouse format for multimodal AI that enables high-performance vector search, full-text search, and feature engineering on object storage.

Lance is an open source lakehouse format built for multimodal AI, supporting images, videos, audio, text, and embeddings alongside tabular data. It functions as a file format, table format, and catalog specification designed for object storage. The system accelerates vector similarity, full-text search, and SQL analytics using secondary indexes embedded in the format. It supports efficient schema evolution, allowing column backfills without full table rewrites. Lance integrates with various data tools, including Apache Spark, Ray, PyTorch, Trino, DuckDB, Pandas, and Polars.

Lance Format at a glance

Best for: Software engineer
Pricing: Free
Released: July 2022
Category: AI coding tool
Website: lance.org

Key features

Hybrid search combining vector similarity, BM25, and SQL analytics
High-speed random access with row addressing
Native storage for multimodal data including blobs and embeddings
Efficient schema evolution and column backfilling
ACID transactions and time travel support
Integration with open engines like Apache Spark and Ray
Compatibility with open catalogs such as Unity Catalog and Hive Metastore

Use it for

Serving machine learning models
Performing data sampling for interactive applications
Executing vector similarity search
Conducting full-text search and SQL analytics on datasets

Alternatives to Lance Format

LanceDB — Provides one table for the whole AI data loop and high-throughput data access.
Memory LanceDB Pro — Provides intelligent memory extraction and management for OpenClaw.
ModLens — Converts screenshots and charts into structured JSON data for coding agents.
Weaviate — Stores data objects and vectors for semantic and hybrid search capabilities.
zvec — It is a pure local, serverless vector database providing fast hybrid search.
seekdb — Unifies relational, vector, and text data for production-grade AI agents.
Muse Wonder — Explore performance benchmarks for Muse Code and Muse Spark.
Pinecone — Provides high-speed, governed knowledge retrieval for AI applications.
Serena — It offers symbol-level code retrieval and refactoring tools for AI agents.
HelixDB — Combines graph, vector, and full-text search in one engine.
LangChain4j — Integrates LLMs and vector stores directly into Java applications.
FastEmbed — It uses ONNX Runtime for high speed and does not require a GPU or PyTorch.

All Lance Format alternatives