Milvus
Billion-scale vector search
The top alternatives to LanceDB in the Vector Databases space, compared on features, pricing, and what they're best at.
LanceDB is an open-source, AI-native multimodal lakehouse designed for billion-scale vector search. Founded in 2022 by Chang She and Lei Xu as part of Y Combinator's Winter 2022 batch, LanceDB is built on the Lance columnar format and combines embedded simplicity with cloud-scale performance. The platform enables users to store, query, and filter vectors, metadata, and multi-modal data (text, images, videos, point clouds, and more) with support for vector similarity search, full-text search, and SQL. LanceDB offers blazing fast hybrid search, filter, and rerank over billions of vectors with compute-storage separation for up to 100x cost savings. The platform includes zero-copy automatic versioning, allowing users to manage versions of data without needing extra infrastructure. LanceDB's disk-based architecture with compute-storage separation enables up to 100x cost savings compared to memory-based solutions while supporting multimodal data. Based in San Francisco with approximately 30 employees, LanceDB hit $2.3M in revenue with a 15-person team in 2024.
Milvus
Billion-scale vector search
Pinecone
Fully managed serverless vector database
Qdrant
High-performance open-source vector search
Chroma
Lightweight embedded vector database
Supabase
pgvector hosting
Weaviate
Open-source vector database
Neo4j
Native graph database with Cypher query language
MongoDB Atlas Vector Search
Elasticsearch
Redis Vector
ClickHouse
Analytics + vector
Vespa
Neon
Serverless Postgres
TigerGraph
Turbopuffer
SingleStore
ArangoDB
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