FAISS vs. LanceDB
Compare FAISS vs. LanceDB by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
FAISS vs. LanceDB on Scalability
Yes.
No. Can not scale beyond single node.
No distributed data replacement
No (static data sharding coming soon)
FAISS scalability
Without any distributed data replacement, FAISS is not able to scale beyond a single node
LanceDB
LanceDB is an open-source vector database that's designed to store, manage, query and retrieve embeddings on multi-modal data. LanceDB and its underlying data format, Lance, are built to scale to really large amounts of data (hundreds of terabytes, 200M+ vectors).
FAISS vs. LanceDB on Functionality
Performance is the biggest challenge with vector databases as the number of unstructured data elements stored in a vector database grows into hundreds of millions or billions, and horizontal scaling across multiple nodes becomes paramount.
Yes, vector search & keyword search
FLAT, IVS_FLAT, IVF_SQ8, IVF_PQ, HNSW, BIN_FLAT and BIN_IVF_FLAT
IVF-PQ, HNSW
(LanceDB adopts a disk-based indexing philosophy.)
FAISS functionality
FAISS is an algorithm to support kNN search.
FAISS vs. LanceDB on Purpose-built
What’s your vector database for?
A vector database is a fully managed solution for storing, indexing, and searching across a massive dataset of unstructured data that leverages the power of embeddings from machine learning models. A vector database should have the following features:
- Scalability and tunability
- Multi-tenancy and data isolation
- A complete suite of APIs
- An intuitive user interface/administrative console
Python, JavaScript
Python, Javascript/Typescript, and Rust
FAISS vs. LanceDB: what’s right for me?
FAISS
Faiss is a powerful library for efficient similarity search and clustering of dense vectors, with GPU-accelerated algorithms and Python wrappers, developed at FAIR, the fundamental AI research team at Meta License: MIT license
LanceDB
LanceDB is an open-source vector database that's designed to store, manage, query and retrieve embeddings on multi-modal data. It also provides a SaaS solution called LanceDB Cloud that runs serverless in the cloud.
Apache 2.0