Weaviate vs. LanceDB
Compare Weaviate vs. LanceDB by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Weaviate vs. LanceDB on Scalability
Yes.
No. Only scale at the server level.
Static sharding
No (static data sharding coming soon)
Weaviate scalability
With static sharding, if your data grows beyond the capacity of your server, you will need to add more machines to the cluster and re-shard all of your data. This can be a time-consuming and complex process. Additionally, imbalanced shards can introduce bottlenecks and reduce the efficiency of your system.
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).
Weaviate 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.
Furthermore, differences in insert rate, query rate, and underlying hardware may result in different application needs, making overall system tunability a mandatory feature for vector databases.
Coming soon
Yes. Sparse & Dense Vectors and Scalar filtering.
Yes, vector search & keyword search
1 (HNSW)
IVF-PQ, HNSW
(LanceDB adopts a disk-based indexing philosophy.)
Weaviate functionality
Weaviate uses two types of indexes to power the database. An inverted index, which maps data object properties to its location in the database and a vector index to support high performance querying. In addition, their hybrid search approach uses dense vectors to understand the context of the query and combines it with sparse vectors for keyword matches.
Weaviate 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, Java, Go
Python, Javascript/Typescript, and Rust
Weaviate vs. LanceDB: what’s right for me?
Weaviate
Weaviate is maintained by a single commercial company offering a cloud version of Weaviate. License: BSD-3-Clause 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
Compare Weaviate with others
The Definitive Guide to Choosing a Vector Database
Overwhelmed by all the options? Learn key features to look for & how to evaluate with your own data. Choose with confidence.