Weaviate vs. FAISS
Compare Weaviate vs. FAISS by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Weaviate vs. FAISS on Scalability
No. Only scale at the server level.
No. Can not scale beyond single node.
Static sharding
No distributed data replacement
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.
FAISS scalability
Without any distributed data replacement, FAISS is not able to scale beyond a single node
Weaviate vs. FAISS 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.
1 (HNSW)
FLAT, IVS_FLAT, IVF_SQ8, IVF_PQ, HNSW, BIN_FLAT and BIN_IVF_FLAT
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.
FAISS functionality
FAISS is an algorithm to support kNN search.
Weaviate vs. FAISS 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
Weaviate vs. FAISS: 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
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
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.