FAISS vs Vespa

FAISS vs. Vespa

Compare FAISS vs. Vespa by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.

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FAISS vs. Vespa on Scalability

FAISSVespa

Separation of storage and compute

❌

Separation of query and insertions

No. Can not scale beyond single node.

Yes.

Multi-replication

❌

Dynamic segment placement vs. static data sharding

No distributed data replacement

Both

Cloud-native

❌

Billion-scale vector support

❌

FAISS scalability

Without any distributed data replacement, FAISS is not able to scale beyond a single node

Vespa

Vespa is a scalable search engine with a robust distributed architecture that supports horizontal scaling by adding more nodes. It features automatic sharding and data redistribution, allowing it to efficiently manage large datasets and high query volumes.

FAISS vs. Vespa 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.

FAISSVespa

Role-based Access Control (RBAC)

❌

Disk Index support

❌

Yes (paged tensor attributes)

Hybrid Search (ie Scalar filtering)

❌

Yes, vector search & keyword seach

Partitions/namespaces/logical groups

❌

Index type supported

FLAT, IVS_FLAT, IVF_SQ8, IVF_PQ, HNSW, BIN_FLAT and BIN_IVF_FLAT

HNSW, Hybrid HNSW-IF (Inverted File), paged tensor attributes

Multiple in-memory indexes support

❌

FAISS functionality

FAISS is an algorithm to support kNN search.

Vespa

Vespa is a powerful search engine and vector database that can handle multiple searches simultaneously. It's great at vector search, text search, and searching through structured data.

FAISS vs. Vespa 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

FAISSVespa

Purpose-built for Vectors

✔️

Yes.

Tunable consistency

❌

Support for both stream and batch of vector data

❌

Binary Vector support

❌

Multi-language SDK

Python, JavaScript

Python, Java

Database rollback

❌

FAISS vs. Vespa: 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

Vespa

Vespa is a powerful search engine and vector database that can handle multiple searches simultaneously. It's great at vector search, text search, and searching through structured data.

Apache 2.0