FAISS vs. KDB.AI

Compare FAISS vs. KDB.AI 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. KDB.AI on Scalability

FAISSKDB.AI

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

Neither.

Cloud-native

❌

Billion-scale vector support

❌

FAISS scalability

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

KDB.AI

KDB.AI is a scalable vector database.

FAISS vs. KDB.AI 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.

FAISSKDB.AI

Role-based Access Control (RBAC)

❌

Disk Index support

❌

Yes (qFlat and qHNSW)

Hybrid Search (ie Scalar filtering)

❌

Yes. Hybrid Sparse & Dense Search

Partitions/namespaces/logical groups

❌

Index type supported

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

Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW.

Multiple in-memory indexes support

❌

FAISS functionality

FAISS is an algorithm to support kNN search.

KDB.AI

Built by KX, a database provider known for time-series data management, KDB.AI enables developers to bring temporal and semantic context and relevancy to their applications. It supports various search types, including vector similarity search, hybrid sparse and vector search, and Non-Transformed TSS, a similarity search algorithm specific for time series data. It uses Cosine Similarity, Inner Product, and L2 Distance (Euclidean) for similarity metrics.

FAISS vs. KDB.AI 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

FAISSKDB.AI

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

Database rollback

❌

FAISS vs. KDB.AI: 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

KDB.AI

KDB.AI is a powerful knowledge-based vector database and search engine that allows you to build scalable, reliable AI applications using real-time data.

Proprietary license