FAISS vs TiDB

FAISS vs. TiDB

Compare FAISS vs. TiDB 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. TiDB on Scalability

FAISSTiDB

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

TiDB

TiDB is designed with scalability as one of its core features. It offers both horizontal and vertical scaling capabilities to handle growing workloads and data volumes.

FAISS vs. TiDB 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.

FAISSTiDB

Role-based Access Control (RBAC)

❌

Disk Index support

❌

Hybrid Search (ie Scalar filtering)

❌

Yes, vector search & SQL search

Partitions/namespaces/logical groups

❌

Index type supported

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

HNSW

Multiple in-memory indexes support

❌

No. HNSW only

FAISS functionality

FAISS is an algorithm to support kNN search.

TiDB

TiDB offers vector search through its serverless cluster and supports vectors with a maximum dimension of 16,000. The Vector data type in TiDB is designed to store single-precision floating-point numbers (Float32). It only supports cosine distance and L2 distance for similarity measurement.

FAISS vs. TiDB 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

FAISSTiDB

Purpose-built for Vectors

✔️

No, vector search is an add-on to TiDB Cloud serverless.

Tunable consistency

❌

Support for both stream and batch of vector data

❌

Binary Vector support

❌

Multi-language SDK

Python, JavaScript

No. TiDB does not provide specific SDKs. Instead, it is designed to be compatible with MySQL, which means TiDB can be used with any language with MySQL client or driver support.

Database rollback

❌

FAISS vs. TiDB: 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

TiDB

TiDB is an open-source distributed SQL database for OLAP and OLTP workloads. It now offers a vector search capability (in public beta) as an add-on to its SaaS solution, TiDB Cloud Serverless.

Apache 2.0