Comparisons with... ​

sqlite-vss

In general, sqlite-vss has the following unique features:

• Embeddable: No separate process, server, or configuration is needed, sqlite-vss runs in the same process as your application, just like SQLite.
• Pure SQL: There is no separate DSL or special API for inserting or querying vectors, it's all SQL.
• Configurable: You can specify different Faiss factory strings to customize how your vectors are indexed.
• Easy Distribution and Installation: sqlite-vss can be installed through several different package managers and languages, including Python, Node.js, Ruby, Deno, Go, rust, and more.

But it also has the following disadvantages:

• Doesn't scale to millions of users: A SQLite database can get you very far, but will eventually hit a limit with enough users or with extremely write-heavy applications.
• Depends on Faiss: Faiss makes it tricky to compile yourself and hard to use on certain platforms.
• Young in development: sqlite-vss's disadvantages listed on the README go over several implementation-specific problems you may face using sqlite-vss, mostly because sqlite-vss is young and has 1 main developer. Consider sponsoring my work to have these problems fixed!

This page goes over other vector databases and vector storage techniques that exist, and how sqlite-vss differs.

Vector Databases (Pinecone, Milvus, Qdrant, etc.) ​

In general, "real" vector databases like Pinecone, Milvus, Qdrant, and dozens of others will likely "scale" better than sqlite-vss. These databases perform writes faster and more efficiently than sqlite-vss, can handle a large number of users at the same time, and can perform metadata-filtering on queries.

On the other hand, sqlite-vss doesn't require an entire new server to maintain, is open source, and is most likely "fast enough" for many use-cases.

JSON or Pickle ​

Faiss ​

datasette-faiss ​

Chroma ​

pgvector ​

txtai ​