Prototyping vector search
memista is a fast way to prototype vector search: cargo add, run the server, insert vectors, query. Browsable OpenAPI docs and plain-file storage make it easy to inspect and throw away. Best under ~100k vectors.
Before committing to a vector database, you want to see retrieval working on your own data. memista is a fast way to spike that.
Why it’s good for a spike
cargo add memista, run the server, and you have three endpoints on127.0.0.1:8083.- Browsable OpenAPI docs at
/swagger,/redoc,/rapidoc,/scalarlet you poke the API without writing a client. - Storage is a
.dband a.usearchfile — inspect them, or delete them and start over.
Then decide
If the prototype confirms you need vector search at a scale beyond memista’s ~100k tested vectors, you’ve learned that cheaply and can move to a distributed store with confidence. If it confirms a single binary is the right shape, you’re already done. Compare the options: vs USearch, vs pgvector, vs Qdrant.
Related: quickstart, features.
Frequently Asked Questions
Is memista meant for production?
It is experimental (v0.1.x) and tested under ~100k vectors. It is excellent for prototyping and small production workloads where a single binary and two files are the right shape; validate carefully before larger scale.