yooo this is awesome. Didn't even realise :)

If your use case is OLAP based, please check it out PuppyGraph. It’s a graph query engine that sits on top of your Lakehouse (no ETL required). Our benchmark has shown consistently that 10-hop queries across billions of edges in <2 seconds. Our customers including some most data demanding companies like Coinbase, Datadog, Palo Alto Network, Netskope, AMD, etc.

In prod we see p99’s of <10ms ms for warm queries and around 50ms per hop for cold queries.

We plan on launching end of month.

Yes you can put vectors, full text data, secondary and range indexes on both nodes and edges.

We see comparable results for vectors and FTS.

For vector search we have warm and cold p99s of approx 20ms and 400ms respectively. For FTS, warm and cold query p99s of approx 15ms and 250ms respectively.

Both of these benchmarks were run on 1m docs.

We don't support cypher or gremlin. We can

You can query HelixDB using JSON or directly in your programming language of choice by using our Rust, TypeScript, Go or Python SDKs. We’ve found AI is very good at working with the SDKs and JSON itself to query, making the development experience much better than before: https://docs.helix-db.com/database/querying

tpuffer is a vector/fts database. Surreal is a bit of an "everything database".

We're a graph database with vector and FTS capabilities. Our vector and FTS benchmarks are comparable with tpuffer, but you would primarily use us for building whole applications, knowledge graphs, or AI memory/retrieval. Anything that is relationship intense.

Let me know if this properly answers your question