OLAP queries, deep multi-hops where latency is a priority.
As long as the sub-graph you're trying to hop is cached, then there's no problem or latency issues. However, if you need to do a deep hop query, where all those nodes and edges are in cold storage, each hop costs ~50ms. So a 10-hop would take ~0.5 seconds.
Again though, we find most people are using us for agentic workloads, so even this worst case scenario the LLMs make up the majority of the latency.
We’re 100% committed to going back to open-source on an Apache 2.0 license as soon as possible. In the meantime, you can continue to deploy us completely for free, however you like, using the compiled docker container.
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
We’re just two young founders sharing what we’ve been building, so I’ll take the drive-by competitor plug as a compliment :)
Definitely a different focus though. Helix is OLTP, built for operational graph + vector workloads, especially apps/agent memory where low-latency traversals and writes are concerned.
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
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.
https://docs.helix-db.com/database/multi-tenancy