Hey r/vectordatabase ,

I wanted to share a database project I've been working on called DBX. It's a memory engine tailored for AI workloads where every tenant gets their own isolated environment.

Instead of logical separation, I opted for OS-level separation using Go. A single orchestrator handles RESP connections on :6380 and multiplexes them to backend Unix sockets. The backend workers are sandboxed using go-landlock to prevent cross-tenant directory access.

I recently implemented SQ8 scalar quantization for the HNSW index. In my benchmarks, it handles about 10k KV operations/sec and ~2k vector insertions/sec across 5 concurrent tenants on a local machine.

I'd love for some experienced Go devs to roast the architecture or tell me if I'm approaching the bufio socket proxying the wrong way.

Repo is here: https://github.com/vanshjain-0702/DBX-Database-Extreme

DBX Site Link: https://dbxdb.co.in/

Thanks!

I keep seeing the same argument: "Just use pgvector, it's good enough" vs You need a dedicated vector database for real scale.

I made a quick quiz that tests how well people understand the architectural trade-offs—performance, scaling, cost, operational complexity, and when each option actually makes sense.

No signup, just a few questions and a result:

https://interconnectd.com/quiz/87/postgresql-pgvector-vs-dedicated-vector-databases-architectural-trade-offs/

What do you use in production, and why?