Built in Rust for performance and reliability, this vector engine is optimized for low-latency nearest-neighbor search across high-dimensional embeddings.

It supports cloud-managed, hybrid, and edge deployment models and includes storage optimizations like compression and quantization to reduce memory use and enable cost-efficient scaling.

Typical applications include recommendation systems, retrieval-augmented generation, semantic search, anomaly detection, and AI agent tooling, with integrations for major embedding models and developer ecosystems.

Developers can run it locally via Docker, contribute or extend the open-source repo, or choose managed and enterprise offerings for production support and scalability.