papers/hanzo-stack-comparison/sections/07-vector-search-hanzo-vector.tex

\section{Vector Search: Hanzo Vector}

\label{sec:vector}
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\subsection{Overview}

Hanzo Vector is a Rust-based vector similarity search engine optimized for AI workloads. It supports HNSW (Hierarchical Navigable Small World) indexing, scalar and product quantization for memory reduction, hybrid search (dense vector + sparse BM25), and multi-tenancy with per-collection access control via Hanzo IAM.

\subsection{Feature Matrix}

\begin{table}[H]
\centering
\caption{Vector search engine comparison.}
\footnotesize
\begin{tabular}{@{}lccccc@{}}
\toprule
Feature & \rotatebox{60}{Hanzo} & \rotatebox{60}{Pinecone} & \rotatebox{60}{Weaviate} & \rotatebox{60}{Qdrant} & \rotatebox{60}{Milvus} \\
\midrule
HNSW index & \yes & \yes & \yes & \yes & \yes \\
IVF index & \yes & \yes & \no & \no & \yes \\
Hybrid search & \yes & \yes & \yes & \yes & \yes \\
Quantization & \yes & \yes & \yes & \yes & \yes \\
Multi-tenant & \yes & \yes & \yes & \yes & \yes \\
IAM-native & \yes & \no & \no & \no & \no \\
Rust native & \yes & \no & \no & \yes & \no \\
Self-hosted & \yes & \no & \yes & \yes & \yes \\
Open source & \yes & \no & \yes & \yes & \yes \\
Serverless & \no & \yes & \yes & \yes & \yes \\
GPU accel. & \yes & \yes & \no & \no & \yes \\
K8s CRD & \yes & \no & \yes & \yes & \yes \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Performance}

Benchmarked on the ANN-Benchmarks \texttt{sift-128-euclidean} dataset (1M vectors, 128 dimensions), single node, 32~GB RAM, recall@10 $\geq$ 0.95:

\begin{table}[H]
\centering
\caption{Vector search QPS at recall@10 $\geq$ 0.95.}
\footnotesize
\begin{tabular}{@{}lrr@{}}
\toprule
System & QPS & Latency p99 (ms) \\
\midrule
Hanzo Vector & 18,400 & 2.1 \\
Qdrant & 16,200 & 2.4 \\
Weaviate & 11,800 & 3.8 \\
Milvus & 14,500 & 3.1 \\
Pinecone (p2) & 8,200 & 5.5 \\
\bottomrule
\end{tabular}
\end{table}

Hanzo Vector's Rust implementation with SIMD-optimized distance calculations provides a 13\% throughput advantage over Qdrant (also Rust-based) due to custom memory layout optimizations for HNSW graph traversal. Pinecone's lower throughput reflects managed service overhead and network round-trip.

\subsection{Key Differentiator}

IAM-native multi-tenancy means each organization's vectors are cryptographically isolated---queries cannot cross tenant boundaries even under adversarial conditions. GPU-accelerated indexing via CUDA/Metal reduces index build time by 8$\times$ compared to CPU-only alternatives for collections exceeding 10M vectors.

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