Features

The first database built from the ground up for multi-vector operations. No compromises. No retrofitting.

Native Multi-Vector Architecture

VectorTree understands that documents, images, and videos are groups of vectors—not collections of independent points.

Store multiple vectors per logical entry
Preserve token-level semantic precision
No lossy compression to single vectors
Full meaning retention across all concepts

Grouping & Voting Algorithm

Our proprietary algorithm finds the most relevant logical entry, not just the most similar individual vector.

Group vectors by their parent entry
Vote across query-document matches
Return documents, not scattered vectors
Semantically correct result ranking

Blazing Fast Performance

Index at 413K vectors/sec and search at 486K vectors/sec on commodity hardware.

Index 1 billion vectors in ~40 minutes
~30,000 queries per second per core
No specialized hardware required
Runs on standard commodity servers

Perfect Recall

Achieve 100% Recall@1 for queries with 10+ vectors without sacrificing speed.

100% Recall@1 for 10+ vector queries
99.55% Recall@1 for 5 vector queries
No approximation compromises
Find exactly what you're looking for

Token-Level Precision

Match at the concept level, not the document level. Preserve the full semantic richness of your data.

Every concept gets its own vector
No information lost to compression
Disambiguate similar terms
2x better relevance vs single-vector

Commodity Hardware Ready

Achieve breakthrough performance without expensive infrastructure investments.

Runs on €95 commodity servers
No GPU requirements
Efficient memory utilization
Cost-effective scaling

Ready to experience multi-vector search done right?

Join the teams already building with VectorTree. Get early access to the future of vector search.

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