VectorTree
The Multi-Vector Database
Engineered from the ground up for multi-vector workloads. Optimized for AI systems that need to manage complex, multi-faceted embeddings with unprecedented speed and precision.
For Applications Where Quality Matters
Multi-vector embeddings deliver more accurate and reliable outcomes by understanding content from multiple angles rather than relying on a single simplified representation.
The Multi-Vector Advantage
This leads to better matches, fewer false positives, and consistently higher-quality results—even in complex or ambiguous scenarios.
Better Matches
More accurate results by capturing multiple aspects of content, not just a single approximation.
Fewer False Positives
Reduce noise and irrelevant results in complex or ambiguous scenarios.
Higher Confidence
Consistently higher-quality results give users greater confidence in every decision.
The Trade-Off
These benefits come with increased cost and complexity that can quickly become a bottleneck at scale.
More Storage
Multiple vectors per entry require significantly more space than single-vector approaches.
Higher Compute
Indexing and retrieval operations are more expensive, requiring greater compute capacity.
Complex Scaling
Without purpose-built infrastructure, adoption becomes challenging at scale.
These trade-offs make adoption challenging—unless you have a solution designed to manage performance and cost efficiently.
The Trade-Off, Resolved
VectorTree is not another ANN library for single vectors.
Search
It is a database designed from the ground up to understand that a single entry—a document, an image, a video—is most accurately represented by a group of multiple vectors. It does not find the most similar vector. It finds the most similar logical entry.
Entry-Level Understanding
VectorTree treats documents, images, and videos as what they are: logical entries composed of multiple vectors working together.
Finds Entries, Not Vectors
Other systems find similar vectors. VectorTree finds similar entries—the actual documents you're searching for.
Speed Without Sacrifice
Achieve multi-vector accuracy at single-vector speeds. No approximation tricks. No precision loss.
Built for Scale
Index billions of vectors on commodity hardware. No specialized infrastructure required.
Built on a Proven Foundation
A Videntifier Technologies Company
VectorTree's technology is originated from Videntifier Technologies, a pioneer in visual identification and content recognition systems with over a decade of experience in research and operations of billions scale vector databases.
The foundation of the technology was born from the demanding requirements of enterprise-scale visual search systems. For years, we've been building and refining this core technology to power mission-critical applications that simply cannot afford to fail.
VectorTree has now transformed this battle-tested and production-hardened technology to a standalone product capable of handling any domain using multivector embeddings. VectorTree is built from the ground up for multi-vector workloads, offering capabilities that traditional vector databases simply can't match.
Battle-Tested at Scale
Processing billions of vectors daily in production environments. Our technology powers mission-critical systems that never sleep.
Enterprise-Grade Reliability
Built for applications that simply cannot afford to fail. Trusted by organizations where reliability is non-negotiable.
Over a Decade of R&D
Our core algorithms have been refined through years of real-world deployment, not just academic benchmarks.
Production-Hardened
Every edge case, every failure mode—we've seen it and solved it. VectorTree inherits lessons learned the hard way.
Ready to experience the difference?
Join the teams already building with VectorTree. Get early access to the future of multi-vector search.
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