# ApertureDB

> Graph-vector DB for multimodal data (images/videos/docs/embeddings) with hybrid vector similarity + graph traversal + metadata/keyword filtering. Enables complex multimodal RAG queries. Combines FAISS vectors with graphs unlike pure vector DBs like Qdrant.

- **URL:** https://www.aperturedata.io/
- **Category:** Multimodal Vector Databases
- **Tags:** Multimodal, Graph Database, 2026 Trends, Vision Text, Clip Compatible, Hybrid Search
- **Updated:** 2026-04-29 11:31
- **Canonical page:** https://vectordb.ever.works/items/aperturedb

## Details

## Overview

ApertureDB is a graph-vector database purpose-built for multimodal data that unifies multimodal AI data into a single, scalable platform. It combines vector similarity search with graph-based relationships to enable comprehensive data management for AI applications.

## Hybrid Capabilities

- Vector search (FAISS) + graph relationships + keyword/metadata filters.
- Unified queries across modalities with reranking potential.

## Key Features

### Multimodal Data Support
- Stores and manages images, videos, documents, feature vectors (embeddings), and associated metadata
- Supports text, images, documents, audio, video, and other unstructured data types
- Enables storage of actual data alongside embeddings and metadata

### Dual Database Architecture
- Builds on top of Facebook's FAISS library for similarity search over n-dimensional feature vectors
- Uses in-memory graph database for application metadata, enabling knowledge graph creation
- Combines vector search with graph relationships for comprehensive querying

### Performance
- Lightning-fast multimodal vector search unifying vectors, metadata, and media in one database
- Badger Technologies increased vector similarity search performance by 2.5x with ApertureDB
- Sub-second search latency with metadata filtering

## Use Cases

- Multimodal RAG: Vector+graph retrieval for enriched LLM context.
- **Semantic Search**: Multi-modal similarity search across different data types
- **Visual Debugging**: Analyze and debug computer vision models
- **Smart Retail**: Annotation management and nearest neighbor search for retail applications

## Comparisons vs Pure Vector DBs (e.g., Qdrant)

| Feature | ApertureDB | Pure Vector DB |
|---------|------------|----------------|
| Graph Support | Native | No |
| Multimodal | Images/videos | Vectors only |
| Unified Query | Vector+graph | Vector-focused |

## Benefits

- Removes complexities of multimodal data management
- Enables AI application deployment 6-9 months faster
- Supports vector search and classification at runtime with customizable distance metrics
- Provides additional metadata constraints on K nearest neighbor searches

## Deployment

Available as a unified platform for industries including healthcare, retail, and finance, supporting both cloud and on-premises deployments.

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