BlogsQdrantReal-time Computer Vision at Scale

Real-time Computer Vision at Scale

Real-time Computer Vision at Scale

1
posts
2025

Dragonfruit AI has successfully scaled its real-time computer vision platform using Qdrant. The solution leverages a Split AI architecture, processing inference on edge devices and utilizing Qdrant in the cloud for vector search. Key aspects include multi-camera person re-identification, self-checkout product verification, and full-frame semantic search. Qdrant's per-collection configurability, efficient numeric formats (float16), and open-source nature were crucial for meeting strict latency targets and managing large-scale vector data across thousands of locations. This enabled faster development and deployment of new AI agents for various industries.

2025

How Dragonfruit AI scaled real-time computer vision with Qdrant - Qdrant

11/13/2025

This post details how Dragonfruit AI implemented a real-time computer vision system by integrating Qdrant. It highlights the challenges of scaling computer vision at 'messy, planetary scale' with bandwidth constraints and heterogeneous environments. The solution architecture involves edge inference and cloud-based Qdrant for vector search, enabling capabilities like multi-camera person re-identification and semantic search. Key technical choices included using Qdrant's per-collection configurability for diverse workloads and employing float16 embeddings for memory efficiency and performance. The post emphasizes the operational aspects of managing retrieval at enterprise scale and the benefits of Qdrant in achieving real-time performance and cost-effectiveness.