landing_page/qdrant-landing/content/edge/edge-features.md

title, features, sitemapExclude

title	features	sitemapExclude
Real-time vector retrieval for Edge AI in resource-constrained environments	id image title description 0 src alt /img/edge-beta-features/native-vector.svg Native Vector Search Native Vector Search for Embedded & Edge AI Run in-memory, disk-backed, and hybrid vector search on the edge. Deploy on mobile devices, IoT gateways, industrial PCs, drones, and more. id image title description 1 src alt /img/edge-beta-features/optimized-for-low-memory.svg Low-Memory Optimized for Low Memory & Low Compute Devices Optimized for resource-constrained environments with a small memory footprint and efficient CPU/GPU utilization to ensure smooth performance on edge devices. id image title description 2 src alt /img/edge-beta-features/local-by-default.svg Local by Default Local-first, Cloud-Connected When Needed Perform vector search locally with fallback to cloud for more complex queries or when more compute is needed to train your AI model. id image title description 3 src alt /img/edge-beta-features/hybrid-and-multimodal-search.svg Hybrid & Multimodal Search Hybrid & Multi-modal Search On Device Support for various data types, including text, images, audio, and more. Combine multiple modalities for more accurate and context-aware results. id image title description 4 src alt /img/edge-beta-features/multitenancy-built.svg Multitenancy Built Multitenancy Built for Edge Scale Designed to manage multiple tenants, users, or applications on a single edge device. Isolate data and control access for secure and scalable deployments.	true