---
title: Real-time vector retrieval for Edge AI in resource-constrained environments
features:
- id: 0
  image:
    src: /img/edge-beta-features/native-vector.svg
    alt: Native Vector Search
  title: Native Vector Search for Embedded & Edge AI
  description: Runs as a lightweight, in-process library. No background threads, no services - ideal for mobile, robotic, and embedded environments.
- id: 1
  image:
    src: /img/edge-beta-features/optimized-for-low-memory.svg
    alt: Low-Memory
  title: Optimized for Low-Memory, Low-Compute Devices
  description: Dramatically Designed for resource-constrained hardware. No idle overhead, no runtime daemons. Fits into tightly scoped edge deployments. memory usage with built-in compression options and offload data to disk.
- id: 2
  image:
    src: /img/edge-beta-features/local-by-default.svg
    alt: Local by Default
  title: Local by Default, Cloud-Connected When Needed
  description: Retrieval runs fully offline. Sync with Qdrant Cloud only when required - for data transfer, tenant promotion, or coordination at scale.
- id: 3
  image:
    src: /img/edge-beta-features/hybrid-and-multimodal-search.svg
    alt: Hybrid & Multimodal Search
  title: Hybrid & Multimodal Search On-Device
  description: Supports dense and multimodal vectors with structured filtering. Enables real-time retrieval from text, image, audio, or sensor-derived embeddings.
- id: 4
  image:
    src: /img/edge-beta-features/multitenancy-built.svg
    alt: Multitenancy Built
  title: Edge-Scale Multitenancy with Native SDKs
  description: Supports payload- and shard-based tenant isolation. Routes queries across uneven edge workloads. Native SDKs in Java (Android), Swift (Apple), and more.
sitemapExclude: true
---