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