Qdrant Edge

Category: Vector Database Engines
Website: https://qdrant.tech/edge/
Vendor: Qdrant
Deployment: Embedded / on-device / edge environments
Status: Private beta

Qdrant Edge is a lightweight, in-process vector search engine optimized for embedded devices, autonomous systems, and mobile/edge agents. It enables on-device, low-latency vector retrieval with a small memory footprint and optional synchronization with Qdrant Cloud.

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Features

Architecture & Deployment

• In-process vector search engine

  • Runs as a library embedded directly into the application process.
  • No separate database service, no background threads, no runtime daemons.
  • Suitable for mobile apps, robots, and embedded systems where extra services are undesirable.

• Local-first operation

  • Retrieval and search run fully on-device and offline.
  • Designed for environments with intermittent or limited connectivity.

• Optional Qdrant Cloud synchronization

  • Can sync with Qdrant Cloud only when needed.
  • Supports use cases such as:
    • Data transfer between edge and cloud.
    • Promoting edge data to cloud tenants.
    • Coordinating large-scale or distributed deployments.

Performance & Resource Utilization

• Optimized for low-memory, low-compute hardware

  • Tailored to resource-constrained devices (embedded boards, low-power CPUs, etc.).
  • No idle overhead from extra processes or background services.

• Memory efficiency & compression

  • Built-in compression options to reduce memory footprint.
  • Ability to offload data to disk to further conserve RAM.

Search Capabilities

• Native vector search on-device

  • Real-time vector retrieval for edge AI workloads.
  • Suitable for latency-sensitive applications running directly at the edge.

• Hybrid & multimodal search

  • Supports dense vectors and multimodal embeddings.
  • Handles embeddings from:
    • Text
    • Images
    • Audio
    • Sensor-derived data (e.g., LiDAR, radar, other signals)

• Structured filtering

  • Combines vector similarity search with filters on structured payload fields.
  • Enables more precise retrieval based on both semantics and metadata.

Multitenancy & Workload Management

• Edge-scale multitenancy

  • Supports payload-based and shard-based tenant isolation.
  • Enables multiple logical tenants or datasets to coexist on constrained devices.

• Query routing across uneven workloads

  • Can route queries across tenants/shards to balance differing edge workloads.

SDKs & Platform Support

• Native SDKs for major edge platforms
  • Java SDK for Android.
  • Swift SDK for Apple platforms.
  • Additional SDKs planned or available for other environments.

Target Use Cases (On-Device AI)

• Robotics & autonomy
  • Multimodal retrieval from onboard sensors (e.g., LiDAR, radar, other robotic sensors).
  • Supports real-time decision-making and perception directly on the robot or autonomous system.

(The site implies broader use across mobile agents and embedded AI systems, but only partially listed examples are included here.)

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Pricing

• Not specified on the referenced content.
• Qdrant Edge is currently described as a private beta; access and pricing details are not publicly listed and likely require direct contact with Qdrant.