What Is Qdrant Edge?

Qdrant Edge is a lightweight, embedded vector search engine for in-process retrieval with a minimal memory footprint and no background services. Qdrant Edge is designed for applications requiring low-latency vector search in environments with limited or intermittent connectivity, such as robots, kiosks, home assistants, and mobile phones.

Unlike Qdrant Server, which uses a client-server architecture, Qdrant Edge runs inside the application process. Think of it as SQLite, but for vector search. Data is stored and queried locally, ensuring low-latency access and enhanced privacy since data does not need to be transmitted to an external server. That said, Qdrant Edge provides APIs to synchronize data with a Qdrant server. This enables you to offload heavy computations such as indexing to more powerful server instances, back up and restore data, and centrally aggregate data from multiple edge devices.

Qdrant Edge Shard

Qdrant Edge is built around the concept of an Edge Shard: a self-contained storage unit that can operate independently. Each Edge Shard manages its own data, including vector and payload storage, and can perform local search and retrieval operations.

To work with a Qdrant Edge Shard, use the Python Bindings for Qdrant Edge package or the qdrant-edge Rust crate. Both expose an EdgeShard type with methods to manage data, query it, and restore snapshots. To learn more about the available methods, refer to the Edge API page.

Using Qdrant Edge

More Examples

The Qdrant GitHub repository contains examples of using the Qdrant Edge API in Python and Rust.