Today, we're putting a spotlight on one of the core components of our platform's power: Dify’s native support for Qdrant, the high-performance vector database. As an integral option within our suite of supported vector stores, we want to highlight why it’s a recommended choice for developers aiming to build faster and truly production-ready knowledge-based applications in Dify.
Choosing the right vector search tool is critical for performance. Dify provides developers the flexibility to select from several leading databases, and the seamless integration with Qdrant offers an enterprise-grade vector search engine for handling massive datasets without compromising on retrieval latency. It handles both the storage and recall of processed data, becoming the powerhouse for speed and accuracy for building agentic workflow and knowledge-based applications.
"Our mission at Dify is to remove the complexity of building production-grade AI applications," said Vince He, Head of Growth & Marketing of Dify. “Qdrant is invisible but powerful. Our users don't need to become vector search experts; they can simply choose Qdrant and immediately see the changes brought by an enterprise-grade, built-in-Rust vector search engine that’s powering their application's knowledge base."
Key benefits of using Qdrant within the Dify platform include:
Unmatched Search Speed: Qdrant is built in Rust for high performance, ensuring that AI applications can retrieve relevant context from millions of documents with minimal latency.
Advanced Metadata Filtering: Developers can now implement complex retrieval strategies by pre-filtering vectors based on metadata, resulting in more accurate and contextually-aware responses from LLMs.
Hybrid search capability: It’s simple to combine the best of keyword and semantic search to get the most relevant results. Implement hybrid search with one API call.
Production-Ready Scalability: Qdrant is engineered to scale, giving Dify users the confidence to move their AI applications from prototype to full-scale production without worrying about performance bottlenecks.
Simple Configuration: For developers using Dify’s community edition, they can configure their Dify applications to use Qdrant as the underlying vector store, giving them the freedom to choose for their knowledge base.
“Developers want to move from prototype to production without fighting infrastructure,” said Andre Zayarni, CEO of Qdrant. “Qdrant gives Dify users that path; an open-source, Rust-powered engine they can trust for low-latency retrieval, hybrid search, and massive scalability. And we can deploy anywhere: on open-source, cloud, hybrid cloud, private cloud, and even edge.”
For developers using Dify's open-source community edition, selecting Qdrant is a straightforward configuration choice. This allows them to instantly upgrade their application's knowledge base to a production-grade vector engine and gives them precise control over their application's performance stack.
As performance and scalability have become top priorities of today, we encourage you to build with Dify and Qdrant. Embrace an intuitive building experience and smooth, high-speed performance with the mindset that your application is always built on a solid foundation.
About Qdrant:
Qdrant is an open-source AI retrieval engine. It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload. Qdrant is tailored to be fast and reliable for the most demanding production environments. Visit their website for more information.
About Dify:
Dify is an open-source, intuitive agentic workflow builder designed to empower creators to build and operate powerful AI-native applications. With a focus on visual orchestration, RAG pipeline, plugins and observability, Dify simplifies the entire application development cycle, accelerating the process from prototyping to final production.
