What Is Qdrant?

Qdrant is an open source project, founded in 2021, that’s gained popularity for functions that depend on high-dimensional vector search. High-dimensional vectors—that is, vectors that represent data in spaces with many dimensions—deliver advanced analytical and predictive capabilities for AI-driven tasks, such as similarity search, natural language processing, and image analysis. These tasks depend on finding complicated patterns and relationships within data sets. But handling high-dimensional vectors using a tool such as Qdrant comes with performance challenges and requires specialized algorithms and techniques. In addition, you’ll still need a complex architecture that includes a separate database that’s designed to store, index, and retrieve high-dimensional vector data. There are simpler options for handling large data sets and mission-critical workloads.

What Is Qdrant?

Qdrant is an open source vector database and a similarity search engine that specializes in storing and searching high-dimensional vectors to enable accurate and efficient similarity searches. It offers cloud and on-premises deployment options and can handle large volumes of data and complex queries.

In the modern system of neural networks, vectors of different sizes and shapes are used, with the type of vector depending on the use case. Qdrant supports sparse, dense, multivectors, and named vectors—the most common vector types currently employed.

Beyond the vector data types, Qdrant is also able to create a quantized representation of the vectors. This allows Qdrant to efficiently rescore the original vectors or go directly to search. This process is done while quantization occurs behind the scenes, which makes the entire process optimized and efficient.

Explore How Oracle Excels in Data Control and Consistency

Qdrant specializes in vector search and has significantly improved performance for enterprises using retrieval-augmented generation. It can be a great starting point because it’s open source and affordable for organizations just getting started with semantic search and recommendation engines.

To improve accuracy, Qdrant uses a descriptive metadata set, called a payload, that can be attached to each vector to provide additional context. However, these payloads must be structured as JSON. In contrast, Oracle Database 23ai allows JSON, graph, text, relational, spatial, and more data types to be returned for a single query. In addition, the information is not supplementary to the vector; it can be a filter of its own with many more data types.

Open source databases, like Qdrant, can allow enterprises to begin exploring vector search use cases at an affordable price because they don’t carry the licensing fees associated with proprietary solutions. But while Qdrant can handle high-performance vector search with large data sets, managing and maintaining those data sets gets complicated and expensive. When using Qdrant, most companies will need to supplement with other databases to manage diverse data types and metrics. Oracle Database 23ai, on the other hand, can bring vector search into your existing enterprise data management framework while handling multiple data types, and the largest data sets.

If you’re looking to experiment with vector search, an open source database, such as Qdrant, can be an affordable entry point, despite a fairly steep learning curve. It’s designed to be flexible and adaptable, allowing businesses to easily integrate it into their existing data infrastructures using APIs and SDKs. But as with any technology choice, your specific needs, data mix, and infrastructure choices will determine which database is right for your organization. For many businesses, vectors are an entirely new yet critical data type to store, administer, and retrieve. They need to choose between specialized, purpose-built databases, like Qdrant, or multimodal databases, such as Oracle Database 23ai, which support all data types in one repository and are suitable for enterprise-grade, mission-critical applications—where data integrity and complex querying capabilities are vital.

Qdrant FAQs

What is Qdrant used for?

Qdrant is a vector database and similarity search engine that efficiently stores, manages, and searches high-dimensional vectors. It’s designed for vector search and doesn’t offer strong transactional guarantees like a traditional relational database.

How does Qdrant ensure real-time vector search performance?

Through an advanced search algorithm, called hierarchical navigable small world (HNSW) graphs, Qdrant finds the closest neighbor to search for and provides live data updates. This means that new updates, including insertion, deletion, and any modification, can be immediately searched to get modified results in real time.

Can Qdrant be integrated with existing AI/ML pipelines?

Yes, Qdrant can integrate with machine learning frameworks, and a hybrid cloud platform allows for integration with AWS, Google Cloud, Oracle Cloud Infrastructure (OCI), and more.

What are the deployment options for Qdrant?

Qdrant has multiple deployment options, including local, cloud, and hybrid. Locally deployed, Qdrant provides easy setup using Docker at no additional charge. Qdrant Cloud provides scalability and easy access. Integrated with Kubernetes clusters, Qdrant Cloud provides the scope for a managed service.

How does Qdrant compare to other vector search engines?

Qdrant is a vector database that specializes in storing and searching high-dimensional vectors. It has been found to provide optimal results and continues to be a leading candidate for companies that are specifically targeting vector search engines and vector databases.