What is the primary use case for Qdrant?

Qdrant is primarily used for vector similarity search, enabling applications like semantic search, recommendation engines, anomaly detection, and image/audio retrieval based on vector embeddings.

Does Qdrant support filtering data during vector search?

Yes, Qdrant offers powerful filtering capabilities, allowing users to combine vector similarity search with structural and categorical filters on associated metadata (payloads).

Is Qdrant open-source?

Yes, Qdrant is open-source and available under the Apache 2.0 License, allowing for free use and modification.

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Qdrant

Freemium

vectorai

Qdrant is an open-source vector search engine designed for high-performance similarity search and AI applications.

Try it out

Qdrant stores vector embeddings and their associated payloads, enabling lightning-fast similarity search through various indexing algorithms like HNSW. It offers a production-ready solution for large-scale vector search, supporting filters, payloads, and distributed deployment. Its key differentiation lies in its direct-to-disk architecture that minimizes RAM usage, making it cost-effective for large datasets, and its extensive ecosystem support for various programming languages and ML frameworks.

Pros

Efficient direct-to-disk architecture for large-scale vector storage, reducing RAM requirements.
Comprehensive filtering capabilities combined with vector search for precise query results.
Open-source and highly scalable, suitable for both local development and distributed production environments.

Cons

Can have a steeper learning curve compared to some simpler vector search libraries.
Resource-intensive for very small datasets where a simple in-memory approach might suffice.
Community support, while growing, might not yet be as extensive as older, more established databases.

Key features

High-performance vector similarity search with HNSW and other indexing methods.
Payload filtering and storage alongside vectors for richer search results.
Distributed deployment and replication for high availability and scalability.
Support for various vector data types and distance metrics.
Language-agnostic API and client libraries.

Integrations

Python client library (qdrant-client)Go client library (qdrant-go)Rust client library (qdrant-rust)Typescript/Javascript client library (qdrant-js)LangChainLlamaIndex

Target audience

AI/ML developers, data scientists, and engineers building applications requiring efficient vector similarity search, recommendations, semantic search, or advanced RAG systems.

Ratings & Reviews

0.0

Based on 0 reviews

Key Metrics

Active Users

10K+

Founded

2021

Headquarters

Berlin, Germany

Pricing Tiers

Qdrant Open Source

Self-managed, fully featured open-source vector database for individual developers and teams.

Free

Qdrant Cloud

Managed service offering Qdrant clusters with scalability, reliability, and support. Pricing based on usage metrics like vector storage, queries per second, and data transfer.

Custom

Frequently Asked Questions

Top Alternatives to Qdrant

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Weaviate

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