Qdrant Vector Database
Qdrant is an open‑source vector database designed for high‑performance similarity search and AI applications such as RAG, recommendation systems, advanced semantic search, anomaly detection, and AI agents. It provides scalable storage and retrieval of vector embeddings with features like filtering, hybrid search, and production‑grade APIs for integrating with machine learning workloads.
About this tool
Qdrant Vector Database
Website: https://qdrant.tech/qdrant-vector-database/
Qdrant is an open‑source vector database designed for high‑performance similarity search and AI applications such as retrieval‑augmented generation (RAG), recommendation systems, advanced semantic search, anomaly detection, and AI agents. It focuses on scalable storage and retrieval of vector embeddings with production‑ready APIs.
Features
- Open‑source vector database focused on high‑performance similarity search.
- Scalable vector storage and retrieval for large‑scale embedding‑based applications.
- Filtering support to combine semantic similarity with structured constraints.
- Hybrid search capabilities (vector search combined with other search modalities).
- Production‑grade APIs for integrating with machine learning and AI workloads.
- Supports AI/ML use cases including:
- Retrieval‑augmented generation (RAG)
- Recommendation systems
- Advanced semantic search
- Data analysis and anomaly detection
- AI agents
- Ecosystem & integrations (via documentation and GitHub) for developer workflows.
Use Cases
- RAG and large language model applications
- Personalized recommendation engines
- Semantic and advanced search over unstructured data
- Anomaly detection and data analysis pipelines
- AI agents and autonomous systems requiring fast vector retrieval
Pricing
A dedicated pricing page is available but detailed plan information is not present in the provided content.
More information: https://qdrant.tech/pricing/
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