Keywords AI
ClickHouse vs Qdrant
Compare ClickHouse and Qdrant side by side. Both are tools in the Vector Databases category.
Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | freemium | Open Source |
| Best For | Teams needing analytics and vector search in one store | Engineering teams who need a fast, self-hosted vector database with strong filtering |
| Website | clickhouse.com | qdrant.tech |
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| Use Cases | — |
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When to Choose ClickHouse vs Qdrant
- High-throughput vector search
- Production RAG with complex filtering
- Self-hosted vector infrastructure
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About ClickHouse
High-performance analytical database with vector search. Standard for observability + embeddings.
About Qdrant
Qdrant is a high-performance open-source vector database written in Rust, optimized for speed and reliability. It supports advanced filtering with payload indexes, quantization for memory efficiency, and distributed deployments for horizontal scaling. Qdrant offers a managed cloud service and is popular with teams that need production-grade vector search with fine-grained control over indexing and query parameters.
What is Vector Databases?
Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.
Browse all Vector Databases tools →Frequently Asked Questions
What is a vector database?▾
A vector database stores high-dimensional numerical representations (embeddings) of data like text, images, or audio, and enables fast similarity search across millions or billions of vectors using approximate nearest neighbor algorithms.
Do I need a dedicated vector database or can I use pgvector?▾
For small to medium datasets (under 10 million vectors), pgvector in PostgreSQL works well and avoids adding another service. For larger datasets or when you need advanced features like hybrid search and real-time indexing, a dedicated vector database is recommended.
How do I choose the right embedding model for my vector database?▾
Match the embedding model to your use case. For general text search, models like OpenAI text-embedding-3 or Cohere embed-v3 work well. For domain-specific applications, consider fine-tuned models. Always benchmark with your actual data.