Keywords AI

Qdrant vs Weaviate

Compare Qdrant and Weaviate side by side. Both are tools in the Vector Databases category.

Quick Comparison

	Qdrant	Weaviate
Category	Vector Databases	Vector Databases
Pricing	Open Source	Open Source
Best For	Engineering teams who need a fast, self-hosted vector database with strong filtering	Developers who need a flexible, open-source vector database with multimodal and hybrid search
Website	qdrant.tech	weaviate.io
Key Features	High-performance open-source vector search Written in Rust for speed Advanced filtering with payload indexes Distributed deployment support On-disk storage for cost efficiency	Open-source vector database Hybrid search (vector + keyword) Multi-modal support GraphQL and REST APIs Kubernetes-native deployment
Use Cases	High-throughput vector search Production RAG with complex filtering Self-hosted vector infrastructure Real-time similarity matching Cost-efficient large-scale deployments	Multimodal search across text, images, and more RAG with hybrid retrieval E-commerce product search Content recommendation engines Self-hosted vector search

When to Choose Qdrant vs Weaviate

Choose Qdrant if you need

High-throughput vector search
Production RAG with complex filtering
Self-hosted vector infrastructure

Pricing: Open Source

Choose Weaviate if you need

Multimodal search across text, images, and more
RAG with hybrid retrieval
E-commerce product search

Pricing: Open Source

How to Choose a Vector Databases Tool

Key criteria to evaluate when comparing Vector Databases solutions:

Query performanceSearch latency and throughput at your expected data scale and query volume.

ScalabilityHow well the database handles growing from thousands to billions of vectors.

Hosting modelFully managed cloud, self-hosted, or embedded options depending on your infrastructure needs.

Filtering supportAbility to combine vector similarity search with metadata filters efficiently.

Integration ecosystemNative integrations with popular frameworks like LangChain, LlamaIndex, and Haystack.

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.

View Qdrant profile →Visit website

About Weaviate

Weaviate is an open-source vector database that combines vector search with structured filtering and generative capabilities. It supports multiple vectorization modules, hybrid search (combining BM25 and vector search), and built-in integrations with LLMs for retrieval-augmented generation. Weaviate offers both self-hosted and managed cloud deployments, with a GraphQL API that makes it easy to query complex data structures.

View Weaviate profile →Visit website

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.