Keywords AI
Vespa vs Weaviate
Compare Vespa and Weaviate side by side. Both are tools in the Vector Databases category.
Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | — | Open Source |
| Best For | — | Developers who need a flexible, open-source vector database with multimodal and hybrid search |
| Website | vespa.ai | weaviate.io |
| Key Features | — |
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| Use Cases | — |
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When to Choose Vespa vs Weaviate
- Multimodal search across text, images, and more
- RAG with hybrid retrieval
- E-commerce product search
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About Vespa
Vespa is an open-source search and recommendation engine combining vector search, full-text search, and structured queries.
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.
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.