Compare SingleStore and Weaviate side by side. Both are tools in the Vector Databases category.
Choose SingleStore if unified platform combining transactional, analytical, and vector workloads.
Choose Weaviate if you need multimodal search across text, images, and more.
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| 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 | singlestore.com | weaviate.io |
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Key criteria to evaluate when comparing Vector Databases solutions:
SingleStore is a real-time, unified, distributed SQL database that combines transactional, analytical, and vector data workloads in a single platform. MySQL and MongoDB wire protocol-compatible, SingleStore enables organizations to scale from one to one million customers, handling SQL, JSON, full text, and vector workloads all in one unified platform. Unlike traditional vector databases, SingleStore stores vector data in relational tables alongside other types of data, allowing easy querying of extended metadata and other attributes with the full power of SQL. The system supports both semantic search using FLAT, IVF_FLAT, IVF_PQ, IVF_PQFS, HNSW_FLAT, and HNSW_PQ vector indexes, with dot product and Euclidean distance for similarity matching. Founded in 2011 and headquartered in San Francisco, SingleStore serves hundreds of customers including 100+ Fortune 500, Forbes Global 2000, and Inc. 5000 brands. The company was acquired by Vector Capital in September 2025.
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.
Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.
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