Weaviate
45 reviews
- 1 profiles
- 2 categories
Average star rating
4.4
Serving customers since
2019
Weaviate Reviews
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Subhashree S.
JAVA-ANGULAR || Full stack developmentWeaviate Makes Semantic Search and RAG Fast, Flexible, and Easy to Build
What I like most about Weaviate is how easy it is to build semantic search and RAG applications around it. The vector search is fast and flexible, and I like that it supports structured metadata along with embeddings, so I can narrow down results without making the retrieval logic overly complicated.
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anish k.
BCA 2nd Year Student | Passionate About Application development | Looking for Internships opportunitySeamless Hybrid Search That Speeds Up Production-Grade RAG
Its seamless hybrid search combining BM25 and vector search and native module integrations. It makes setting up, scaling, and retrieving data for production-grade RAG applications remarkably fast and easy.
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Muhammad O.
Salesforce Business Analyst | CRM & Sales Operations SpecialistWeaviate Makes Vector Search and Embeddings Simple
What I like most about Weaviate is how straightforward it makes working with vector search and embeddings. The setup feels intuitive, and it’s easy to store, search, and retrieve relevant data for AI applications without adding unnecessary complexity to my workflow.
About
What is Weaviate?
Weaviate is an open-source, AI-native vector database for building semantic search, hybrid search, RAG, and agentic AI applications. It stores data objects alongside vector embeddings and integrates with leading embedding and LLM providers. Weaviate is available self-hosted or as a fully managed service (Weaviate Cloud) on AWS, Google Cloud, and Azure, with a free tier, Query Agent for natural-language querying, and Engram for AI-agent memory. SOC 2 Type II certified, with HIPAA available.
Details
Year Founded
2019
Website
weaviate.io