Glossary · AI & Agents
Weaviate
Overview
How Weaviate works
Weaviate stores each item as an object plus a vector embedding — a numeric representation of its meaning. To answer a query, it converts the query into a vector and returns the objects whose vectors are closest, so results match by meaning rather than exact keywords. It can generate embeddings automatically via built-in modules or accept vectors you supply.
Key capabilities
Beyond pure vector search, Weaviate supports hybrid search that blends vector similarity with traditional BM25 keyword ranking, plus metadata filtering, multi-tenancy, and generative modules that pass retrieved context straight to an LLM. Written in Go, it is open-source and available self-hosted or through Weaviate Cloud, and it scales to billions of vectors.
Where it fits in a project
A vector database is the retrieval engine behind most RAG systems. When our team builds AI chatbots and knowledge assistants, Weaviate can hold a client's documents as embeddings so the assistant answers from their own content instead of guessing. We weigh it against alternatives based on scale, hosting preferences, and how much hybrid search and filtering a use case needs.
Where we use it
Related Zen in Tech services
How our team puts Weaviate to work in real projects.
FAQ
Weaviate — common questions
What is a vector database?
A vector database stores data as high-dimensional vectors (embeddings) and finds items by similarity rather than exact matches. This makes it ideal for semantic search, recommendations, and feeding relevant context to LLMs in RAG systems. Weaviate is one popular example.
Is Weaviate free?
Weaviate's core is open-source and free to self-host. Weaviate Cloud, its managed service, is a paid product with a sandbox or trial tier, and pricing scales with data volume and usage.
Weaviate vs Qdrant or Elasticsearch?
All three can do vector search. Weaviate and Qdrant are purpose-built vector databases with strong hybrid search and filtering; Elasticsearch is a mature search engine that added vector capabilities. The best fit depends on existing infrastructure, scale, and whether you also need full-text search.
Need Weaviate done right?
Book a free consultation and we’ll map the fastest, most cost-effective path for your project.
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