Learn how Weaviate's open source vector database works for AI search and RAG, plus its self-hosted and cloud pricing options.
Weaviate is an open source, AI-native vector database built for semantic search, retrieval-augmented generation, and other applications powered by large language models. Founded in 2019 by Bob van Luijt and Etienne Dilocker and headquartered in Amsterdam, the company has grown into one of the better-known independent vector database providers as demand for AI infrastructure has expanded.
The core Weaviate database stores both structured data objects and their vector embeddings together, allowing developers to combine vector similarity search with traditional filtering and keyword search in a single query rather than stitching together separate systems.
Weaviate is released under the Apache 2.0 license, making it free to self-host, while the company also operates Weaviate Cloud, a managed hosting service, and has raised significant venture capital, including a 50 million dollar Series B round in 2023, to fund product development.
Hybrid search combines vector similarity search with keyword-based BM25 search in a single query, improving relevance for use cases where pure semantic search alone falls short.
Built-in generative search modules connect retrieved results directly to large language models, streamlining retrieval-augmented generation pipelines without extra integration work.
Model integrations support popular embedding providers such as OpenAI, Cohere, and Hugging Face, letting developers generate and store vectors without managing separate embedding infrastructure.
Structured filtering allows queries to combine vector similarity with traditional metadata filters, such as category or date range, for more precise results.
Reranking support improves result ordering after initial retrieval, which is useful for RAG applications where result quality directly affects LLM output.
Multi-language client libraries in Python, JavaScript, Go, and Java make it accessible across common application development stacks.
Cloud-native architecture supports horizontal scaling and fault tolerance for production workloads handling large volumes of vector data.
The core Weaviate database is free and open source under the Apache 2.0 license, so developers can self-host it on their own infrastructure at no software cost, paying only for their own compute and storage.
Weaviate Cloud, the managed hosting option, includes a free sandbox tier for experimentation, followed by paid Standard tier plans generally priced in the range of 25 to 200 dollars per month, billed roughly by vector dimensions stored.
Higher Professional and Business Critical tiers, starting around 135 and 450 dollars per month respectively, add production-grade availability, support, and scaling for larger workloads, while large enterprise deployments are priced through custom quotes.
Yes, the core Weaviate database is open source and free to self-host under the Apache 2.0 license. Weaviate Cloud also offers a free sandbox tier alongside paid plans.
Weaviate is used to build semantic search, recommendation systems, chatbots, and retrieval-augmented generation applications powered by large language models.
Yes, Weaviate combines vector similarity search with keyword-based BM25 search in a single hybrid query.
Weaviate Cloud pricing generally ranges from around 25 to 200 dollars per month on the Standard tier, with Professional and Business Critical tiers starting around 135 and 450 dollars per month respectively, plus custom enterprise pricing.
Weaviate was founded in 2019 by Bob van Luijt (CEO) and Etienne Dilocker (CTO) and is headquartered in Amsterdam, Netherlands.
Weaviate integrates with popular embedding model providers such as OpenAI, Cohere, and Hugging Face, along with support for custom or self-hosted embedding models.
Yes, since the core database is open source under Apache 2.0, it can be self-hosted on a developer's own infrastructure as an alternative to using Weaviate Cloud.