The open-source vector database built for production RAG and semantic search at scale.
The open-source vector database built for production RAG and semantic search at scale.
Who it's for: ML engineers, AI app builders, and any team that needs semantic search, RAG, or hybrid retrieval over millions of vectors with low latency.
Weaviate's killer feature. Run semantic (vector) and keyword (BM25) search in a single query, fused with reciprocal rank. Best-of-both-worlds retrieval that beats pure vector on technical docs.
Native modules for OpenAI, Cohere, HuggingFace, Voyage, and Jina embeddings. Plus built-in rerankers (Cohere, Jina) — no glue code required. Just specify the module and it works.
Unlike most vector DBs that only have REST, Weaviate has a first-class GraphQL API. Get vectors, metadata, and generative outputs in one query. Plays beautifully with frontend stacks.
Built-in multi-tenancy with isolated shards per tenant. Horizontal sharding, replication, and backups. Powers production RAG at companies like Reddit, Salesforce, and Stack Overflow.
If you're building production RAG and want full control of your stack, Weaviate is the most capable open-source vector database in 2026. The hybrid search alone is worth the setup. For pure prototyping, Chroma is simpler. For 'just works at any scale' managed, Pinecone is still king.
The fully-managed vector DB. Zero ops, fastest scale.
The simplest vector DB for prototypes. In-memory or persistent.
Best-in-class embeddings + reranker for RAG pipelines.
Open-source embeddings, rerankers, and the model hub.