Pinecone
Managed vector database for AI applications
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Scalable vector database for semantic search and AI applications
Weaviate delivers fast, cloud‑native vector search with hybrid filtering, built‑in RAG, and flexible vectorization, supporting production‑grade scaling, multi‑tenancy, and role‑based access.
Weaviate is a cloud‑native vector database designed for developers, data scientists, and enterprises building AI‑powered applications. It stores objects alongside their vector embeddings, enabling semantic search at scale while also supporting traditional keyword filtering.
The platform offers automatic vectorization using integrated models from OpenAI, Cohere, HuggingFace, and others, as well as the ability to import pre‑computed embeddings. Its query engine combines vector similarity, BM25 keyword search, image search, and advanced filtering in a single API call. Built‑in Retrieval‑Augmented Generation (RAG) and reranking let you create sophisticated Q&A, chatbot, and summarization pipelines without external tooling. Clients are available for Python, JavaScript/TypeScript, Java, and Go, and the database can be accessed via REST, gRPC, or GraphQL.
Weaviate can be run locally with Docker, orchestrated with Kubernetes, or consumed as a managed cloud service. Production‑ready features include horizontal scaling, multi‑tenancy, replication, and fine‑grained RBAC. Vector compression and quantization reduce memory usage, lowering operational costs while maintaining high query performance.
When teams consider Weaviate, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Retrieval‑Augmented Generation for Q&A
Provides up‑to‑date answers by retrieving relevant documents and feeding them to LLMs directly from the database.
Semantic image search in e‑commerce
Enables shoppers to find visually similar products using combined text and image vectors.
Personalized recommendation engine
Generates real‑time item suggestions by matching user embeddings with product vectors.
Chatbot with context‑aware retrieval
Delivers coherent conversational responses by fetching relevant knowledge snippets via hybrid search.
Use the provided Docker‑compose file or run the official Docker image; the quick‑start guide walks you through starting the server and an optional embedding model.
Yes, configure the collection with self‑provided vectors and upload the embeddings directly.
Weaviate offers first‑party clients for Python, JavaScript/TypeScript, Java, and Go; a C# client is slated for release.
Weaviate supports horizontal scaling via Kubernetes, replication across nodes, and multi‑tenant deployments, plus a managed cloud offering.
Fine‑grained RBAC lets you define roles and permissions per collection, and multi‑tenant isolation ensures data separation between tenants.
Project at a glance
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