Open-source AI-native vector database for semantic, hybrid, and agentic search
Weaviate documents an open-source vector database at docs.weaviate.io/weaviate for storing objects and vector embeddings with semantic, keyword, and hybrid search, RAG, reranking, and agent workflows. The ecosystem includes self-hosted Docker/Kubernetes installs, Weaviate Cloud (console.weaviate.cloud), Query Agent, and Weaviate Embeddings for managed inference. Client libraries include Python (`weaviate-client` v4, requires Weaviate 1.23.7+), TypeScript, Go, and Java with REST, gRPC, and GraphQL APIs per the official documentation.
Use cases
- Production RAG catalogs with hybrid BM25 + vector retrieval
- Recommendation engines and semantic product search at billion-vector scale
- Agent memory and retrieval layers paired with Weaviate MCP tools
- Multi-tenant SaaS apps isolating customer data per tenant
- Migrating from prototype in-memory stores to cloud-native vector infrastructure
Key features
- Vector, keyword, and hybrid search with filtering in one query interface
- Integrated vectorizers for OpenAI, Cohere, HuggingFace, and other model providers
- Multi-tenancy, replication, RBAC, and quantization options for production
- Weaviate Agents (Query, Transformation, Personalization) on Weaviate Cloud
- Built-in MCP server from v1.37.1 at `/v1/mcp` per docs.weaviate.io/weaviate/mcp/mcp-server
Who Is It For?
- ML engineers building semantic search or RAG backends
- Platform teams operating vector databases with RBAC and replication
- Developers evaluating open-source alternatives to single-vendor vector clouds
Frequently Asked Questions
- Is Weaviate only available as a managed cloud?
- No—Weaviate is open source with Docker/Kubernetes self-hosting plus optional Weaviate Cloud.
- Which Python client version should I use?
- Docs recommend weaviate-client v4 (pip install -U weaviate-client) with Weaviate 1.23.7 or higher.
- How do MCP clients connect?
- From v1.37.1, enable MCP_SERVER_ENABLED=true and point clients to the built-in /v1/mcp endpoint.
Related
Related
3 Indexed items
Qdrant
Qdrant documents an AI-native vector search engine at qdrant.tech/documentation for storing, indexing, and querying high-dimensional vectors with optional payloads, supporting dense, sparse, and multi-vector configurations. Official guides cover Docker/Kubernetes self-hosting, Qdrant Cloud on AWS/GCP/Azure, Hybrid Cloud, Private Cloud, and Qdrant Edge for embedded retrieval. Client libraries include Python (`qdrant-client`), JavaScript/TypeScript (`@qdrant/js-client-rest`), Rust, Go, Java, and .NET with REST and gRPC APIs per the API reference at api.qdrant.tech.
Chroma
Chroma documents an open-source embedding database at docs.trychroma.com for storing and querying vectors, metadata, and full-text fields in Python and JavaScript clients. Official guides cover ephemeral in-memory collections, persistent local storage, self-hosted server deployments, and Chroma Cloud at trychroma.com with authentication tokens. The docs describe collection CRUD, `add`/`query`/`get`/`update`/`delete` APIs, embedding functions (default and third-party), hybrid search, and multitenancy patterns for RAG and agent memory workloads per the documentation index.
Supabase
Supabase documents an open-source Postgres development platform at supabase.com/docs providing a hosted backend with database, authentication, storage, Edge Functions, realtime subscriptions, and vector search for web and mobile apps. Client libraries and REST/GraphQL APIs let teams provision projects via the dashboard or CLI, manage Row Level Security policies, and integrate AI workflows through pgvector-backed tables and partner marketplace tools. Supabase positions the stack as a Firebase alternative built on standard Postgres, with local development via the Supabase CLI and typed client SDKs for JavaScript, Flutter, Swift, and other frameworks per the documentation index.