Skip to main content
Submit

Weaviate

Open-source vector database — hybrid search, multi-tenancy, and native LLM module integrations for RAG

Vector DBs & EmbeddingsFree

Weaviate is an open-source vector database designed for AI applications. It supports vector similarity search, BM25 keyword search, and hybrid search (combining both) — making it ideal for Retrieval-Augmented Generation (RAG). Weaviate's built-in module system integrates with OpenAI, Cohere, HuggingFace, and other embedding providers, enabling automatic vectorization during data ingestion. Available as a self-hosted deployment or Weaviate Cloud managed service.

Visit site

Maker of Weaviate? Get your db.fyi badge

What we know

PricingFreefrom our original listing, not yet re-checked

Why it matters

  • Hybrid search (vector + BM25) outperforms pure vector search for most RAG use cases — critical for production-quality retrieval where some queries are semantic and others are exact-match.
  • Built-in LLM modules auto-vectorize data on ingestion — point at OpenAI/Cohere/HuggingFace and skip manual embedding generation pipelines.
  • Open-source self-hosted option provides complete data privacy and cost control — critical for enterprises who can't send data to cloud vector databases.
  • Multi-tenancy support handles SaaS applications where each customer has isolated vector data.

Key capabilities

  • Vector search: Approximate nearest neighbor (ANN) search with HNSW indexing.
  • BM25 search: Traditional keyword/term frequency search.
  • Hybrid search: Combine vector and keyword search with configurable weighting.
  • Auto-vectorization: Native modules for OpenAI, Cohere, HuggingFace, and local model embedding.
  • GraphQL API: Query with GraphQL in addition to REST.
  • Multi-tenancy: Data isolation between tenants for SaaS applications.
  • Generative AI: RAG modules for generating answers from retrieved context.
  • Self-hosted: Run on Docker/Kubernetes; no vendor lock-in.
  • Weaviate Cloud: Managed service with free sandbox tier.
  • Python/JS SDKs: Official client libraries.

Technical notes

  • License: BSD 3-Clause
  • GitHub: github.com/weaviate/weaviate
  • Stars: 11K+
  • Self-hosted: Docker/Kubernetes; free
  • Cloud: Weaviate Cloud; Sandbox free (14-day TTL); paid from $25/month
  • API: GraphQL + REST
  • Embedding modules: OpenAI, Cohere, HuggingFace, Palm, custom
  • Languages: Python, JavaScript/TypeScript, Go, Java client libraries

Usage example

import weaviate
from weaviate.classes.init import Auth

# Connect to Weaviate Cloud
client = weaviate.connect_to_weaviate_cloud(
    cluster_url="your-cluster.weaviate.network",
    auth_credentials=Auth.api_key("your-api-key"),
    headers={"X-OpenAI-Api-Key": "your-openai-key"}  # for auto-vectorization
)

# Hybrid search
response = client.collections.get("Documents").query.hybrid(
    query="machine learning transformers",
    alpha=0.5,  # balance vector and keyword search
    limit=5
)

Ideal for

  • RAG applications requiring hybrid search for high-quality retrieval across semantic and keyword queries.
  • Enterprise teams who need self-hosted vector storage with full data privacy.
  • Multi-tenant SaaS applications where each customer's data must be isolated.

Not ideal for

  • Simple vector search without keyword matching needs — Pinecone or Qdrant may be simpler.
  • Teams wanting the simplest possible managed setup with no infrastructure thinking — Pinecone has less operational overhead.
  • Very high vector counts (100M+) without managed cloud support — self-hosted Weaviate requires careful capacity planning.

See also

  • LlamaIndex — RAG framework with native Weaviate integration; use together for full RAG pipeline.
  • Jina Embeddings — High-quality embeddings for populating Weaviate.
  • Haystack — NLP pipeline framework with Weaviate as a document store backend.

FAQ

What is Weaviate?

Weaviate is an open-source vector database that stores objects with their vector embeddings and provides fast similarity search. Key capabilities: vector search (find semantically similar objects), BM25 keyword search (traditional term matching), hybrid search (combine vector + keyword), multi-tenancy (separate data per tenant), and automatic vectorization via LLM modules. Used as the retrieval layer in RAG systems and semantic search applications.

Is Weaviate free?

Weaviate is open-source under the BSD 3-Clause license — free to self-host with no limitations. Weaviate Cloud (managed service) has a free Sandbox tier (14-day TTL clusters, 1M vectors), then paid plans starting at $25/month for small persistent clusters. Self-hosted Weaviate is completely free and used by many production teams on their own infrastructure.

How does hybrid search work in Weaviate?

Weaviate's hybrid search combines vector similarity (dense retrieval) and BM25 keyword matching (sparse retrieval) to score results. A configurable alpha parameter balances the two: alpha=0 is pure keyword, alpha=1 is pure vector, alpha=0.5 blends both equally. Hybrid search outperforms either approach alone for most RAG use cases — particularly for queries mixing semantic concepts with specific terms or product names.

How does Weaviate compare to Pinecone?

Pinecone is a fully managed vector database with simple API — no infrastructure to manage, easy scaling, but no self-hosting option and vendor lock-in. Weaviate offers both self-hosted (full control, no cost) and managed cloud. Weaviate has richer built-in features (GraphQL, hybrid search, LLM modules, multi-tenancy) but more operational complexity when self-hosted. Pinecone is easier to start with; Weaviate provides more control.

Alternatives

Integrations

None listed.

Built on

None listed.

Good for these tasks

Related tools

Listed 2026-02-22 · updated 2026-09-11