Weaviate is an open-source vector database designed for semantic search, recommendation systems, and AI-powered retrieval — storing objects alongside their vector embeddings for fast similarity search across millions of items. Unlike traditional databases that search exact matches, Weaviate finds semantically similar content based on meaning. Used by AI application developers building RAG (Retrieval Augmented Generation) pipelines, semantic search, and personalization systems, Weaviate's hybrid search combines vector similarity with traditional BM25 keyword search for accurate results.
Weaviate
Open-source vector database for AI applications.
Best for: Developers building AI search, RAG or recommendation systems who want an open-source vector database with multi-modal support.
- Free plan
4.5/5 ratingEditorial score 87/100weaviate.io
Starts at
Free (14-day sandbox)
Get started free
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Editorial score87/100
The editorial note
What to know about Weaviate
Is it right for you?
Who Weaviate suits
Choose it if
- Developers building AI search, RAG or recommendation systems who want an open-source vector database with multi-modal support.
Look elsewhere if
- Teams wanting the simplest managed vector database experience — Pinecone provides a fully managed vector database with a simpler REST API and generous free tier that requires no infrastructure knowledge to get started
What you get
Key features
- Vector similarity search
- Hybrid vector + BM25 search
- Built-in embedding model integration
- GraphQL API
- Multi-tenancy
- CRUD operations on vectors
- RESTful backup API
- Weaviate Cloud Services
Works with
Integrations8
What it costs
Pricing
| Plan | Price | What it includes |
|---|---|---|
| Sandbox (Free) | Free (14-day sandbox)Billed monthly | Free sandbox cluster, all features, 14-day timeout, for development/testing |
| Standard | From $25 / monthBilled monthly | Production cluster, storage-based pricing, 99.9% SLA, monitoring, support |
| Business Critical | CustomBilled monthly | High availability, 99.95% SLA, HIPAA, SOC2, dedicated support, enterprise features |
Prices are recorded from the vendor's public pricing page and can change. Check before you buy.
The trade-off
Pros and cons
Pros
- Native hybrid search combines vector similarity and BM25 keyword matching for best retrieval accuracy
- Built-in ML model integration — runs embedding models directly in Weaviate without a separate step
- GraphQL API provides expressive, flexible queries for complex vector search use cases
- Open-source with self-hosting option — avoids vendor lock-in compared to Pinecone
- Weaviate Cloud Services provides fully managed hosting for teams not wanting to self-manage
Cons
- GraphQL-first API has a steeper learning curve than REST-based vector databases
- Resource-intensive for self-hosting at scale — requires significant memory for large vector indexes
- Less mature product support compared to Pinecone for high-scale production deployments
- Community is smaller than competing vector databases — fewer tutorials and third-party guides
Quick read
At a glance
- Category
- Development & Coding
- Pricing model
- Freemium
- Entry plan
- Free (14-day sandbox)
- Free plan
- Available
- Free trial
- Not listed
- Website
- weaviate.io
The verdict
Our take on Weaviate
The most feature-rich open-source vector DB — multi-modal and hybrid search making it ideal for complex AI search applications.
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