Open-source vector database with ML
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29 reviews trackedThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Built-in vectorization
Biggest con
Learning curve
TL;DR - Weaviate
- Open-source vector database
- Run locally or in the cloud
- Filters combine with vector search
Pricing: Free plan available
Best for: Growing teams
4.6/5 across review platforms
What is Weaviate?
Weaviate is an open-source vector database for AI applications. Features hybrid search, dynamic indexing, and multi-tenancy for building semantic search and RAG systems.
Available on: Web
Pros & Cons
Pros
- Built-in vectorization
- GraphQL API
- Hybrid search
- Good documentation
- Open source
Cons
- Learning curve
- Memory intensive
- Self-hosting complex
- Cloud pricing high
- Less mature than Pinecone
Ratings Across the Web
4.6(29 reviews)
Ratings aggregated from independent review platforms. Learn more
Key Features
Vector databaseHybrid searchDynamic indexingCompressionMulti-tenancyRBACOpen-sourceAWS/GCP/Azure supportTechnical Account Team (Premium)Custom training
Pricing Plans
Pricing checked Jul 13, 2026
Free Trial
Free
- 14-day trial
- Sandbox cluster
- Hybrid search
- Dynamic indexing
- Compression
- Multi-tenancy
- Baseline RBAC
- Community support
Flex
$45
- Pay-as-you-go
- Shared cloud cluster
- Full core toolkit
- RBAC security
- 99.5% uptime
- Email support (next-business-day for critical)
Premium
$400
- Prepaid contracts
- Shared or dedicated deployment
- Up to 99.95% uptime
- Metrics endpoint
- AWS/GCP/Azure global coverage
- 1-hour Severity 1 response
- Technical Account Team
- Training and consultation
Reviews
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4.6/5
Across 29 verified user reviews on G2
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Weaviate FAQ
How does Weaviate support building AI applications?
Weaviate functions as an open-source vector database specifically designed for AI applications, offering features like hybrid search and dynamic indexing. It enables the creation of semantic search and Retrieval Augmented Generation (RAG) systems by managing vector embeddings efficiently.
Which teams would benefit most from using Weaviate?
Teams focused on developing AI applications, particularly those requiring semantic search or RAG capabilities, would find Weaviate beneficial. Its open-source nature and built-in vectorization are advantageous for developers building intelligent systems.
How is Weaviate's pricing structured?
Weaviate offers a free tier for users to get started, with paid plans available for those requiring more extensive usage and additional features. This model allows for scalability as application needs grow.
Can Weaviate perform complex searches beyond simple keyword matching?
Yes, Weaviate supports hybrid search, which combines different search techniques to deliver more relevant results. This capability is crucial for advanced AI applications like semantic search, allowing for nuanced querying.
What are the primary trade-offs when choosing Weaviate for a project?
Weaviate has a notable learning curve and can be memory intensive, which might require careful resource management. Additionally, while powerful, self-hosting can be complex, and cloud pricing for its services tends to be high.
How does Weaviate compare to Pinecone?
Weaviate is an open-source vector database with built-in vectorization and a GraphQL API, whereas Pinecone is a managed service. While both serve similar purposes in AI applications, Weaviate is considered less mature than Pinecone in the market.
Does Weaviate offer an API for integration with other tools?
Yes, Weaviate provides a GraphQL API, which facilitates integration with various applications and services. This API allows developers to interact with the database for tasks such as data retrieval and indexing.
Source: weaviate.io