PDFAITools
Weaviate
Open-source vector database that allows storing data objects with ML model embeddings for semantic search and generative AI.
What is Weaviate?
What is Weaviate?
Weaviate is an open-source vector database that stores data objects alongside their ML model embeddings. Unlike pure vector stores, Weaviate is a full-featured database with a rich query language, making it suitable for complex applications that need both vector search and structured data querying.
Unique Capabilities
- Native support for both vector and traditional object properties
- Built-in vectorization via model integrations (OpenAI, Cohere, Hugging Face)
- GraphQL and REST APIs for flexible querying
- Multi-modal support for text, images, and more
- Horizontal scalability with distributed architecture
Deployment Options
Weaviate can be self-hosted using Docker or Kubernetes, or used as a fully managed cloud service via Weaviate Cloud Services (WCS). This flexibility makes it a strong choice for teams with data sovereignty requirements as well as those who prefer a managed solution.
Key Features
Automatically vectorize data at write time using built-in model integrations.
Combine vector and BM25 keyword search for superior retrieval accuracy.
Query data with a flexible GraphQL interface supporting complex filters.
Store and search across text, images, and other data types in one database.
Scale horizontally with sharding and replication for high-availability deployments.
Who Uses Weaviate?
Build search systems that understand user intent across large document corpora.
Power RAG applications with reliable, scalable vector retrieval.
Enable similarity-based product search and recommendation in e-commerce.
Index and search large scientific datasets like protein structures or literature.
Pros & Cons
✅ Pros
- Open-source with active community and commercial support
- Built-in vectorization removes the need for external embedding pipelines
- Flexible deployment: self-hosted or fully managed
- Rich querying with GraphQL goes beyond simple nearest-neighbor lookups
- Strong multi-modal capabilities
❌ Cons
- More complex to set up than simpler vector stores like Chroma
- Resource-intensive for self-hosted deployments
- GraphQL API has a learning curve for developers unfamiliar with it
- Managed cloud pricing can be expensive at scale
Weaviate Pricing
Open Source
- Full feature set
- Self-hosted
- Docker & K8s
- Community support
Serverless
- Managed cloud
- 14-day free trial
- Auto-scaling
- Standard support
Enterprise
- Dedicated cluster
- SLA
- SSO
- Premium support
Weaviate earns a 4.3/5 rating from our editorial team. Its generous free tier lets you explore core features before upgrading, making it a low-risk choice for individuals and teams. Standout strengths include open-source with active community and commercial support and built-in vectorization removes the need for external embedding pipelines.
Get Started with Weaviate →