Weaviate
Open-source vector database with native hybrid search combining BM25 and vector search
12K+
GitHub Stars
<100ms
P95 Latency
100+
Modules
Overview
Weaviate is an open-source vector database that pioneered native hybrid search, combining traditional keyword search (BM25) with vector similarity search in a single query. Built in Go for performance, Weaviate offers a modular architecture with pluggable vectorizers (OpenAI, Cohere, Hugging Face), rerankers, and generative modules. Its GraphQL API provides powerful querying capabilities including filtering, aggregation, and cross-references between objects. Weaviate can be self-hosted or run on Weaviate Cloud Services (WCS), making it popular with teams who need flexibility between managed and self-hosted deployments.
The Verdict
Who Should Use Weaviate?
Best For
- Teams needing true hybrid search (BM25 + vectors)
- Organizations requiring self-hosted deployment option
- Complex data models with relationships (GraphQL)
- Multi-modal search (text, images, audio)
- Teams wanting modular, extensible architecture
Not Ideal For
- Simple use cases (Chroma is easier)
- Zero-ops requirements (use Pinecone)
- Lowest possible latency needs (try Qdrant)
- Teams without infrastructure experience
What's Great
- Best-in-class hybrid search (BM25 + vector fusion)
- Fully open-source with active community
- Modular vectorizers (OpenAI, Cohere, HuggingFace)
- GraphQL API with powerful filtering and aggregations
- Multi-tenancy with data isolation
- Self-hosted or managed cloud options
Watch Out For
- Steeper learning curve than simpler alternatives
- Self-hosting requires DevOps expertise
- Cloud pricing can scale up quickly
- GraphQL complexity for simple use cases
- Module configuration adds setup overhead
Pricing
Sandbox
$0
14-day trial, perfect for testing
Serverless
Pay-per-use
$0.095/1M dimensions stored
Enterprise Cloud
Custom
Dedicated clusters, SLA, support
Self-Hosted
Free (OSS)
Run on your infrastructure
View all features & details
Core Features
- Hybrid search (BM25 + vector fusion)
- GraphQL & REST APIs
- HNSW vector indexing
- Inverted index for filtering
- Cross-references between objects
- Real-time CRUD operations
- Multi-tenancy support
- Horizontal scaling (sharding)
Vectorizer Modules
- text2vec-openai (GPT embeddings)
- text2vec-cohere
- text2vec-huggingface
- text2vec-transformers (local)
- multi2vec-clip (images + text)
- img2vec-neural (images)
- ref2vec (cross-references)
Generative Modules
- generative-openai (GPT-4, etc.)
- generative-cohere (Command)
- generative-palm (Google)
- generative-anthropic (Claude)
- RAG in a single query
- Grouped task execution
Deployment Options
- Weaviate Cloud Services (WCS)
- Docker / Docker Compose
- Kubernetes (Helm charts)
- AWS, GCP, Azure marketplaces
- Embedded Weaviate (in-process)
Community & Ecosystem
Community Stats
- 200+ contributors
- Active Slack community (10k+ members)
- Weekly office hours
- Comprehensive documentation
GitHub, 2025
Integrations
- LangChain & LlamaIndex native
- Haystack integration
- Python, JavaScript, Go, Java clients
- Vercel AI SDK
- Jupyter notebooks support
Weaviate.io, 2025
How It Compares
| Feature | Weaviate | Pinecone | Qdrant | Chroma |
|---|---|---|---|---|
| Deployment | Managed + Self-hosted | Managed only | Managed + Self-hosted | Self-hosted + Cloud |
| Hybrid Search | Native BM25 + vector | Basic sparse-dense | Good | Basic |
| API Style | GraphQL + REST | REST | REST + gRPC | REST |
| Multi-modal | Yes (CLIP, images) | No | Limited | No |
| Latency | 50-100ms | <50ms | <50ms | Variable |
| Modules | 100+ pluggable | Fixed | Limited | Limited |
| Free Tier | 14-day sandbox | 100K vectors | 1GB | Unlimited (self-host) |
| Best For | Hybrid search, flexibility | Zero-ops production | Performance + OSS | Prototyping |
User Reviews
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