Weaviate
Open-source vector database with built-in ML model integration. Weaviate supports hybrid search (vector + BM25), multi-tenancy, and can run fully self-hosted or managed.
BSD-3-Clause
TypeScript / Python
Why Weaviate?
You want open-source vector DB you can self-host
Hybrid search combining semantic + keyword
Multi-tenant SaaS applications
Signal Breakdown
What drives the Trust Score
Download Trend
Last 12 months
Tradeoffs & Caveats
Know before you commitSimplest possible managed setup (use Pinecone)
You need a serverless pay-per-query model
Your team has no DevOps capacity for self-hosting
Pricing
Free tier & paid plans
Free sandbox + open-source self-host
Managed cloud from $25/mo
Open-source means zero vendor lock-in
Alternative Tools
Other options worth considering
Managed vector database purpose-built for AI applications. Fully hosted with serverless scaling and a free tier (100k vectors). The fastest way to add semantic search or RAG to production.
High-performance vector database written in Rust. Qdrant delivers exceptional speed and memory efficiency for large-scale vector search with a clean REST and gRPC API.
Often Used Together
Complementary tools that pair well with Weaviate
Learning Resources
Docs, videos, tutorials, and courses
Get Started
Repository and installation options
View on GitHub
github.com/weaviate/weaviate
npm install weaviate-clientpip install weaviate-clientQuick Start
Copy and adapt to get going fast
import weaviate from 'weaviate-client';
const client = await weaviate.connectToWeaviateCloud(
process.env.WEAVIATE_URL,
{ authCredentials: new weaviate.ApiKey(process.env.WEAVIATE_API_KEY) }
);
const collection = client.collections.get('Article');
const result = await collection.query.nearText(['AI news'], { limit: 5 });
console.log(result.objects);Code Examples
Common usage patterns
Hybrid search
Combine vector and keyword search
const result = await collection.query.hybrid(
'quantum computing breakthroughs',
{ limit: 10, alpha: 0.75 } // alpha=1 pure vector, alpha=0 pure BM25
);Self-hosted with Docker
Run Weaviate locally
# docker-compose.yml
version: '3.4'
services:
weaviate:
image: cr.weaviate.io/semitechnologies/weaviate:latest
ports: ['8080:8080', '50051:50051']
environment:
QUERY_DEFAULTS_LIMIT: 25
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
DEFAULT_VECTORIZER_MODULE: noneCommunity Notes
Real experiences from developers who've used this tool