Weaviate

📖 What is Weaviate?

Weaviate is an open-source vector database capable of storing both objects and vector embeddings. It supports hybrid search (combining sparse keyword search with dense vector similarity), native model vectorization, and multi-modal media indexing.

💡 Why It Matters

Weaviate provides developers with the flexibility of self-hosting an enterprise-grade vector database or utilizing cloud hosting, making it a cornerstone for privacy-sensitive RAG architectures.

🎯 Workplace Use Cases

• Self-hosting vector storage for private enterprise medical and financial RAG apps
• Combining BM25 keyword search with dense vector embeddings for hybrid retrieval
• Indexing multi-modal data (images, text, audio) within a unified database schema
• Running local vector search pipelines using Docker containers

🚀 How to Use It

Run Weaviate via Docker (`docker run -p 8080:8080 semitechnologies/weaviate`) or create a cluster on Weaviate Cloud Services (WCS), then connect via Python/Go/JS client libraries.

✨ Key Features

• Open-source core engine with local Docker/Kubernetes deployment capability
• Hybrid Search engine combining BM25 keyword matching and dense vector search
• Native module integration for automated vectorization (OpenAI, HuggingFace, Cohere)
• Multi-modal embedding support (text, image, audio)

💳 Pricing & Plans

Free Tier: Open-source engine is free forever to self-host. Free 14-day cloud trial.

Plan	Price	Notes
Weaviate Cloud (Serverless)	Starting at $0.085 per 1M vector dimensions / month	Pay-per-use managed cloud hosting with zero infrastructure management.
Enterprise Dedicated	Custom pricing	Dedicated cloud clusters or self-hosted enterprise support SLA.