· Generative AI · 1 min read
Pinecone vs Milvus vs Weaviate: Choosing a Vector Database
RAG requires a Vector DB. But do you need a managed service, an open-source cluster, or just a Postgres plugin?
The Vector Database is the long-term memory of your AI. Choosing the right one is critical for the speed and cost of your RAG application.
The Contenders
1. Pinecone (The Apple approach)
- Pros: Fully managed (SaaS), incredibly easy to start, scales infinitely.
- Cons: Proprietary, can get expensive at massive scale.
- Best For: Startups and Enterprises who want zero maintenance.
2. Weaviate / Milvus (The Power User)
- Pros: Open Source, can be self-hosted, features like Hybrid Search (keyword + vector) are very strong.
- Cons: You have to manage the cluster (Kubernetes).
- Best For: Tech-heavy teams who need customisation.
3. pgvector (The Boring Choice)
- Pros: It is just Postgres. If you already have Cloud SQL, you already have a Vector DB.
- Cons: Not as fast as the dedicated engines at massive scale (100M+ vectors).
- Best For: 90% of use cases. seriously.
Our Advice
Unless you have 100 Million documents, start with pgvector. Keeping your vectors next to your relational data gives you powerful join capabilities (“Find semantic matches, but only for users in the UK”).
Architecting a RAG platform? We help you choose the right stack. Contact our Data Architects.
