Vector Databases and Embeddings — The Foundation of a Modern AI Stack

Want your AI system to understand your data? You need a vector database. Embeddings transform text into numerical vectors — and vector databases enable lightning-fast search across them.

What Are Embeddings¶

An embedding is a numerical representation of meaning. The words “car” and “automobile” have similar embeddings. OpenAI text-embedding-ada-002 is the most widely used model — 1,536 dimensions, solid quality. We’re also testing open-source alternatives.

Pinecone — Managed and Simple¶

A fully managed vector database. Zero ops, serverless pricing, excellent documentation. Ideal for getting started. Downside: data in the cloud, vendor lock-in.

Weaviate — Flexible and Open-Source¶

Open-source, self-hosted. Supports hybrid search (vector + keyword), GraphQL API. For enterprise clients with on-premise requirements, it’s the top choice.

ChromaDB — Lightweight for Prototypes¶

Ultra simple to set up. Pip install, a few lines of Python. Perfect for PoC. For production with millions of documents, reach for Weaviate or Pinecone.

How to Choose¶

• PoC / prototype: ChromaDB — minutes to first results
• SaaS product: Pinecone — zero ops, auto-scales
• Enterprise on-prem: Weaviate — full control, open-source
• Hybrid search: Weaviate or Elasticsearch with kNN

Vector Databases Are the New Standard¶

Every AI project today needs vector storage. Start with ChromaDB, migrate to Weaviate or Pinecone for production.