Cloudflare Vectorize

Website: https://developers.cloudflare.com/vectorize/

Overview

Cloudflare Vectorize is a globally distributed, managed vector database integrated with Cloudflare Workers and Workers AI. It stores and queries vector embeddings (for text, images, audio, and other objects) to power search, similarity, recommendations, classification, and anomaly detection within applications running on Cloudflare’s developer platform.

Features

Core Vector Database

• Managed, globally distributed vector database service.
• Stores high-dimensional vector embeddings representing text, images, audio, and other objects.
• Designed for use with Cloudflare Workers for building full-stack AI-powered applications.
• Supports querying embeddings for search, similarity, recommendation, classification, and anomaly detection use cases.
• Can associate vectors with external data stored in:
  • Cloudflare R2 (for images and other unstructured data).
  • Cloudflare KV (for documents and key-value data).
  • Cloudflare D1 (for relational/user profile data).

Integrations with Workers and Workers AI

• Native integration with Cloudflare Workers to create databases, upload vectors, and run queries directly from serverless functions.
• Works with Workers AI to generate vector embeddings that can be stored and indexed in Vectorize.
• Supports using embeddings from third-party platforms such as OpenAI (bring-your-own embeddings).

Embedding Management & Search

• Store embeddings generated by Workers AI or external models.
• Query stored embeddings to enable semantic and similarity-based operations.
• Designed to power:
  • Semantic search and retrieval.
  • Content and product recommendations.
  • Classification tasks on custom datasets.
  • Anomaly detection based on embedding distances.

RAG & AI Search Integration

• Integrates with Cloudflare AI Search to support Retrieval-Augmented Generation (RAG) workflows.
• Automatically indexes data and stores it in Vectorize for subsequent retrieval.
• Enables querying stored vectors to generate context-aware responses via AI Search.

Developer Experience

• Documentation and guides for:
  • Creating a Vectorize database.
  • Uploading vector embeddings.
  • Querying embeddings from Cloudflare Workers.
  • Generating embeddings using Workers AI and using them with Vectorize.

Pricing

The provided content does not include pricing or plan details for Cloudflare Vectorize.