Vector embedding storage
Stores vector data for applications that need to represent content as embeddings and retrieve related information.
Cloudflare Vectorize
Freemium
Cloudflare Vectorize is a vector database for storing and retrieving vector embeddings in applications such as retrieval-augmented generation and semantic search. It runs within the Cloudflare Workers platform and supports vector lookups at the edge.
什么是 Cloudflare Vectorize？
Cloudflare Vectorize is a vector database for storing vector embeddings and retrieving relevant context in applications such as retrieval-augmented generation (RAG), semantic search, and other AI workflows. It provides vector storage within the Cloudflare Workers platform, so developers can use it as part of applications built on Cloudflare’s serverless compute environment.
Vectorize emphasizes edge vector lookups: queries are handled close to users rather than always returning to a centralized origin where data is stored. This positioning is intended to support lower-latency retrieval for globally distributed applications. The product is presented alongside Workers and Workers AI as part of a broader platform for application compute, data storage, and AI inference.
The source supports use in topic-specific chatbots, search applications, AI context injection, and edge AI workflows. Detailed setup steps, output formats, integrations beyond the Cloudflare platform, and operational limits are not established by the available product material.
Cloudflare Vectorize 能做什么？
Edge vector lookups
Provides vector database lookups from Cloudflare’s edge-oriented platform, placing retrieval closer to users than a centralized-origin workflow.
RAG context retrieval
Supplies relevant context from stored vector embeddings for retrieval-augmented generation applications, including topic-specific chatbots.
Semantic search foundation
Supports search applications that use vector similarity to find related content rather than relying only on exact keyword matches.
Workers platform placement
Runs within the Cloudflare Workers platform, allowing Vectorize to be used as part of applications built with Cloudflare’s serverless compute environment.
Workers AI workflow pairing
Can be combined with Workers AI to create edge-based AI workflows that retrieve vector context and use AI model inference.
使用场景
“Topic-specific chatbots”
Build chat experiences that retrieve information from a focused body of topical data stored as vectors before generating a response.
“Semantic search applications”
Power search interfaces that return conceptually related results using vector similarity, including cases where user wording differs from the indexed content.
“AI context injection”
Add relevant retrieved context to an AI application’s prompt or processing flow so model interactions can use application-specific information.
“Edge AI workflows”
Combine Vectorize with Workers and Workers AI to build an application workflow that performs retrieval and model inference within Cloudflare’s platform.
常见问题
What is Cloudflare Vectorize used for?
Vectorize is used to store vector embeddings and retrieve related context for applications such as retrieval-augmented generation, semantic search, topic-specific chatbots, and other AI workflows.
How does Vectorize fit with Cloudflare Workers?
Vectorize provides vector storage from within the Cloudflare Workers platform, so developers can use it as part of applications running on Cloudflare’s serverless compute environment.
Can Vectorize support AI applications?
Yes. The product is designed to provide relevant context from vector embeddings to AI applications. Cloudflare also describes combining Vectorize with Workers AI for edge-based AI workflows.
Does Vectorize provide natural-language search or a ready-made search interface?
The source positions Vectorize as vector storage and retrieval for semantic search and RAG. It does not establish that Vectorize itself provides the broader indexing, natural-language query, or ready-made interface capabilities described for Cloudflare AI Search.
How is Vectorize priced?
The product page lists a free allowance of 30 million queried vector dimensions per month and 5 million stored vector dimensions, followed by paid usage charges of $0.01 per million queried dimensions and $0.05 per hundred million stored dimensions. Check Cloudflare’s current pricing and documentation for applicable details and limits.
快速信息
- Category
- Vector database
- Platform
- Cloudflare Workers
- Primary uses
- RAG, semantic search, and AI context retrieval
- Related Cloudflare service
- Workers AI
- Free allowance
- 30M queried vector dimensions/month and 5M stored vector dimensions
- Pricing basis
- Queried and stored vector dimensions
Cloudflare Vectorize 替代品
Vespa.ai
vespa.ai
Vespa is an AI search platform for building search, retrieval-augmented generation, recommendation, personalization, and agent applications over text, vectors, tensors, and structured data. It is designed for developer teams that need configurable ranking and distributed operation at production scale.
Supabase
supabase.com
Supabase is a Postgres development platform for building applications with a database, authentication, APIs, realtime features, serverless functions, storage, and vector support. It helps developers move from application code to a managed backend through a dashboard, client libraries, CLI, and generated APIs.
Upstash Vector
upstash.com
Upstash Vector is a serverless vector database for storing and querying embeddings and metadata in AI and machine-learning applications. It supports dense, sparse, and hybrid indexes through a REST API and SDKs for Python, TypeScript, Go, and PHP.
Powabase
powabase.ai
面向 AI 应用的后端平台，集成 Postgres、身份验证、存储、实时功能、检索和智能体
Weaviate
weaviate.io
Weaviate is a vector database for building AI-native applications, including retrieval-augmented generation systems. It helps developers store and retrieve vector data, connect applications to language models, and deploy the database in a managed, self-hosted, or private-cloud environment.
Spice AI
spice.ai
面向数据密集型应用和 AI 智能体的开源 SQL 查询与混合搜索引擎，零 ETL 即可运行。