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Cloudflare Vectorize vs Zvec

Cloudflare Vectorize

developers.cloudflare.com

Zvec

zvec.org

Side by side

FigureCloudflare VectorizeZvec
Price fromFreeFree
Free planYesYes
Free trial——
Open source——
WebYes—
Windows—Yes
macOS—Yes
Linux—Yes
iOS—Yes
Android—Yes
APIYesYes
Self-hosted—Yes
Plans published21
Spec score · Vector Databases8.0 (#8)7.5 (#20)
SupportCloudflare directs Vectorize users to its #vectorize Discord channel for questions, discussion, bug reports, and feedback.The documentation directs users to report issues on GitHub and lists community channels including DingTalk, WeChat, Discord, and X.

Plans

Cloudflare Vectorize

  • Workers FreeFree
  • Workers PaidPrice not published

Cloudflare Vectorize pricing →

Zvec

  • ZvecFree

Zvec pricing →

In their own words

Cloudflare Vectorize

Cloudflare Vectorize is a globally distributed vector database for AI applications built with Cloudflare Workers. It supports semantic search, recommendations, classification, anomaly detection, and supplying context to large language models. Vectors can come from Workers AI models or services such as OpenAI. Workers connect to indexes through a Vectorize binding configured in Wrangler or the Cloudflare dashboard, while a REST API can insert and query vectors. Search results can reference images in R2, documents in KV, and user profiles in D1. Metadata filters support indexed string, number, and boolean properties, with up to 10 metadata indexes per index. Each index can hold up to 20 million vectors, with a maximum of 1,536 dimensions at 32-bit precision. Queries return up to 50 results with values or metadata, or 100 without them. Cloudflare lists a free plan and paid usage from $5/mo. The Workers Free plan includes stated monthly queried and stored dimension allowances. Cloud deployment is available through API and web access.

Visit developers.cloudflare.com

Zvec

Zvec is an open-source, in-process vector database for indexing and querying vectors locally without a server or external infrastructure. It supports dense and sparse vectors, full-text search, and hybrid retrieval with structured filters. Listed index types include Flat, HNSW, IVF, and DiskANN, with RaBitQ variants also available; quantization options include FP16, INT8, INT4, RaBitQ, and PQ. Write-ahead logging provides persistence across process crashes or power failures. Multiple processes can read a collection at once, while writes are exclusive to one process. SDKs are listed for Python, Node.js, Go, Rust, Dart/Flutter, C, and C++. The README lists Linux, macOS, and Windows support, and prebuilt SDK binaries are published for Android and iOS as well. Named integrations include Logseq, MyContext, ReMe, txtai, and CocoIndex. The maker presents Zvec for application developers working on prototyping, local development, embedded and edge applications, or production systems. Its product family also includes Zvec-Grep, a local search CLI and MCP tool for code, documentation, and text-based data. It is available free under the Apache License 2.0.

Visit zvec.org