Independent product intelligence · Worldwide · 2026-05-01 → 2026-07-31
client-vector-search enables developers to implement client-side semantic search using vector embeddings, offering fast computation and search capabilities directly in the browser.
Search demand
AI referral visibility
Product profile
Traffic makes more sense when you can connect it to the actual product promise.
“client-vector-search”
client-vector-search enables developers to implement client-side semantic search using vector embeddings, offering fast computation and search capabilities directly in the browser.
4 of 8 signal groups are available for this product: identity, authority and backlinks, AI referrals, search demand.
Not measured for this domain: traffic and engagement, audience demographics, country distribution, acquisition mix. Those sections are left out of the page rather than filled with estimates.
Canonical website: https://clientvectorsearch.com
Observed across 1 discovery source.
Research questions
Every answer is tied to a source we collected, and the questions with no evidence behind them are left unanswered rather than guessed.
It solves the problem of implementing semantic search in web applications without relying on server-side processing. By enabling vector computation and search directly in the browser, it eliminates the latency associated with traditional vector databases.
It offers client-side vector embedding computation, storage, searching, and caching. It can search up to 100,000 vectors in under 100 milliseconds and provides a simple JavaScript implementation requiring only a few lines of code. It also has a playground for testing.
The core library can be installed via npm. For scaling, there is an embedding API available for a fee of $20 per month, which allows embedding, storing, and searching up to 10 million vectors.
client-vector-search is a client-side vector embedding and search library for developers. It allows embedding, storing, searching, and caching vectors directly in the browser with semantic search implementation.
It is used to implement client-side semantic search for text data, such as articles or datasets. Developers can use it to embed text into vectors and perform fast searches directly within the browser.
It is for developers who need to add AI-powered search functionality to their web applications. The tool is categorized as a developer tool for implementing vector embedding and search in the browser.
Behind the lock
Worldwide estimates · Data period 2026-05-01 → 2026-07-31. Figures are measured estimates intended for market research. Signals we have not measured for this domain are omitted, never estimated; a measured zero is still reported as zero. How this was measured