# How Plaud uses Zilliz

**under 200ms**: “Recall returns in under 200ms on average and under 800ms at P99”

As published on [zilliz.com](https://zilliz.com/customers/plaud). Captured by usedby on 2026-10-06.

- Company: [Plaud](https://www.usedby.ai/companies/plaud.md)
- Tool: [Zilliz](https://www.usedby.ai/tools/zilliz.md)

## What the story says

Plaud uses Zilliz Cloud as the retrieval layer behind its ContextOS system. It stores semantic vectors and scalar filter fields to support search across a user's recordings, Ask Plaud (RAG), and long-term personal memory for its agents.

Summary written by usedby from the source page, in English. The figures are those of Zilliz and Plaud, not ours.

> Recall returns in under 200ms on average and under 800ms at P99, giving the end-to-end Ask AI pipeline room to stay responsive even as data grows into the billions.

- **under 800ms**: “under 800ms at P99”

> AI is moving from answering one-off questions to agents that remember, which makes AI memory the heart of consumer AI products. Zilliz Cloud gives us a solid foundation we can trust for agentic memory retrieval at a massive scale, so our team can put its energy into product innovation and user experience, not the plumbing beneath it.
>
> Charles Liu, Co-founder & CTO

## What usedby checked

We compared the story with its live page on 2026-10-06.

- Checked: the figure under 200ms is printed word for word on the page, near the name of Plaud.
- Checked: the passage quoted above is copied word for word from the page, near the name of Plaud.
- Checked: Plaud uses Zilliz. Confirmed line. Latest check across sources: 2026-10-06.
- Not checked: the result itself. We quote it; we did not measure it.

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Source: https://www.usedby.ai/case-studies/plaud-zilliz · How we check: https://www.usedby.ai/methodology