# How Posit uses Baseten

**\<200ms** latency for inline edit suggestions in production

As published on [baseten.co](https://www.baseten.co/resources/customers/launching-positai-with-baseten/), March 2026. Captured by usedby on 2026-10-10.

- Company: [Posit](https://www.usedby.ai/companies/posit.md)
- Tool: [Baseten](https://www.usedby.ai/tools/baseten.md)
- Industry: [Data & Analytics](https://www.usedby.ai/companies/industry/data-analytics.md)
- Teams: AI Core Team

## What the story says

Posit hosts and serves the small fine-tuned LLMs behind its Next Edit Suggestions feature in RStudio on Baseten's inference stack, and used Baseten Training to run fine-tuning experiments before launching Posit AI.

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

> By deploying and optimizing the model on the Baseten Inference Stack, Posit achieved sub-200ms latencies, which was the critical threshold for the inline edit suggestion experience.

> Being able to stand up compute in a matter of minutes helped us to control costs while rapidly iterating on our training runs.
>
> Simon Couch, AI Core Team

Source: [baseten.co](https://www.baseten.co/resources/customers/launching-positai-with-baseten/), captured 2026-10-10.

## What usedby checked

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

- Checked: the figure \<200ms is on the page; its label is our wording.
- Checked: the passage quoted above is copied word for word from the page, near the name of Posit.
- Checked: the publication date is read from the page’s own metadata, never guessed.
- Checked: Posit uses Baseten. Confirmed line. Latest check across sources: 2026-10-10.
- Not checked: the result itself. We quote it; we did not measure it.

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