# How Attentive uses Anyscale

**99%**: “With Anyscale, we were able to unify it into one model and reduce the cost by 99%”

As published on [anyscale.com](https://www.anyscale.com/resources/case-study/attentive). Captured by usedby on 2026-10-07.

- Company: [Attentive](https://www.usedby.ai/companies/attentive.md)
- Tool: [Anyscale](https://www.usedby.ai/tools/anyscale.md)
- Industry: [Marketing Tech](https://www.usedby.ai/companies/industry/marketing-tech-martech.md)
- Teams: ML Platform, ML Ops

## What the story says

Attentive uses Anyscale as its AI compute platform to train and serve machine learning models for personalized marketing, replacing a self-managed Kubernetes setup. Models are migrated one at a time, using Ray-based data processing, training and serving on a single platform.

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

> Before Anyscale, we didn’t have the ability to consolidate our data into a single model because we simply couldn’t process it all. With Anyscale, we were able to unify it into one model and reduce the cost by 99% while increasing the data volume for it by 12X.

- **5x**: “5x reduction in training time”
- **50x**: “50x increase in number of customers supported by models”
- **3 days**: “3 days to onboard engineers to 1st Anyscale Workspace”
- **12x**: “Able to process 12x more data”

> When we trialed Anyscale, we were amazed by how much just worked out of the box. The hosted workspace, the managed compute layer, autoscaling, spot instances… it would have taken us months to build what we got with Anyscale in 3 days.
>
> Christian Stano, Engineering Manager, ML Platform

## What usedby checked

We compared the story with its live page on 2026-10-07.

- Checked: the figure 99% is printed word for word on the page, near the name of Attentive.
- Checked: the passage quoted above is copied word for word from the page, near the name of Attentive.
- Checked: Attentive uses Anyscale. Confirmed line. Latest check across sources: 2026-10-07.
- Not checked: the result itself. We quote it; we did not measure it.

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