# How Handshake uses Anyscale

**50%**: “50% savings on cloud costs”

As published on [anyscale.com](https://www.anyscale.com/resources/case-study/how-handshake-saves-50-on-llm-gpu-costs-with-anyscale). Captured by usedby on 2026-10-07.

- Company: [Handshake](https://www.usedby.ai/companies/handshake.md)
- Tool: [Anyscale](https://www.usedby.ai/tools/anyscale.md)
- Industry: [EdTech & E-learning](https://www.usedby.ai/companies/industry/edtech-e-learning.md)
- Teams: Machine Learning, ML Infrastructure

## What the story says

Handshake uses Anyscale's managed Ray platform to train, fine-tune and host LLMs and deep learning recommender models such as graph neural networks and two tower recommenders. These power job matching, recommendations and LLM-generated content for students and employers.

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

> The team’s velocity has increased significantly with Anyscale, which is essential for lean teams that have to move fast. Anyscale provides a platform that has transformed Handshake’s data scientists into full-stack machine learning engineers with only a lean team of 1-2 ML infrastructure engineers supporting and maintaining it.

- **5x**: “5x faster iteration for AI workloads, accelerating time to market and innovation velocity”
- **\>50%**: “10x scalability and \>50% cost savings for LLM GPUs”
- **90%**: “90% higher engagement on jobs, a key business metric”
- **30%**: “while saving 30% on costs versus comparable A100 workloads”

> Iterating and scaling foundational embedding models (graph neural networks, two tower recommenders) trained over 100M-200M+ interactions was difficult and time-consuming until we migrated our feature producers and training jobs to Anyscale + Ray. Our experiment velocity with deep models and dependencies has 5x’ed while training on more data for cheaper. Anyscale has also enabled net-new real-time inference services and real-time user experiences that data scientists can roll out without waiting on ML Ops experts.
>
> Scot Fang, TLM, Machine Learning

## What usedby checked

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

- Checked: the figure 50% is printed word for word on the page, near the name of Handshake.
- Checked: the passage quoted above is copied word for word from the page, near the name of Handshake.
- Checked: Handshake 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/handshake-anyscale · How we check: https://www.usedby.ai/methodology