85%
“85% reduction in data pipeline development and deployment time, cutting it from a week to just a day.”
As published on anyscale.com. Captured by usedby on Oct 7, 2026.
What happened
Runway uses Anyscale as its AI platform to run Ray-based distributed data processing and training workloads for its Gen-3 Alpha video generation model. It also uses Ray Serve to offload last-mile data preprocessing during training to a separate pool of compute.
Summary written by usedby from the source page, in English. The figures are those of Anyscale and Runway, not ours.
- 13x“13x faster model loading”
- 40-50“40-50 Runway engineers use Anyscale”
With Anyscale as their AI platform, Runway built and launched Gen-3 Alpha, their most advanced model to date, in the summer of 2024.
Anyscale enables us to push the boundaries of what’s possible in generative AI by giving us the flexibility to scale workloads seamlessly. This removes the risk around our infrastructure and allows our team to focus on innovation rather than infrastructure bottlenecks
Switching to Anyscale made Ray far easier to use. Now, researchers can just submit jobs and look at the dashboard in Anyscale without having to think about cluster management. It works out of the box.
What the story claims, and what we checked
We compared the story with its live page on Oct 7, 2026.
- The figure: 85%CheckedPrinted word for word on the page, near the name of Runway.
- The passage quoted aboveCheckedCopied word for word from the page, near the name of Runway.
- The numbers in our summaryCheckedEach one is printed on the page.
- Runway uses AnyscaleCheckedConfirmed line. Latest check across sources: Oct 7, 2026.
- The result itselfNot checkedWe quote it; we did not measure it.




