Anyscale
Managed Ray platform for distributed training, batch inference and data processing on GPU clusters.
Infrastructure by Anyscale
Chalk Labs
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Install
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pip install ray
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About
Use cases: model-inference, data-pipelines
Reviews
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Fact sheet
- Category
- Inference · model serving
- Self-host
- Yesanyscale.com
- Open source
- Yesgithub.com
Plans
| Pay as you go | Contact sales | usage-based, billed per instance-hour in Anyscale Credits, e.g. NVIDIA L4 0.9542 per hour |
|---|---|---|
| Committed contracts | Contact sales | volume discounts and reservations, talk to sales |
From the vendor's pricing page, read Oct 24, 2026.
Facts read from the vendor's own pages by claude-haiku-5-5 on Oct 24, 2026; a fact is shown only with the page it came from.
Signals
- Stars
- 44,115
- Forks
- 8,193
- Open issues
- 2,832
- Last commit
- Oct 24, 2026 (6 days ago)
- Last release
- Oct 2, 2026 (29 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- Packages
- pypi
ray - License
- Apache-2.0
- Repository
- github.com/ray-project/ray
- Website
- www.anyscale.com
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
Done for you
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Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
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Cite this page
Anyscale. Agent Mart. https://agentmart.dev/infrastructure/anyscale (last updated 2026-10-24)