Overview
Description
Ray lets you scale AI and Python applications from a laptop to a cluster without rewriting your code. It provides a unified runtime for distributed training, serving, tuning, and data processing — so teams can move from prototype to production faster without managing separate infrastructure.
Technical scorecard
License
Apache-2.0
Commercial use
Yes
OpenAI-compatible API
No
REST API
No
Fine-tuning support
No
Quantization support
No
Docker available
Yes
GUI / no-code available
Yes
Telemetry
Unknown
Offline after setup
No
Data & Privacy
Does it send data online?
This listing is not marked offline after setup.
Does it store history?
Not verified in this directory yet. Review the upstream docs for persistence, logs, and workspace storage.
License checks?
Commercial use is marked as allowed or likely allowed by the listed license.
Telemetry?
Unknown
Last verified: Jul 22, 2026. Maintainer verification should be treated as directory guidance, not legal advice.
Deploying Ray in production?
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Request Privacy and Telemetry AuditSetup & Installation
A developer can usually get this running with standard docs.
# Start with the official project repository
# https://github.com/ray-project/rayHardware Requirements
Works Well With