Comparison
pai vs ray
Verdict
Pick pai if pai is an open-source solution focused on resource scheduling and cluster management that supports deep learning frameworks including TensorFlow, PyTorch, and Chainer; pick ray if ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads.
Markdown twin · pai alternatives · ray alternatives
GraphCanon updated Sep 29, 2026
12views this month
Trust & integrity
| Signal | pai | ray |
|---|---|---|
| Maintenance | Active (11d since push) As of Sep 29, 2026 · github_public_v1 | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 29, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- pai
- Resource scheduling and cluster management for AI
- ray
- Unified framework for scaling AI and Python applications
Stars
- pai
- 2.7k
- ray
- 44k
Forks
- pai
- 554
- ray
- 8.1k
Open issues
- pai
- 260
- ray
- 3.6k
Language
- pai
- JavaScript
- ray
- Python
Adopt for
- pai
- pai is an open-source solution focused on resource scheduling and cluster management that supports deep learning frameworks including TensorFlow, PyTorch, and Chainer.
- ray
- Ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads.
Persona
- pai
- -
- ray
- -
Runtime
- pai
- -
- ray
- -
License
- pai
- MIT
- ray
- Apache-2.0
Last pushed
- pai
- Sep 18, 2026
- ray
- Sep 18, 2026
Categories
- pai
- Inference & Serving, Model Training
- ray
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Maintenance
- pai
- Active (82%)
- ray
- Very active (96%)
Days since push
- pai
- 11d
- ray
- 0d
Open issues (now)
- pai
- 260
- ray
- 3.6k
Stars delta
- pai
- +8 (30d)
- ray
- +337 (30d)
Open issues delta
- pai
- -22 (30d)
- ray
- +107 (30d)
Full report
- pai
- Trust report
- ray
- Trust report
Choose pai if…
- pai is primarily JavaScript; ray is Python.
- License: pai is MIT, ray is Apache-2.0.
- Tags unique to pai: ai, artificial-intelligence, gpu, kubernetes.
- When you are working with JavaScript-based projects and need to integrate model training or serving operations within your tech stack seamlessly
When NOT to use pai
- For organizations that prefer a more comprehensive suite tailored for specific languages other than JavaScript, as the tool's focus is clearly on this language environment
- When looking for solutions strictly hosted in cloud environments, as pai also supports deployment in on-premise settings which could complicate decisions if cloud dependency is critical
Choose ray if…
- ray is primarily Python; pai is JavaScript.
- License: ray is Apache-2.0, pai is MIT.
- Tags unique to ray: data-science, deep-learning, deployment, distributed.
- Also covers Developer Tools, Evaluation & Observability.
- When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs.
When NOT to use ray
- If your project is limited to single-node development and does not require scaling across multiple machines or clusters.
- When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU
- If you are working in a non-Python environment, as Ray is primarily designed for Python applications.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (microsoft/pai) · observed Sep 29, 2026
- GitHub forks (microsoft/pai) · observed Sep 29, 2026
- Last push (microsoft/pai) · observed Sep 18, 2026
- License file (MIT) · observed Sep 29, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (ray-project/ray) · observed Sep 20, 2026
- GitHub forks (ray-project/ray) · observed Sep 20, 2026
- Last push (ray-project/ray) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: pai 2.7k · ray 44k (synced Sep 29, 2026).
Common questions
- What is the difference between pai and ray?
- pai: Resource scheduling and cluster management for AI. ray: Unified framework for scaling AI and Python applications. See the comparison table for live GitHub stats and shared categories.
- When should I choose pai over ray?
- Choose pai over ray when pai is primarily JavaScript; ray is Python; License: pai is MIT, ray is Apache-2.0; Tags unique to pai: ai, artificial-intelligence, gpu, kubernetes; When you are working with JavaScript-based projects and need to integrate model training or serving operations within your tech stack seamlessly.
- When should I choose ray over pai?
- Choose ray over pai when ray is primarily Python; pai is JavaScript; License: ray is Apache-2.0, pai is MIT; Tags unique to ray: data-science, deep-learning, deployment, distributed; Also covers Developer Tools, Evaluation & Observability; When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs.
- When should I avoid pai?
- For organizations that prefer a more comprehensive suite tailored for specific languages other than JavaScript, as the tool's focus is clearly on this language environment When looking for solutions strictly hosted in cloud environments, as pai also supports deployment in on-premise settings which could complicate decisions if cloud dependency is critical
- When should I avoid ray?
- If your project is limited to single-node development and does not require scaling across multiple machines or clusters. When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU If you are working in a non-Python environment, as Ray is primarily designed for Python applications.
- Is pai or ray more popular on GitHub?
- ray has more GitHub stars (43,863 vs 2,694). Stars measure visibility, not whether either tool fits your constraints.
- Are pai and ray open source?
- Yes - both are open-source projects on GitHub (pai: MIT, ray: Apache-2.0).
- Where can I find alternatives to pai or ray?
- GraphCanon lists graph-backed alternatives at pai alternatives and ray alternatives (pai markdown twin, ray markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, pai or ray?
- pai: Active. ray: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for pai and ray?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: pai trust report; ray trust report.