Ray
Distributed computing infrastructure for scaling machine learning applications
- Also known as
- ray
What is Ray?
An open-source library that abstracts away distributed systems complexity, enabling developers to run machine learning workflows across multiple machines and GPUs using familiar Python patterns.
Ray pricing
We don't have Ray's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
What Ray does
The capabilities that matter for mlops & llmops tools, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Tool role
- Agent / RAG framework
- Self-hostable / OSS core
- ✓
- Managed cloud available
- ✓
- On-prem / VPC deployment
- ✓
- LLM tracing / observability
- ✓
- Evaluation (offline / LLM-judge / human)
- -
- Prompt management + versioning
- -
- Experiment tracking / model registry
- ✓
- Model serving / inference endpoint
- ✓
- OpenTelemetry / OpenLLMetry compatible
- -
- Framework-agnostic
- ✓
- Multi-provider model support
- ✓
- No-train-on-customer-data guarantee
- -
Platform & deployment
Independently observed- CLI
- Cloud / SaaS
- Hybrid
- On-premise
- Self-hosted
Integrations (27)
Independently observed- Kubernetes•
- AWS•
- GCP•
- Azure•
- Databricks•
- DeltaLake•
- Spark⇄•
- Pandas⇄•
- PyTorch
- TensorFlow
- AIBrix
- AReaL
- Cosmos Curate
- Daft
- Data-Juicer
- DeltaCAT
- Modin
- NeMo Curator
- NeMo-RL
- OpenRLHF
- RayDP
- ROLL
- SkyRL
- SLIME
- Syftr
- verl
- vLLM
Security & compliance
Known vulnerabilities: 12 (6 in the last 12 months), max severity CRITICAL sourcea count reflects scale & disclosure, not quality
Ray FAQ
Common questions about Ray, answered from independent, dated evidence.
What is Ray?
Ray is an open-source unified framework for scaling Python and machine learning applications across any infrastructure. It provides distributed compute primitives, specialized AI libraries for data processing, model training, tuning, and serving, with seamless scaling from development environments to large clusters. It is indexed under MLOps & LLMOps Tools.
Source: https://www.ray.io
Is Ray free?
Ray offers a free tier, so you can start without paying. Pricing changes often, so verify at source before relying on it.
Source: https://www.ray.io
What platforms does Ray support?
Ray supports a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.
Source: https://www.ray.io
Can Ray be self-hosted?
Yes. Ray can be deployed cloud / SaaS, hybrid, on-premise and self-hosted, so it does not have to run on the vendor's infrastructure.
Source: https://www.ray.io
What does Ray integrate with?
We have confirmed 19 integrations for Ray, including PyTorch, TensorFlow, AIBrix, AReaL, Cosmos Curate, Daft, Data-Juicer and DeltaCAT, plus 11 more. This is what we could verify from public sources, so the vendor may support others we have not indexed.
Source: https://www.ray.io
Is Ray open source?
Yes. Ray is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.
Ray alternatives
Other mlops & llmops tools we track, ranked by the same independent score.
- PortkeyAn API gateway for routing requests across thousands of language models with built-in safety guardrailshigh · 76%
- LangChainA platform for building, testing, and operating AI agents at scalemedium · 70%
- BasetenAn inference platform for deploying and running AI models at scalemedium · 73%
- Together AICloud platform for deploying and running open-source AI models with optimized inferencemedium · 61%
- OllamaA platform for running open-source language models locally or in the cloud with cost-effective access.medium · 57%
- MLflowAn open-source platform for managing the full lifecycle of AI models, agents, and language models from development to productionmedium · 68%
Compare Ray
Side by side against other mlops & llmops tools, attribute by attribute, with a source on every value.
The vioscaleAI score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for Ray, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Package downloads | 92 | 0.14 | 12.5 | ✓ |
| Capabilities | 92 | 0.07 | 6.4 | ✓ |
| Development activity | 63 | 0.09 | 5.9 | ✓ |
| Release cadence | 90 | 0.05 | 4.7 | ✓ |
| Dependent projects | 59 | 0.06 | 3.7 | ✓ |
| Integrations | 41 | 0.08 | 3.2 | ✓ |
| Security score | 57 | 0.04 | 2.4 | ✓ |
| Stars | 88 | 0.03 | 2.3 | ✓ |
| Security posture | 0 | 0.06 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 100 | mediumsource · 2026-09-11 · 65% |
Adoption
Content
| Attribute | Value | Evidence |
|---|---|---|
| Faq | 6 items | mediumsource · 2026-09-10 · 61% |
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Role: framework · Managed cloud: Yes · Model serving: Yes · Self hostable: Yes · Multi provider: Yes · Vpc deployment: Yes | mediumsource · 2026-09-14 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 25 | mediumsource · 2026-09-14 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-09-11 · 98% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | mediumsource · 2026-09-11 · 67% |
Pricing
Release
Reliability
| Attribute | Value | Evidence |
|---|---|---|
| Status page | Yes | mediumsource · 2026-08-01 · 60% |
Security
Is Ray the right choice for you?
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