# Ray vs ReOpenly

| Attribute | Ray | ReOpenly |
|---|---|---|
| **vioscaleAI score** | 69.7 (64% (medium)) | 65.5 (40% (low)) |
| activity.commits_last_30d | 100 | - |
| adoption.dependent_repos | 3,641 | - |
| adoption.github_stars | 43,777 | - |
| adoption.package_downloads_weekly | 11,291,327 | - |
| content.faq | `[{"answer":"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","question":"What is Ray?","confidence":0.6},{"answer":"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","question":"Is Ray free?","confidence":0.6},{"answer":"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","question":"What platforms does Ray support?","confidence":0.6},{"answer":"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","question":"Can Ray be self-hosted?","confidence":0.6},{"answer":"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","question":"What does Ray integrate with?","confidence":0.6},{"answer":"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.","source":"https://github.com/ray-project/ray","question":"Is Ray open source?","confidence":0.67}]` | `[{"answer":"ReOpenly is an end-to-end platform that handles the infrastructure and training pipeline for fine-tuning open-source models on your dataset, then serving them via hosted API or downloading the weights for self-deployment. Smaller models tuned to specific tasks achieve comparable accuracy to large generalist models at significantly lower cost. It is indexed under MLOps & LLMOps Tools.","source":"https://reopenly.com/pt/pricing/","question":"What is ReOpenly?","confidence":0.6},{"answer":"ReOpenly offers a free tier, so you can start without paying. Paid plans start at $0.03 per GPU per minute. Prices are published openly on the vendor's own pricing page. Pricing changes often, so verify at source before relying on it.","source":"https://reopenly.com/pt/pricing/","question":"Is ReOpenly free?","confidence":0.6},{"answer":"We have confirmed browser-based access to ReOpenly. That is the extent of what we could verify from public sources, so it may well offer desktop or mobile clients we have not indexed.","source":"https://reopenly.com/pt/pricing/","question":"What platforms does ReOpenly support?","confidence":0.6},{"answer":"We have only confirmed a cloud / SaaS deployment for ReOpenly, so it appears to be vendor-hosted. If a self-hosted option exists we have not found it documented publicly.","source":"https://reopenly.com/pt/pricing/","question":"Can ReOpenly be self-hosted?","confidence":0.6},{"answer":"We have confirmed 3 integrations for ReOpenly, including LangChain, n8n and OpenAI. This is what we could verify from public sources, so the vendor may support others we have not indexed.","source":"https://reopenly.com/pt/pricing/","question":"What does ReOpenly integrate with?","confidence":0.6},{"answer":"ReOpenly is available worldwide. Its primary market is Brazil. The vendor is headquartered in Brazil.","source":"https://reopenly.com/pt/pricing/","question":"Where is ReOpenly available?","confidence":0.75}]` |
| deployment.options | `{"cloud":true,"hybrid":true,"on_prem":true,"self_hosted":true}` | `{"cloud":true}` |
| description.long | 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. | ReOpenly is an end-to-end platform that handles the infrastructure and training pipeline for fine-tuning open-source models on your dataset, then serving them via hosted API or downloading the weights for self-deployment. Smaller models tuned to specific tasks achieve comparable accuracy to large generalist models at significantly lower cost. |
| features.capabilities | `{"managed_cloud":true,"model_serving":true,"self_hostable":true,"multi_provider":true,"vpc_deployment":true,"framework_agnostic":true,"experiment_tracking":true}` | `{"role":"platform","evaluation":true,"managed_cloud":true,"model_serving":true,"self_hostable":true,"vpc_deployment":true,"no_train_on_data":"unknown","framework_agnostic":true}` |
| integrations.count | 17 | 3 |
| integrations.list | `[{"name":"PyTorch"},{"name":"TensorFlow"},{"name":"AIBrix"},{"name":"AReaL"},{"name":"Cosmos Curate"},{"name":"Daft"},{"name":"Data-Juicer"},{"name":"DeltaCAT"},{"name":"Modin"},{"name":"NeMo Curator"},{"name":"NeMo-RL"},{"name":"OpenRLHF"},{"name":"RayDP"},{"name":"ROLL"},{"name":"SkyRL"},{"name":"SLIME"},{"name":"Syftr"},{"name":"verl"},{"name":"vLLM"}]` | `[{"name":"LangChain","category":"framework"},{"name":"n8n","category":"automation"},{"name":"OpenAI","category":"llm"}]` |
| language.primary | Python | - |
| license.spdx | Apache-2.0 | - |
| market.availability | - | `{"hqCountry":"BR","primaryMarkets":["BR"],"availabilityScope":"global","availableCountries":[],"notAvailableCountries":[]}` |
| platform.support | `{"cli":true}` | `{"web":true}` |
| pricing | `{"type":"free","freeTier":true,"sourceUrl":"https://www.ray.io","retrievedAt":"2026-08-14T16:39:27.110Z"}` | `{"type":"usage","plans":[{"free":false,"name":"Pay-as-you-go","summary":"$0.03/GPU minute (training and inference)","features":["Fine-tune small open models","Hosted inference","OpenAI-compatible API","Auto-save dataset curation","Real-time training loss visualization"],"components":[{"kind":"metered","unit":"GPU","amount":0.03,"period":"minute","currency":"USD"}],"contactSales":false}],"summary":"Usage-based: $0.03 per GPU minute. New accounts receive welcome credits.","currency":"USD","freeTier":true,"sourceUrl":"https://reopenly.com/pt/pricing/","retrievedAt":"2026-09-08T13:40:11.997Z","startingPrice":{"unit":"GPU","amount":0.03,"period":"minute","currency":"USD"}}` |
| pricing.free_tier | yes | yes |
| pricing.model | commercial | freemium |
| pricing.price_level | free | low |
| pricing.starting_price | - | `{"amount":0.03,"currency":"USD"}` |
| pricing.transparent | - | yes |
| release.cadence_days | 18 | - |
| release.history | `[{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.58.0","date":"2026-08-23T05:42:08Z","type":"stable","version":"ray-2.58.0"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.57.0","date":"2026-08-11T01:15:44Z","type":"stable","version":"ray-2.57.0"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.56.1","date":"2026-07-17T23:19:47Z","type":"stable","version":"ray-2.56.1"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.56.0","date":"2026-06-29T20:32:55Z","type":"stable","version":"ray-2.56.0"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.55.1","date":"2026-04-22T20:24:41Z","type":"stable","version":"ray-2.55.1"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.55.0","date":"2026-04-15T20:34:27Z","type":"stable","version":"ray-2.55.0"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.54.1","date":"2026-03-25T23:37:39Z","type":"stable","version":"ray-2.54.1"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.54.0","date":"2026-02-18T23:44:44Z","type":"stable","version":"ray-2.54.0"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.53.0","date":"2025-12-20T15:16:24Z","type":"stable","version":"ray-2.53.0"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.51.2","date":"2025-11-29T00:40:40Z","type":"stable","version":"ray-2.51.2"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.52.1","date":"2025-11-28T02:23:22Z","type":"stable","version":"ray-2.52.1"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.52.0","date":"2025-11-21T19:10:39Z","type":"stable","version":"ray-2.52.0"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.51.1","date":"2025-11-01T03:27:11Z","type":"stable","version":"ray-2.51.1"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.51.0","date":"2025-10-29T05:33:49Z","type":"stable","version":"ray-2.51.0"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.50.1","date":"2025-10-18T19:21:43Z","type":"stable","version":"ray-2.50.1"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.50.0","date":"2025-10-10T23:06:29Z","type":"stable","version":"ray-2.50.0"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.49.2","date":"2025-09-19T18:10:36Z","type":"stable","version":"ray-2.49.2"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.49.1","date":"2025-09-03T00:44:11Z","type":"stable","version":"ray-2.49.1"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.49.0","date":"2025-08-26T19:52:24Z","type":"stable","version":"ray-2.49.0"},{"url":"https://github.com/ray-project/ray/releases/tag/ray-2.48.0","date":"2025-07-18T22:27:25Z","type":"stable","version":"ray-2.48.0"}]` | - |
| reliability.status_page | yes | - |
| security.scorecard | 5.7 | - |
| security.trust_center | - | https://reopenly.com/pt/privacy/ |
| security.vulnerabilities | `{"count":12,"source":"https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=ray&per_page=100","last_12m":6,"max_severity":"CRITICAL"}` | - |

## Capabilities (MLOps & LLMOps Tools)

| Capability | Ray | ReOpenly |
|---|:--:|:--:|
| **Core** |  |  |
| Tool role | - | End-to-end ML platform |
| **Deployment** |  |  |
| Self-hostable / OSS core | ✓ | ✓ |
| Managed cloud available | ✓ | ✓ |
| On-prem / VPC deployment | ✓ | ✓ |
| **Observability** |  |  |
| LLM tracing / observability | - | - |
| Evaluation (offline / LLM-judge / human) | - | ✓ |
| **Dev** |  |  |
| Prompt management + versioning | - | - |
| **Tracking** |  |  |
| Experiment tracking / model registry | ✓ | - |
| **Serving** |  |  |
| Model serving / inference endpoint | ✓ | ✓ |
| **Interop** |  |  |
| OpenTelemetry / OpenLLMetry compatible | - | - |
| Framework-agnostic | ✓ | ✓ |
| **Gateway** |  |  |
| Multi-provider model support | ✓ | - |
| **Data** |  |  |
| No-train-on-customer-data guarantee | - | Unknown |

*Source: vioscaleAI. Generated 2026-09-11T23:36:57.396Z. "-" = undocumented, not absent.*