# Ray vs Replicate

**Leader by Vioscale score:** Ray

| Attribute | Ray | Replicate |
|---|---|---|
| **Vioscale score** | 66.8 (68% (medium)) | 52 (41% (low)) |
| activity.commits_last_30d | 100 | - |
| adoption.dependent_repos | 3,641 | - |
| adoption.github_stars | 43,612 | - |
| adoption.package_downloads_weekly | 11,291,327 | - |
| deployment.options | `{"cloud":true,"hybrid":true,"on_prem":true,"self_hosted":true}` | `{"cloud":true,"on_prem":true,"self_hosted":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. | Replicate is an infrastructure platform that lets you run state-of-the-art AI models through a simple API or deploy your own custom models. It automatically handles containerization, scaling, and resource allocation, charging only for compute used. |
| features.capabilities | `{"managed_cloud":true,"model_serving":true,"self_hostable":true,"multi_provider":true,"vpc_deployment":true,"framework_agnostic":true,"experiment_tracking":true}` | `{"role":"serving","compute_model":"serverless_per_token","managed_cloud":true,"model_serving":true,"pricing_model":"per_compute_hour","self_hostable":true,"multi_provider":true,"vpc_deployment":true,"no_train_on_data":"unknown","llm_observability":true,"framework_agnostic":true,"experiment_tracking":true,"bring_your_own_weights_byow_hosting":true,"multimodal_vision_and_audio_in_out_support":true,"lpu_or_custom_silicon_for_extreme_low_latency":false}` |
| integrations.count | 17 | 8 |
| 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":"Google"},{"name":"OpenAI"},{"name":"ByteDance"},{"name":"Black Forest Labs"},{"name":"HuggingFace"},{"name":"Anthropic"},{"name":"Alibaba"},{"name":"Krea"},{"name":"GitHub"},{"name":"Docker"}]` |
| language.primary | Python | - |
| license.spdx | Apache-2.0 | - |
| market.availability | - | `{"primaryMarkets":["US"],"availabilityScope":"global","availableCountries":[],"notAvailableCountries":[]}` |
| platform.support | `{"cli":true}` | `{"cli":true,"web":true}` |
| pricing | `{"type":"free","freeTier":true,"sourceUrl":"https://www.ray.io","retrievedAt":"2026-08-14T16:39:27.110Z"}` | `{"type":"usage","plans":[{"name":"Public Models","summary":"Per-token or per-output pricing varies by model","features":["Run public models","Text-to-image generation","Image editing and restoration","Video generation","Speech generation","Large language models","Music generation"],"components":[{"per":{"qty":1000,"unit":"output tokens"},"kind":"metered","amount":0.015,"currency":"USD"},{"per":{"qty":1000000,"unit":"input tokens"},"kind":"metered","amount":3,"currency":"USD"},{"kind":"per_unit","unit":"image","amount":0.04,"period":"per_output","currency":"USD"},{"kind":"per_unit","unit":"video","amount":0.09,"period":"per_second","currency":"USD"}],"description":"Thousands of open-source and proprietary models billed by execution time or output tokens/images","contactSales":false},{"free":false,"name":"Private Models","summary":"$0.09–$20.16/hr depending on hardware","features":["Dedicated hardware (no shared queue)","Auto-scaling","Custom model deployment","Hourly billing available"],"components":[{"kind":"per_unit","unit":"CPU (Small)","amount":0.000025,"period":"second","currency":"USD"},{"kind":"per_unit","unit":"CPU","amount":0.0001,"period":"second","currency":"USD"},{"kind":"per_unit","unit":"Nvidia A100 (80GB)","amount":0.0014,"period":"second","currency":"USD"},{"kind":"per_unit","unit":"Nvidia H100","amount":0.001525,"period":"second","currency":"USD"},{"kind":"per_unit","unit":"Nvidia L40S","amount":0.000975,"period":"second","currency":"USD"},{"kind":"per_unit","unit":"Nvidia T4","amount":0.000225,"period":"second","currency":"USD"}],"description":"Deploy custom models on dedicated hardware. Billed for all uptime (setup, idle, and processing)","contactSales":false},{"free":false,"name":"Fast Booting Fine-Tunes","summary":"Active processing time only","features":["Fine-tuned model deployment","No idle time billing"],"description":"Private fine-tuned models billed only for active processing time (no idle time charges)","contactSales":false}],"addOns":[{"name":"Multi-GPU A100 Capacity","components":[{"kind":"per_unit","unit":"4x Nvidia A100 (80GB)","amount":0.0056,"period":"second","currency":"USD"},{"kind":"per_unit","unit":"8x Nvidia A100 (80GB)","amount":0.0112,"period":"second","currency":"USD"}]},{"name":"Multi-GPU H100 Capacity","components":[{"kind":"per_unit","unit":"2x Nvidia H100","amount":0.00305,"period":"second","currency":"USD"},{"kind":"per_unit","unit":"4x Nvidia H100","amount":0.0061,"period":"second","currency":"USD"},{"kind":"per_unit","unit":"8x Nvidia H100","amount":0.0122,"period":"second","currency":"USD"}]}],"summary":"Usage-based: public models from $0.01–$0.25 per output token/image/second; private models from $0.09–$40.32/hr. Free tier available.","currency":"USD","freeTier":true,"sourceUrl":"https://replicate.com","retrievedAt":"2026-08-03T22:46:21.215Z","startingPrice":{"amount":0.000025,"period":"month","currency":"USD"}}` |
| pricing.free_tier | yes | yes |
| pricing.model | commercial | commercial |
| pricing.price_level | free | unknown |
| pricing.transparent | - | no |
| 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 | yes |
| security.scorecard | 5.7 | - |
| 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 | Replicate |
|---|:--:|:--:|
| **Core** |  |  |
| Tool role | - | Model serving |
| **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: Vioscale. Generated 2026-09-01T15:21:25.773Z. "-" = undocumented, not absent.*