- Primary focus
- Managed platform for Ray, scaling AI/ML and Python workloads from a laptop to a cluster.
- Deployment
- Self-hosted
- Source model
- Other license
- Pricing model
- paid
- API available
- Yes
- Windows desktop
- Not specified
- Mobile app
- Not specified
- Overview
- Anyscale is the company behind Ray, the open-source distributed computing framework born at UC Berkeley's RISELab. Its managed platform runs training, batch inference, and LLM serving workloads on autoscaling Ray clusters, hosted or in your own cloud (BYOC).

| Attribute | ||
|---|---|---|
| Overview | ||
| Primary focus | Managed platform for Ray, scaling AI/ML and Python workloads from a laptop to a cluster. | Managed inference, fine-tuning and GPU clusters for open models |
| Deployment | Self-hosted | Not specified |
| Source model | Other license | Other license |
| Pricing model | paid | paid |
| API available | Yes | Yes |
| Windows desktop | Not specified | Not specified |
| Mobile app | Not specified | Not specified |
| Overview | Anyscale is the company behind Ray, the open-source distributed computing framework born at UC Berkeley's RISELab. Its managed platform runs training, batch inference, and LLM serving workloads on autoscaling Ray clusters, hosted or in your own cloud (BYOC). | Together AI runs an API platform for inference and fine-tuning of open-source models, plus dedicated GPU cluster rentals, positioned as an alternative to building your own model-serving infrastructure. |
| Explore further | ||
| Vendor website | Visit vendor ↗ | Visit vendor ↗ |
| ITHub profile | View full profile → | View full profile → |
- Primary focus
- Managed inference, fine-tuning and GPU clusters for open models
- Deployment
- Not specified
- Source model
- Other license
- Pricing model
- paid
- API available
- Yes
- Windows desktop
- Not specified
- Mobile app
- Not specified
- Overview
- Together AI runs an API platform for inference and fine-tuning of open-source models, plus dedicated GPU cluster rentals, positioned as an alternative to building your own model-serving infrastructure.
ⓘ Confirm technical details and prices with each vendor before making a decision.