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Anyscale | Scalable Compute for AI and Python logo

Anyscale | Scalable Compute for AI and Python

Free

Production-scale AI with Ray

3
Developer ToolsFreeFree tier
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Anyscale
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About Anyscale | Scalable Compute for AI and Python

Anyscale is the leading AI application platform that allows developers to build, run, and scale AI applications instantly. It leverages Ray, a distributed computing framework, to optimize performance and lower costs for AI workloads. Anyscale offers tools for compute governance, developer tooling, and supports any cloud, accelerator, and stack.

How to Use

To use Anyscale, developers can leverage Ray's Pythonic APIs to run workloads across GPUs and CPUs at any scale. The platform offers tools for optimizing performance, managing resources, and deploying AI applications in various environments, including cloud, on-premise, and hybrid setups. Users can get started with a $100 credit and explore the platform's features through demos and expert consultations.

Key Features

  • RayTurbo: A supercharged version of Ray for optimized AI compute.
  • Compute Governance: Tools to manage and govern AI usage.
  • Developer Tooling: World-class tooling to delight developers.
  • Flexible Deployment: Support for any cloud, accelerator, and stack.

Use Cases

  • Scaling distributed ML workloads across GPUs and CPUs.
  • Building AI applications with lasting impact.
  • Maximizing throughput for optimized cost.
  • Parallelizing thousands of small- and mid-sized models.

Key Features

RayTurbo: A supercharged version of Ray for optimized AI compute.
Compute Governance: Tools to manage and govern AI usage.
Developer Tooling: World-class tooling to delight developers.
Flexible Deployment: Support for any cloud, accelerator, and stack.

Pros & Cons

Pros
  • Deep integration with Ray, a widely adopted open-source distributed computing framework
  • Supports multiple deployment options: hosted, BYOC, or on-premises
  • Usage-based pricing with no fixed monthly fees and a $100 free credit to start
  • Built-in GPU observability and elastic scaling for cost-efficient training
  • Rich set of pre-built code templates and examples for common AI workloads
Cons
  • Requires familiarity with Ray and distributed computing concepts
  • Costs can accumulate quickly at large scale without committed contracts
  • Advanced features like enterprise SLAs and 24x7 support only available on higher-tier plans

Best For

Scaling distributed ML workloads across GPUs and CPUs.Building AI applications with lasting impact.Maximizing throughput for optimized cost.Parallelizing thousands of small- and mid-sized models.

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FAQ

What’s the difference between Ray and Anyscale?
Ray is an open-source distributed computing framework, while Anyscale is a production-ready managed platform built on top of Ray that simplifies deployment, scaling, and governance.
What workloads does Anyscale support?
Anyscale supports data-intensive AI workloads including multimodal data curation, distributed model training, batch embedding generation, and post-training (e.g., LLM inference and reinforcement learning).
Where can I run Anyscale?
Anyscale can be deployed as a fully managed hosted service, in your own cloud (BYOC on AWS, Azure, GCP), or on-premises. It supports any cloud region and VM or Kubernetes infrastructure.
How do I get started with Anyscale?
You can sign up for a free account and receive $100 in credits to explore the platform. Anyscale also provides dozens of code templates and sample projects to get started quickly.
How much does Anyscale cost?
Anyscale uses usage-based billing; you only pay for the compute you use. Pay-as-you-go rates are listed per accelerator type. Committed contracts are available for volume discounts. A $100 free credit is provided to new users.
Do you provide support options?
Yes. The hosted plan includes business hours support with limited case submissions. Enterprise plans offer 24x7 coverage, unlimited case submissions, and dedicated SLAs.