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Anyscale

Paid

Scalable AI model deployment

Model APIsPaidFree tier
Inputs: text, image, audio, video, codeOutputs: text
Type
Api
Founded
2019
Company
Anyscale
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About Anyscale

Anyscale is a managed platform built on Ray, the open-source AI compute engine, designed to power production-scale AI workloads. It enables teams to build, train, and deploy foundation models with capabilities including multimodal data curation, distributed model training across GPU clusters, batch embedding generation, and post-training tasks like LLM inference. Anyscale supports any cloud or accelerator, offers pay-as-you-go pricing with a $100 credit for new users, and provides both hosted and bring-your-own-cloud (BYOC) deployment options to maximize GPU utilization and reduce costs.

Key Features

Multimodal data curation pipeline for videos, images, text, and audio
Distributed model training with elastic scaling and GPU observability
Batch embedding generation at scale for search, retrieval, or training
Post-training support for LLM inference and frameworks like SkyRL and veRL
Deploy on any cloud or accelerator with hosted or BYOC options
Pay-as-you-go pricing with usage-based billing and no fixed monthly fees
Committed contracts and volume discounts for larger usage
Expert support available 24x7 for enterprise customers

Pros & Cons

Pros
  • Built on Ray, the most widely adopted AI compute engine
  • Supports elastic scaling across GPU clusters for large workloads
  • Handles diverse data types (text, image, audio, video) in one pipeline
  • Offers both fully managed hosted deployment and BYOC for data residency
  • Usage-based billing with no upfront costs and potential savings up to 99%
  • Provides GPU observability and last-mile data preprocessing integration
  • Includes $100 free credit for new users to get started

Best For

Foundation model building and scalingLarge-scale multimodal data preparation for AI trainingDistributed training of large language models and vision modelsBatch embedding generation for semantic search and RAGPost-training and fine-tuning of LLMs using frameworks like vLLMFine-tuning vision-language-action models (e.g., PI0.5 VLA)

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Fireworks AI

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FAQ

What's the difference between Ray and Anyscale?
Ray is an open-source framework for distributed computing, originally developed at UC Berkeley. Anyscale is a managed platform that provides production-ready Ray, adding tooling, scalability, and support so developers can focus on AI workloads without managing infrastructure.
What workloads does Anyscale support?
Anyscale supports multimodal data curation, distributed model training, batch embedding generation, and post-training workloads such as LLM inference and fine-tuning using Ray-based frameworks like vLLM, SkyRL, and veRL.
Where can I run Anyscale?
Anyscale can be deployed on any cloud or region (AWS, Azure, GCP) and on-premises. Two deployment options are available: Hosted (fully managed by Anyscale) and Bring Your Own Cloud (BYOC) where infrastructure runs in your own VPC.
How do I get started with Anyscale?
You can create a free account and receive $100 in Anyscale credits to explore the platform. Anyscale provides dozens of code templates, self-service courses, and technical events to help you build and scale your AI workloads.
How much does Anyscale cost?
Anyscale offers pay-as-you-go pricing where you only pay for compute used, starting at $0.0135/hr for CPU-only instances. Committed contracts are available for volume discounts, and you can use existing GPU reservations. There are no monthly fixed fees.
Do you provide support options?
Yes. The Hosted plan includes business hours support with 5 case submissions. Enterprise plans provide 24x7 support with unlimited case submissions, dedicated SLAs, and expert assistance.