Sold by: AnyscaleÂ
Deployed on AWS
Anyscale-creators of Ray-delivers an AI-native compute platform that accelerates development and enables scalable deployment of any AI workload. The platform provides a unified runtime that can distribute any Python code or AI library, including XGBoost, PyTorch, and vLLM, making it seamless to scale data processing, training or inference from a single machine to thousands of CPUs, GPUs, or both.
4.3
Overview
Anyscale provides teams with an AI-native compute platform, one that is Python-based, multimodal-ready and GPU optimized. Powered by Ray, the leading framework for scalable AI processing, Anyscale enables teams to build and deploy AI without limits.
Anyscale gives AI teams a production-ready platform that accelerates time to value, reduces TCO, and de-risks operating an internal AI development and deployment platform that supports both traditional machine learning and modern AI workloads.
Teams can get started quickly with our fully managed, Anyscale-hosted experience-or deploy into the customer VPC (virtual private cloud) via BYOC (bring your own cloud), with the flexibility to run on VM-based infrastructure (EC2) or Kubernetes environments (AWS EKS and SageMaker HyperPod).
Highlights
- Unified AI Platform: Accelerate development and reduce time to production with scalable processing for all your AI workloads with the Anyscale Runtime, a performance and reliability optimized engine powered by Ray.
- Developer Velocity: Develop on a multi-node backed IDE and seamlessly transition from dev to prod with self-service clusters for batch and online processing, without any cluster management.
- Deployment Flexibility: Run fully managed clusters on Anyscale compute or your own compute-in your VPC-whether that is using AWS EKS, SageMaker HyperPod or directly on EC2.
Details
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Delivery method
Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
Anyscale Contract | Aggregate of all Anyscale contract usage in U.S. Dollars e.g. Platform usage, Support, Advisory, Training, etc. | $1,000.00 |
Dimension | Cost/unit |
|---|---|
Aggregate of all Anyscale contract usage in U.S. Dollars | $0.01 |
Vendor refund policy
All fees are non-refundable and non-cancellable except as required by law.
Custom pricing options
Request a private offer to receive a custom quote.
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Support
Vendor support
Support offerings are listed at http://anyscale.com/support . Unless you contract for support via a Private Offer, your support is limited to public forums and documentation
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Anyscale
By Lightning AI
By Iguazio
Top
10
In High Performance Computing
Top
25
In ML Solutions
Top
10
In Feature Engineering, ML Solutions
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Distributed Computing Framework
Provides a unified runtime that can distribute Python code and AI libraries across multiple machines, supporting scalable processing from single machine to thousands of CPUs and GPUs
AI Library Compatibility
Supports multiple AI and machine learning libraries including XGBoost, PyTorch, and vLLM for seamless distributed computing
Multi-Environment Deployment
Enables deployment across VM-based infrastructure (EC2), Kubernetes environments (AWS EKS and SageMaker HyperPod), and supports both managed and customer VPC configurations
Runtime Optimization
Offers a performance and reliability optimized engine powered by Ray for accelerating AI workload processing
Multi-Modal Processing
Provides a Python-based, multimodal-ready, and GPU-optimized platform for handling diverse AI development and deployment scenarios
Distributed Training Capabilities
Supports multi-node training across various compute resources including CPUs and GPUs with seamless scalability
Development Environment Integration
Integrates multiple machine learning tools into a unified, cohesive development platform with consistent cloud and local code execution
Model Development Studio
Provides pre-configured studios for different AI domains including Large Language Models, Diffusion models, and Graph Neural Networks
Enterprise Security Framework
Implements fine-grained access control, private networking, and data isolation mechanisms with bring-your-own-cloud (BYOC) architecture
Computational Resource Management
Offers on-demand access to high-performance computing resources including A100 and H100 GPUs with serverless deployment capabilities
Machine Learning Workflow Automation
Comprehensive AI platform with end-to-end workflow capabilities for building, deploying, and operationalizing machine learning and generative AI applications
Large Language Model Customization
Advanced capabilities for fine-tuning models using techniques like Retrieval-Augmented Generation (RAG) and Retrieval-Augmented Fine-Tuning (RAFT)
GPU Resource Management
Dynamic GPU resource provisioning with scalable and flexible deployment across multiple environments including cloud, on-premises, and hybrid infrastructures
AI Application Governance
Built-in monitoring, guardrails, and governance mechanisms for managing machine learning and generative AI application lifecycles
Multi-Environment Deployment
Supports deployment across diverse computing environments with auto-scaling and automation capabilities
Standard contract
No
No
Customer reviews
Rakshit A.
Great tool for scaling AI workloads
Reviewed on Nov 19, 2025
Review provided by G2
What do you like best about the product?
What impresses me most is how it handles the heavy lifting for Ray. I can develop my AI application code right on my laptop and then deploy it to a large cluster without having to rewrite anything or wrestle with complex infrastructure setups. This effectively bridges the gap between code that only "works on my machine" and a real production environment, which is particularly useful when scaling LLM workloads and managing distributed training. In the end, it saves me a considerable amount of time on DevOps tasks.
What do you dislike about the product?
The pricing structure can feel somewhat unclear, making it difficult at times to anticipate your final monthly bill. This is especially noticeable when compared to the more straightforward cost management you get with handling raw EC2 instances on your own.
What problems is the product solving and how is that benefiting you?
I use Anyscale mainly to overcome the infrastructure challenges of scaling Python machine learning code from my local laptop to a large distributed cluster. My team operates a substantial Retrieval-Augmented Generation (RAG) pipeline, which includes OCR processing and embedding generation for millions of PDF files. Previously, running this workload on a single large EC2 instance would take weeks, and managing AWS Batch jobs involved a lot of boilerplate and ongoing DevOps work. With Anyscale, we were able to wrap our existing Python functions with Ray decorators, enabling the platform to automatically spin up a cluster of over 50 spot instances, process 2TB of data in less than four hours, and then scale back down to zero. This approach has reduced our compute costs by about 60% by taking advantage of spot instances without the need for manual fault-tolerance solutions, and it has allowed my data scientists to independently run large-scale experiments without waiting for DevOps to provision resources.
Mohammad hanif A.
AI/ML
Reviewed on Sep 11, 2025
Review provided by G2
What do you like best about the product?
Anyscale makes it easy to scale AI/ML workloads without worrying about infrastructure complexity
What do you dislike about the product?
Documentation could be more beginner-friendly with clearer end-to-end examples
What problems is the product solving and how is that benefiting you?
solves the challenge of scaling machine learning and AI workloads without requiring deep expertise in distributed systems. eg remove complexity
Subrat M.
Scalable and reliable platform for AI workloads
Reviewed on Aug 25, 2025
Review provided by G2
What do you like best about the product?
Anyscale simplifies the process of moving AI and ML workloads from development to production. Since it is built on Ray, it enables scalability without requiring major code changes.
What do you dislike about the product?
The platform has a noticeable learning curve, particularly for teams unfamiliar with Ray concepts. Pricing is not always transparent, which makes cost planning more challenging.
What problems is the product solving and how is that benefiting you?
Anyscale addresses the challenge of running distributed ML and GenAI workloads efficiently.
Akanksha R.
Good
Reviewed on Aug 19, 2025
Review provided by G2
What do you like best about the product?
The Anyscale platform was essential as it fully managed, production-ready version of Ray, offering a simplified and integrated developer experience and it made easy to build and it has good scalability.
What do you dislike about the product?
About the disadvantage is during building there is little trouble when debugging trouble.
What problems is the product solving and how is that benefiting you?
I solved the scalability and robustness problems as it was easier to solve.
Atul G.
Infrastructure for AI
Reviewed on Apr 19, 2022
Review provided by G2
What do you like best about the product?
It's provide infrastructure for AI and deep learning.
What do you dislike about the product?
I haven't found anything wrong with the product.
What problems is the product solving and how is that benefiting you?
We were struggling with the risk analysis for the wind turbines components but with the help of Anyscale ray technology we Easley crack it with high true rate.