Sold by: Anyscale
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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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 |
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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
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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
reviewer2788458
Fraud prevention has improved and security strengthens while monitoring and CI workflows still need work
Reviewed on Dec 16, 2025
Review from a verified AWS customer
What is our primary use case?
In my field, which is a FinTech company, Anyscale Platform is mainly used for security purposes, protecting data and everything by applying all the basic features from the platform.
For us, we are mainly using Anyscale Platform with VS Code development for providing services for production deployment from my end, as per my role.
We mainly do coding with Anyscale Platform, but if you dig deeper, as I mentioned, we are a FinTech company, so we work daily to prevent fraud. The entire integration with the platform helps us address this, along with faster service provision and risk assessment using Anyscale services.
What is most valuable?
Anyscale Platform provides top features including AI and ML workflows which are reliable and scalable, along with management capabilities. The integration with services like AWS and GCP is beneficial, and we also have a built-in monitor.
The monitoring feature of Anyscale Platform, particularly the metrics dashboard, stands out because it shows GPU memory usage and visualizations that simplify our experience with the entire dashboard along with a well-prepared debugging and logging system.
Since adopting Anyscale Platform, we have observed a drastic improvement in overall security, and it is cost-efficient. The performance and productivity boost have led to a reduction in the number of people needed for operations, and scalability has also improved significantly.
What needs improvement?
Anyscale Platform sometimes lags and there is no response, which happens rarely but is noted. Additionally, a built-in CI/CD integration could be a useful addition.
That was just a suggestion from my end regarding CI/CD integration since we have been using it differently, but I cannot add more than that.
The overall performance and boost from Anyscale Platform is amazing, but I still see some improvement points, which is why I chose that score.
For how long have I used the solution?
I have been using Anyscale Platform for more than over a year.
What do I think about the stability of the solution?
Anyscale Platform is stable.
What do I think about the scalability of the solution?
The scalability of Anyscale Platform is good.
How are customer service and support?
Customer support is outstanding, as the support team is always available whenever we need to reach out or raise a ticket, and they assist us effectively.
I rate the customer support a ten out of ten.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
I did not previously use a different solution.
How was the initial setup?
The entire process with pricing, setup cost, and licensing was smooth, with the costing sheet metrics provided and approval received, which seemed reasonable.
What was our ROI?
I do not really receive reports on exact numbers or budget saved, but if four people used to work on a particular task, that has reduced to one due to Anyscale Platform, allowing others to focus on different tasks.
I mentioned previously that we reduced the number of people needed for certain tasks from four to one, which signifies fewer people, less money saved, and time saved, although I do not have the entire metrics available.
What's my experience with pricing, setup cost, and licensing?
The entire process with pricing, setup cost, and licensing was smooth, with the costing sheet metrics provided and approval received, which seemed reasonable.
Which other solutions did I evaluate?
The other options were evaluated, but Anyscale Platform seemed the best choice.
What other advice do I have?
I advise others looking into using Anyscale Platform to consider its scalability and the services provided, which are all on point. I provided a review rating of seven out of ten.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Uday Nagpure
Streamlined data pipelines have cut training cycles and now empower rapid experimentation
Reviewed on Dec 16, 2025
Review from a verified AWS customer
What is our primary use case?
Anyscale Platform is being used for large-scale data processing, such as ETL, feature engineering, and embedding generation, which helps us to outgrow the single-node Spark or Pandas limitations. Additionally, it supports training and fine-tuning of LLMs across many GPUs, reducing fine-tuning cycles from weeks on one GPU to days on 8+ GPUs with Ray Train. It also provides very good throughput and low latency model serving for a RAG agent pipeline using Ray Serve for our online interface at 10,000 QPS. Anyscale Platform is also helping us to get integrated with Kubernetes to offload orchestration and focus on model and application logic.
What is most valuable?
Some of the best features of Anyscale Platform include the Unified Ray Runtime, which helps to refine and tune the Ray engine for all kinds of workload types, such as batch, streaming, training, and serving. The fully managed cluster is something that I personally love as it helps with automatic provisioning, auto-scaling from 1 to 1,000+ nodes, auto-retries, and job scheduling. Clusters help us get the job done on time, and they can start up to 5x faster compared to stock Ray according to Anyscale benchmark. Additionally, it supports multi-node back IDEs and notebooks. Observability , log access, metrics, and debugging controls are specifically tailored for Ray workflows, which Anyscale Platform provided us.
Personally, I feel the biggest impact has been provided by the fully managed clusters, which help with automatic provisioning, auto-scaling from nodes, auto-retries, and job scheduling. These features help serve our purpose for data collection and tuning with Ray Train.
Anyscale Platform has helped us reduce computing costs by 67%, which is our official figure. As we used to manage EC2 for batch clusters by leveraging spot instances and aggressive features, we have saved almost 60% in costs compared to manual management. It has also helped with reductions in training and data processing times. We used to have a cycle time of two weeks, which was reduced to almost half a week, representing more than 100% improvement in training times. We have seen significant productivity gains in our developers as they were able to run large experiments independently without waiting for infrastructure provisioning, which reduced the time to market and increased our total organizational throughput by 77%.
Anyscale Platform is best suited for organizations that are committed to Ray as their distributed compute layer and want a production-ready and managed platform that can help maximize Ray advantages while minimizing operational burden.
What needs improvement?
Anyscale Platform could integrate AI in a better way and add more workflows in the future.
For how long have I used the solution?
Our organization has been using Anyscale Platform for almost three years now.
What do I think about the stability of the solution?
Anyscale Platform is definitely a stable solution. We did not face any kind of downtime, lags, glitches, or bugs, even during updates. It has solved our purpose without incurring significant glitches or downtime.
What do I think about the scalability of the solution?
The scalability is really good as Anyscale Platform comes with the pay-as-you-go model, so we can definitely scale as much as we want. We have seen this at a very large scale and did not face any particular problems while scaling. Scalability is something that works according to my observation.
How are customer service and support?
Customer support has been really good. We had an interaction almost six months back with the team, and they were very prompt. The service was good and they resolved our query. We were having an issue with the deployment on one of the new team structures, and they helped us resolve the concern within 16 minutes. Customer support deserves a 10 out of 10 according to my observation.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
Previously, we did not use any solution for this particular purpose, so this was the first time we used Anyscale Platform.
How was the initial setup?
It was definitely very easy to deploy Anyscale Platform in our environment. The support from the team during deployment was commendable, which allowed us to do it easily.
What about the implementation team?
The configuration is very easy and we were able to configure it as the learning curve was not that steep. We had careful monitoring over the overspending part for GPU-heavy systems. Dependency and environment management for complex Python stacks which Anyscale Platform mitigates required good DevOps practices, and this was handled really well.
The procurement was really easy and we did not face any kind of challenges. The teams involved were very positive about it and the outcomes were really positive. No issues were reported at all.
What was our ROI?
There was a return on investment and we have seen a reduction in compute costs and reduced the time to market by at least 50 to 60%. These are the relevant metrics showing our ROI.
What's my experience with pricing, setup cost, and licensing?
Pricing, setup cost, and licensing have been very transparent for us. We used custom-priced models for our organization, which added governance support and SLAs with dedicated resources at the upper tiers as we use the upper tier model. We can scale up anytime as it is based on the pay-as-you-go model, so there are no problems at all.
Regarding metering and billing experience, Anyscale Platform is based on the pay-as-you-go model and focuses on consumption-based pricing, which is a game-changer for us. Whenever we require GPUs, hours, or storage, we go ahead and get it done through the AWS Marketplace and receive good discounts according to our needs. The metering and billing experience has been very transparent and we have maximized output by paying the least amount.
Which other solutions did I evaluate?
We evaluated DIY Ray on Kubernetes , SageMaker , Vertex AI, and Azure ML as alternatives.
What other advice do I have?
The features which Anyscale Platform is providing are really exceptional and I personally do not feel any challenges or particular problems that we have faced. That is why I really admire it and we are using it. We have seen a good amount of ROIs only instead of problems. The features that we wished for have been there, which is why Anyscale Platform deserves a five out of five rating. It integrates really and whatever we use with it works really well. I am very positive and feel that they are headed in the right direction. I give this product a five out of five because it has solved our problems and delivered a good rate of investment.
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.