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Anyscale

  • What it is:Anyscale is a fully managed, unified compute platform built by the creators of Ray for scaling AI, ML, and Python workloads from data processing to training and inference.
  • Best for:Enterprise ML teams running distributed workloads, Organizations with multi-cloud or existing cloud commitments, Teams building GenAI and large language model applications
  • Pricing:Free tier available, paid plans from $0.0135/hour
  • Rating:72/100Good
  • Expert's conclusion:Anyscale is ideal for AI teams that are prepared to take advantage of Ray at scale to enable cost-efficient, production-quality ML infrastructure.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Anyscale and What Does It Do?

Anyscale utilizes the open-source distributed computing framework Ray to provide a completely managed solution for building scalable AI and ML applications without requiring companies to manage their own complex infrastructure. As of the time of this writing, Anyscale's customers include some of the most well-known names in tech such as Uber, OpenAI, Shopify, and Amazon, and has demonstrated significant success with Ray's open-source model.

Active
📍San Francisco, CA
📅Founded 2019
🏢Private
TARGET SEGMENTS
DevelopersEnterprise AI TeamsMachine Learning EngineersData Scientists

What Are Anyscale's Key Business Metrics?

👥
Uber, OpenAI, Shopify, Amazon
Major Customers

How Credible and Trustworthy Is Anyscale?

72/100
Good

The company's ability to attract such high-profile clients, combined with the fact that the founders are all from UC Berkeley and have developed the Ray open-source project indicates strong credibility. In contrast to many companies in this space, Anyscale appears to lack transparency regarding their financials, security certifications, and customer reviews which likely contributed to a lower overall score.

BREAKDOWN
Product Maturity75/100
Company Stability70/100
Security & Compliance65/100
User Reviews75/100
Transparency70/100
Support Quality70/100
TRUST SIGNALS
Founded by UC Berkeley RISELab researchersRay is widely adopted in ML/AI communityUsed by industry leaders: OpenAI, Uber, Shopify, AmazonOpen-source foundation for transparency

What is the history of Anyscale and its key milestones?

2019

Company Founded

Anyscale was co-founded by Ion Stoica, Michael I. Jordan, Philipp Moritz, and Robert Nishihara to bring distributed computing technology solutions to the commercial market for ML and AI.

2019

Ray Open Source Release

Ray is an open-source distributed computing framework that was developed at UC Berkeley's RISELab to address the scalability challenges facing ML and AI.

Who Are the Key Executives Behind Anyscale?

Ion Stoica— Co-founder
Ion Stoica is a Professor at UC Berkeley and a Researcher at the RISELab who specializes in Distributed Systems and Cloud Computing.
Michael I. Jordan— Co-founder
Michael I. Jordan is a Distinguished Professor at UC Berkeley and one of the world's leading researchers in Machine Learning and Statistics.
Philipp Moritz— Co-founder
Philipp Moritz is a Researcher at UC Berkeley's RISELab where he focuses on Distributed Machine Learning Systems.
Robert Nishihara— Co-founder
Robert Nishihara is also a Researcher at UC Berkeley's RISELab and his specialty is Distributed Systems for Machine Learning.

What Are the Key Features of Anyscale?

📊
Fully-Managed Compute Platform
By eliminating the need to manage distributed systems, Anyscale reduces the complexity associated with infrastructure management so developers can focus on developing applications rather than managing distributed systems.
🔗
Ray Framework Integration
Anyscale is based upon the proven, widely adopted distributed computing framework Ray. This allows Anyscale to offer a proven distributed computing solution for a wide variety of AI and ML workloads.
✨
Scalability from Laptop to Data Center
Anyscale provides automatic application scaling. Once developers begin to develop an application using Anyscale, it will automatically scale from running on a developer's local machine to being deployed in production environments across a large cluster of machines.
💬
Support for Any ML/AI Workflow
Anyscale supports a wide range of ML tasks including model training, hyperparameter tuning, distributed inference, and others. These represent a number of the common types of workloads encountered when working with AI.
✨
Developer-Friendly Abstractions
Enables developers of all skill levels to develop scalable distributed applications without having extensive experience with the underlying infrastructure.
📊
Production-Ready Platform
Takes care of deploying, monitoring and managing distributed Machine Learning (ML) systems in production, similar to what large-scale companies do.

What Technology Stack and Infrastructure Does Anyscale Use?

Infrastructure

Unknown - details not publicly available

Integrations

ML frameworks and librariesData processing toolsEnterprise ML platforms

AI/ML Capabilities

Distributed machine learning and AI computing framework built on Ray, supporting various ML workflows including model training, hyperparameter tuning, and inference at scale.

Information limited to publicly available company website content

What Are the Best Use Cases for Anyscale?

Machine Learning Engineers
Allows users to train, tune hyperparameters, and perform inference using scalable ML models without worrying about the complexities of the distributed infrastructure they need.
Enterprise AI Teams
Reduces infrastructure management costs when moving machine learning projects from development to production, at a large scale.
Data Scientists
The user can focus on developing models and experimenting with them, rather than dealing with the complexity of distributed computing, which scales automatically over multiple resource types.
Startups Building ML Platforms
Enabling faster market entry for AI-based products through the use of a production ready distributed computing platform, rather than requiring an organization to build one from scratch.
Developers with Limited Distributed Systems Expertise
Allowing users to develop scalable ML applications without needing a deep understanding of distributed computing, cluster management, or designing their own infrastructure.
NOT FORReal-Time Low-Latency Applications
Limited suitability – the platform is designed for batch and training workloads, and does not support sub-millisecond real-time inference requirements.
NOT FOROrganizations with Existing Proprietary ML Infrastructure
Will be hindered by the need to migrate from existing customized systems and will require both a plan for migrating and a cost/benefit analysis before being adopted.

How Much Does Anyscale Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
CPU Only$0.0135/hourPay-as-you-go compute with no monthly fixed feesAnyscale pricing page
NVIDIA T4 GPU$0.5682/hourGPU-accelerated compute for training and inferenceAnyscale pricing page
NVIDIA L4 GPU$0.9542/hourMid-range GPU for ML workloadsAnyscale pricing page
NVIDIA A10G GPU$1.3635/hourHigh-performance GPU for demanding training tasksAnyscale pricing page
NVIDIA A100 GPU$4.9591/hourEnterprise-grade GPU for large-scale AI workloadsAnyscale pricing page
NVIDIA H100 GPU$9.2880/hourNext-generation GPU for cutting-edge AI applicationsAnyscale pricing page
NVIDIA H200 GPU$10.6812/hourLatest high-performance GPU with expanded memoryAnyscale pricing page
Bring Your Own Cloud (BYOC)Platform fee + cloud provider costsDeploy in your own AWS, GCP, or on-premise infrastructure. Platform fee charged on top of existing cloud costs. Allows use of existing cloud reservations.Anyscale pricing page
Committed ContractsCustom pricingVolume discounts for stable, high-volume workloads with upfront spending commitments. Contact sales for quote.Anyscale pricing guide
Starter Credit$100 free creditFree credit to get started with pay-as-you-go computeAnyscale pricing page
CPU Only$0.0135/hour
Pay-as-you-go compute with no monthly fixed fees
Anyscale pricing page
NVIDIA T4 GPU$0.5682/hour
GPU-accelerated compute for training and inference
Anyscale pricing page
NVIDIA L4 GPU$0.9542/hour
Mid-range GPU for ML workloads
Anyscale pricing page
NVIDIA A10G GPU$1.3635/hour
High-performance GPU for demanding training tasks
Anyscale pricing page
NVIDIA A100 GPU$4.9591/hour
Enterprise-grade GPU for large-scale AI workloads
Anyscale pricing page
NVIDIA H100 GPU$9.2880/hour
Next-generation GPU for cutting-edge AI applications
Anyscale pricing page
NVIDIA H200 GPU$10.6812/hour
Latest high-performance GPU with expanded memory
Anyscale pricing page
Bring Your Own Cloud (BYOC)Platform fee + cloud provider costs
Deploy in your own AWS, GCP, or on-premise infrastructure. Platform fee charged on top of existing cloud costs. Allows use of existing cloud reservations.
Anyscale pricing page
Committed ContractsCustom pricing
Volume discounts for stable, high-volume workloads with upfront spending commitments. Contact sales for quote.
Anyscale pricing guide
Starter Credit$100 free credit
Free credit to get started with pay-as-you-go compute
Anyscale pricing page
💡Pricing Example: Monthly GPU training workload: 200 hours on NVIDIA A100
Pay-as-you-go$991.82/month
$4.9591/hour × 200 hours
Committed contract (estimated 30% discount)$693.27/month
Approximately $4.9591 × 200 × 0.70
💰Savings:Committed contracts provide significant discounts for predictable, continuous workloads

How Does Anyscale Compare to Competitors?

FeatureAnyscaleAWS SageMakerGoogle Vertex AIModal
Pricing ModelPay-as-you-go with volume discountsPay-as-you-goPay-as-you-goPay-as-you-go
Starting PriceCPU: $0.0135/hour$0.25/hour (on-demand)$0.28/hour (standard)$0.002/hour (CPU)
GPU Options AvailableYes (T4, L4, A10G, A100, H100, H200)Yes (limited selection)Yes (limited selection)Yes (limited selection)
BYOC SupportYesLimitedLimitedNo
Free TierYes ($100 credit)Yes ($300 credit)Yes ($300 credit)Yes (generous free tier)
Auto-scalingYes (with auto-suspension)YesYesYes
Multi-cloud SupportYes (AWS, GCP, on-premise)AWS onlyGCP onlyLimited
Support TiersDeveloper, Standard, EnterpriseDeveloper, Business, EnterpriseStandard, Enhanced, PremiumCommunity, Professional
Pricing Model
AnyscalePay-as-you-go with volume discounts
AWS SageMakerPay-as-you-go
Google Vertex AIPay-as-you-go
ModalPay-as-you-go
Starting Price
AnyscaleCPU: $0.0135/hour
AWS SageMaker$0.25/hour (on-demand)
Google Vertex AI$0.28/hour (standard)
Modal$0.002/hour (CPU)
GPU Options Available
AnyscaleYes (T4, L4, A10G, A100, H100, H200)
AWS SageMakerYes (limited selection)
Google Vertex AIYes (limited selection)
ModalYes (limited selection)
BYOC Support
AnyscaleYes
AWS SageMakerLimited
Google Vertex AILimited
ModalNo
Free Tier
AnyscaleYes ($100 credit)
AWS SageMakerYes ($300 credit)
Google Vertex AIYes ($300 credit)
ModalYes (generous free tier)
Auto-scaling
AnyscaleYes (with auto-suspension)
AWS SageMakerYes
Google Vertex AIYes
ModalYes
Multi-cloud Support
AnyscaleYes (AWS, GCP, on-premise)
AWS SageMakerAWS only
Google Vertex AIGCP only
ModalLimited
Support Tiers
AnyscaleDeveloper, Standard, Enterprise
AWS SageMakerDeveloper, Business, Enterprise
Google Vertex AIStandard, Enhanced, Premium
ModalCommunity, Professional

How Does Anyscale Compare to Competitors?

vs AWS SageMaker

While both platforms are designed to provide flexible multi-cloud deployments, including Bring Your Own Cloud (BYOC), Anyscale is Ray native and therefore provides more flexible deployment options than SageMaker, which is Amazon Web Services (AWS) native and has deeper integration with other AWS services. Additionally, Anyscale is positioned as a Ray native distributed ML platform, while SageMaker is a full-featured managed service. Therefore, Anyscale would appeal to teams that want more control and flexibility in their deployments, while SageMaker would be a good fit for AWS committed organizations.

For multi-cloud flexibility, choose Anyscale. For Ray-based workloads and Amazon Web Services (AWS) integrations, choose SageMaker.

vs Google Vertex AI

Offers similar positioning to SageMaker but is Google Cloud Platform (GCP) native. Anyscale's main advantage is its ability to run on a variety of clouds and also allow users to bring their own cloud reservation, providing greater flexibility and availability than Vertex AI, which is tightly integrated into GCP and provides fewer options. Both platforms charge similarly for compute resources.

Use Anyscale if you have a multi-cloud strategy or are already locked into a cloud commitment. If your team uses Google Cloud Platform (GCP), use Vertex AI.

vs Modal

The price point for Modal is a lower price point at the start ($0.002/hour CPU vs $0.0135/hour from Anyscale) and an unlimited free tier. Although Anyscale has a larger set of available GPUs, Anyscale supports BYOC and has been optimized for use with distributed ML and Ray. Therefore, Modal will be better suited for those that are looking to use serverless computing simply; Anyscale for those using distributed training workflows with large amounts of data.

Choose Modal for cost-efficient, simple serverless workloads. Choose Anyscale for high-end, distributed machine learning and scalable cost-optimization.

What are the strengths and limitations of Anyscale?

Pros

  • Native Ray-platform — developed by the developers of Ray, enables efficient distributed machine learning and data processing without complex configurations.
  • Deploy in multiple clouds using BYOC — deploy in AWS, GCP, on premise or Kubernetes while utilizing cloud reservation and commitments you already have.
  • Cost savings — users have reported a 50-99 percent cost savings. One user reduced their costs by 67 percent through the optimization of cluster management.
  • Multiple GPUs to select from — utilize the latest GPUs (H100, H200) and legacy options, enabling the right size for different workload stages.
  • Only pay for what you use — pay-as-you-go with no monthly minimums; Auto-Suspend prevents paying for idle resources.
  • Discounts when you purchase in volume — pricing becomes less expensive as usage increases; Committed contracts can be established for predictable workloads.
  • Free credits to help you get started — A $100 startup credit eliminates the initial barriers to entry.

Cons

  • Complex pricing model — Final bills are heavily influenced by hardware choices (H100 160 times more expensive than CPU), duration of jobs, and usage patterns, which makes budgeting for the future very difficult.
  • Uncertain costs for variable workloads — Usage based model does not provide transparency for variable ML jobs, which makes predicting costs for variable workloads difficult.
  • High barrier to entry -- Platform uses many Ray-specific concepts that require a lot of knowledge before you can get started. Teams have reported spending an enormous amount of engineering time for troubleshooting and optimizing their workloads.
  • The biggest hidden cost of the team training -- in addition to the cost of the compute, most of the cost of the training will be the time engineers spend learning how to manage the distributed system and how to optimize the workload.
  • The lack of clarity into what the true Total Cost of Ownership (TCO) is for using the platform -- Many users find themselves having trouble estimating what their final monthly bill will be compared to something as simple as getting raw EC2 services from the cloud providers.
  • There is a cognitive overhead associated with managing a cluster -- It takes a certain level of knowledge about distributed computing, auto-scaling, and resource optimization to effectively manage a cluster and get good performance out of your applications.
  • No pricing transparency on a month-to-month basis -- Users cannot simply and easily determine what their consistent monthly costs will be for budgeting and financial planning purposes.

Who Is Anyscale Best For?

Best For

  • Enterprise ML teams running distributed workloads — Anyscale was designed to optimize for large-scale distributed training and inference, but the Ray Foundation and auto-scaling capabilities allow for optimized resource usage for complex ML pipelines.
  • Organizations with multi-cloud or existing cloud commitments — BYOC (bring-your-own-cloud) support enables leveraging existing AWS/GCP reservations and commitments which results in significant reductions in overall cloud costs.
  • Teams building GenAI and large language model applications — The combination of Anyscale Runtime and Ray integration provides up to 2 times faster inference and up to 6 times better cost efficiency for LLM serving at scale.
  • Companies needing 50%+ cost optimization on ML infrastructure — We have proven experience and success with providing cost savings through volume discounts, auto-suspension, and being able to mix and match CPU and GPU resources for different stages of the pipeline.
  • Development teams with predictable, stable ML workloads — We also offer committed contracts to provide substantial discounts for teams who are going to consistently use the service at a very high volume.
  • Data engineering teams processing large datasets — Native support for distributed data processing via Ray provides efficient processing of data preparation and ETL pipelines at scale.

Not Suitable For

  • Startups and small teams with limited engineering resources — Due to the steep learning curve and required investment in setting up the platform consider using Modal for a simpler, more cost effective way to perform serverless compute.
  • Organizations requiring predictable, fixed monthly costs — The usage based pricing model does not make budgeting easy, consider using AWS SageMaker On-Demand with Reserved Instances or Azure ML for more clear pricing.
  • Teams unfamiliar with distributed computing and Ray framework — Use of the platform requires expertise in Ray and understanding of distributed systems. For simpler use cases, consider a managed alternative such as Hugging Face Inference API.
  • Single-region, AWS-only organizations without multi-cloud needs — For those who are fully invested in AWS, SageMaker offers both better integration and easier management than Anyscale’s BYOC model would allow.
  • Projects requiring minimal operational overhead — With Anyscale you will be actively managing your clusters and optimizing them as well. If you want to use as little DevOps as possible, look at something such as Modal or RunPod that are fully managed services.

Are There Usage Limits or Geographic Restrictions for Anyscale?

Compute Billing
Billed by compute usage (starting from $0.00006/minute for CPU); no monthly minimums but actual costs depend heavily on real-time resource consumption
GPU Instance Maximum
Highest available is NVIDIA H200 at $10.6812/hour; cannot combine multiple H200s in single instance
Auto-suspension Default
Automatic cluster suspension available to reduce idle costs; can be configured based on usage patterns
Deployment Options
Hosted deployments managed by Anyscale, or BYOC in your own AWS, GCP, or on-premise Kubernetes infrastructure
Data Residency Control
Data residency options available; specific regional restrictions depend on deployment type (hosted vs BYOC)
Support Tiers
Multiple support levels available; Developer Support included with AWS Marketplace contracts at $1,000/month minimum
Contract Minimums
AWS Marketplace entry point at $1,000/month for 1-month contracts; custom pricing available for enterprise deals

Is Anyscale Secure and Compliant?

Multi-cloud InfrastructureDeployable on AWS, GCP, or customer's own infrastructure. BYOC option provides control over data location and compliance requirements.
Data Control with BYOCBring Your Own Cloud deployments allow organizations to maintain data in their own infrastructure while leveraging Anyscale's platform layer.
Industry-grade SecurityFlexible network configuration and industry-grade security features available; specific security certifications available upon request.
Cloud Provider ComplianceInherits security certifications of underlying cloud providers (AWS, GCP); organizations can leverage existing compliance frameworks.
Enterprise SupportEnterprise tier includes governance support and SLAs with dedicated resources; available through AWS Marketplace and direct contracts.
Production-Ready SecurityPlatform designed for production AI/ML workloads with features to support enterprise security and compliance requirements.

What Customer Support Options Does Anyscale Offer?

Channels
Available via support portalIncluded in platform contracts, upper tiers get dedicated resourcesComprehensive docs.anyscale.com
Hours
Business hours for standard support, 24/7 monitoring
Response Time
SLA with dedicated resources for upper tiers
Satisfaction
Positive reviews on monitoring and management features
Specialized
Developer support for AI/ML workflows
Business Tier
Priority support with SLAs, dedicated resources for enterprise

What APIs and Integrations Does Anyscale Support?

API Type
REST API via Ray platform for AI workloads
Authentication
API keys and cloud provider IAM integration (AWS, GCP)
Webhooks
Supported through Ray Serve for inference endpoints
SDKs
Official Ray Python SDK, integrates with popular ML frameworks
Documentation
Comprehensive at docs.anyscale.com, including Ray APIs
Sandbox
$100 free credits for testing
SLA
Enterprise SLAs via contracts, spot instance management for reliability
Rate Limits
Usage-based with cost governance quotas and budgets
Use Cases
Scale ML training, inference, data processing programmatically

What Are Common Questions About Anyscale?

Anyscale is a unified platform for computing AI and ML workloads across all phases of the workflow, from data preparation through to inference. Additionally, Anyscale utilizes technologies including Spot Instances, Heterogeneous Clusters, Auto-Scaling, and Pay-As-You-Go Compute for optimized resource utilization.

The pricing model for Anyscale is also based on a per-use basis (e.g. $0.0135/hr for CPU and $0.5682/hr for NVIDIA T4). Anyscale also offers volume discounts and the option for committed contracts which can help reduce costs. As opposed to fixed monthly fees, the costs associated with using Anyscale are directly related to the hardware utilized, the scale of the workload, and the duration it runs. Additionally, new users can receive up to $100 worth of free credit to begin their experience with the service.

In addition to offering a managed version of open-source Ray, Anyscale provides additional value to customers by providing additional layers of functionality such as managed infrastructure, cost governance, observability, and automated optimizations such as Spot Instance Management. By utilizing Anyscale to manage their production level AI workloads, customers can realize reductions in total cost of ownership (TCO) as well as reduced operational overhead. Customers wishing to self-manage their Ray-based infrastructure will have to handle scaling and costs manually.

Anyscale provides its customers with data residency controls and the ability to Bring Your Own Cloud (BYOC) if required to meet compliance regulations. Additionally, Anyscale integrates seamlessly with the respective security features of Amazon Web Services (AWS) and Google Cloud Platform (GCP) and includes enterprise-level contract options for customers that require governance and monitoring to ensure secure operation of AI systems.

Yes, Anyscale is available on AWS Marketplace and GCP with BYOC options. Customers can leverage their existing reservations and billing, but Anyscale will still provide the platform for customers. Anyscale supports heterogeneous hardware such as GPUs and TPUs.

Support for developers is provided through contracts with Anyscale and depending upon the tier, customers may have access to dedicated support resources, SLAs, comprehensive documentation, and $100 in credits for onboarding. Customers have reported that they found it easy to set-up and that the billing was transparent.

Yes, Anyscale offers $100 in free Anyscale Credits so that customers can get started immediately with no setup fees and only charged for compute usage beyond the initial credits. This makes Anyscale an ideal choice for customers looking to test their multimodal AI, LLM training, and inference projects.

The learning curve for understanding Ray and distributed systems. The price of an Anyscale account will be harder to predict because of the fluctuations in workload that occur when there are no contracted commitments from customers. Additional indirect costs to a team's time used for optimizing Anyscale.

Is Anyscale Worth It?

Anyscale offers an enterprise-ready production quality AI platform, based on Ray, which enables efficient, cost-effective manage of large-scale, complex machine learning (ML) and General Artificial Intelligence (GenAI) workloads across multiple public cloud providers. Anyscale significantly lowers total cost of ownership (TCO) using spot instances and other forms of optimizations; however, there will be a learning curve for Ray. Best fit for AI teams that require a managed infrastructure rather than a self-hosted alternative.

Recommended For

  • AI/ML engineering teams that need to scale their distributed training and inference.
  • Large enterprises that have varying needs for GPUs and reserved cloud capacity.
  • Organizations that are developing end-to-end AI pipelines on Amazon Web Services (AWS)/Google Cloud Platform (GCP).
  • Teams that prioritize cost governance and observability.

!
Use With Caution

  • Teams that are new to Ray or distributed computing — will require training.
  • Budget planners who want to know fixed prices — will experience price variability due to usage-based billing.
  • Small projects with simple workloads — may find the overhead does not justify the use of Anyscale.

Not Recommended For

  • Non-AI workloads or basic Python applications that do not require scaling capabilities.
  • Budget-constrained startups — will benefit most from fixed contract pricing to receive the best possible rates.
  • Teams that prefer to use fully managed no-infrastructure platforms such as Google Cloud Vertex AI.
Expert's Conclusion

Anyscale is ideal for AI teams that are prepared to take advantage of Ray at scale to enable cost-efficient, production-quality ML infrastructure.

Best For
AI/ML engineering teams that need to scale their distributed training and inference.Large enterprises that have varying needs for GPUs and reserved cloud capacity.Organizations that are developing end-to-end AI pipelines on Amazon Web Services (AWS)/Google Cloud Platform (GCP).

What do expert reviews and research say about Anyscale?

Key Findings

Anyscale offers an enterprise-ready platform built on Ray for scalable AI compute with a pay-as-you-go pricing model, uses spot instance optimization, and integrates with cloud providers (AWS/GCP). Key strengths include cost governance, heterogeneous hardware support, and end-to-end ML workflow support; however, Anyscale will also present a learning curve for Ray, and usage-based billing variability. Positive reviews of Anyscale focus on the ROI provided through reduced team needs and transparent metering.

Data Quality

Good - detailed info from official site, AWS Marketplace, docs, and third-party reviews. Pricing specifics usage-based; no public tier breakdowns.

Risk Factors

!
The learning curve for Ray and distributed systems.
!
Challenges in predicting the cost of Anyscale accounts for variable workloads.
!
Dependence on cloud providers for BYOC deployments of Anyscale :
Last updated: February 2026

What Additional Information Is Available for Anyscale?

Cloud Marketplace Availability

Contracted based pricing available through AWS Marketplace, along with Developer Support; also supports hybrid clusters with TPUs/GPUs via integration with Google Cloud Compute Engine.

Cost Optimization Features

Auto-suspend capabilities combined with Spot instance management and dynamic scaling result in cost savings of as much as 99% using cost governance tools including budgets, quotas, and team-wide usage tracking.

Ray Foundation

Developed by the same individuals who developed Ray, the most popular open-source solution for large-scale AI; Anyscale builds upon this foundation while adding enterprise-focused optimizations for training, inference, and data processing.

User Reviews Highlights

Customers have reported a seamless process of setting up their applications, clear and easy to read billing, and an ROI resulting from being able to downsize their team (for example, 4 people can do what used to require 16) and strong dashboards to monitor GPU usage and debug.

Platform Capabilities

The platform supports multimodal AI, fine-tuning Large Language Models, and any other Python-based workload. MLOps features are designed to make it easier to develop, deploy, and collaborate across teams.

What Are the Best Alternatives to Anyscale?

  • •
    Ray (Self-Managed): This is an open-source framework that underlies Anyscale, allowing developers to build distributed AI applications. It does not require a subscription or payment, however the user will need to manage their own infrastructure, scale, and costs. Ideal for teams that already have the necessary skills to manage their own DevOps environments and are looking to avoid paying for a platform. (https://ray.io)
  • •
    Google Vertex AI: A fully managed machine learning platform with TPUs/GPUs and auto-scaling. While easier for GCP users, it offers less flexibility than Anyscale for custom Ray-based workloads. Ideal for companies that want a complete end-to-end Google Cloud ML solution. (https://cloud.google.com/vertex-ai)
  • •
    AWS SageMaker: A comprehensive managed machine learning service with distributed training and spot instances. While a broader AWS ecosystem is available, there may be additional costs for those outside of the AWS ecosystem. Ideal for companies that are heavily invested in AWS and want to utilize its built-in pipeline capabilities. (https://aws.amazon.com/sagemaker)
  • •
    Databricks: A unified analytics platform for Machine Learning / Artificial Intelligence that utilizes Delta Lake and GPU clusters. Has strengths in both data engineering and machine learning, however has a higher base cost. Ideal for companies that use big data and AI together. (https://www.databricks.com)
  • •
    CoreWeave: A GPU cloud provider that is focused on the needs of AI training and inference and offers lower-cost Spot Pricing. Is a lower-level compute focus with no full-platform features. Ideal for companies that just need raw GPU resources and do not need MLOps features. (https://www.coreweave.com)

What Are Anyscale's Model Training Compute?

Ray-powered distributed training
Distributed Computing Framework
Zero to hundreds of nodes in under a minute
Cluster Scaling
+2x faster Ray workload execution
Performance Optimization
Multi-stage continuous fine-tuning
Model Support

What Finetuning Techniques Does Anyscale Support?

LoRAFull-Parameter Fine-TuningPreference Tuning and AlignmentReinforcement Learning from Verifiable Rewards (RLVR)Agentic TuningMulti-stage Continuous Fine-Tuning

Comprehensive fine-tuning capabilities with support for any framework and custom model compatibility

What Supported Models Does Anyscale Offer?

Open-Source Models

Full support for all models available from the Hugging Face open source ecosystem.

LLaMA Family

Full support for LLaMA and LLaMA variants.

Mistral Models

The Mistral 7B and other Mistral versions

Multimodal Models

Vision-Learning Models (VLMs) for text and image processing

Diffusion Models

Support for fine-tuning Stable Diffusion and other diffusion models

Custom Models

A large number of API's that support any model compatibility

What Is Anyscale's Training Pricing?

Pricing Model
Pay-per-training-run with cost-effective managed infrastructure
Cost Optimization
Automatic scaling and GPU utilization optimization minimize infrastructure costs
Managed Service
Anyscale Endpoints fine-tuning available as cost-effective managed service
Data Control
Full data control with no vendor lock-in

What Training Features Does Anyscale Offer?

Distributed Training

Multi-Node and multi-Model Training using the Ray Train Library

Advanced Performance Features

Gradient Checkpointing; Mixed Precision Training; DeepSpeed Support

Hyperparameter Control

Ability to fully control all hyperparameters and hardware selection

Unified Development Experience

Seamless scaling from laptop to cloud with Anyscale Workspaces without writing a single line of new code

Flexible Task Support

Multi-Stage Continuous Fine-Tuning with Custom Prompt Formats

Batch Inference

Ray Data LLM supports batch processing of both text and multimodal datasets

How Do You Deploy Models with Anyscale?

Inference Endpoints
Anyscale Endpoints with OpenAI-compatible API for seamless integration
Deployment Integration
Fine-tuned models immediately available through Anyscale Endpoints with no additional steps
Serving Infrastructure
Ray Serve for scalable orchestration with high-performance vLLM backends using PagedAttention and continuous batching
Multi Model Support
Unified multi-model deployment and dynamic multi-LoRA support for serving multiple fine-tuned adapters simultaneously
Scaling
Automatic scaling based on traffic from zero to many replicas, optimizing GPU utilization and minimizing costs

How Does Anyscale Handle Data Management, Storage, and Governance?

Full Data Control

Complete control and ownership of your proprietary training data

Ray Data Processing

Reliable scalable data ingestion, pre-processing and management with an SQL-like experience

Multimodal Data Support

Ray Data LLM can handle datasets that include both text and images

Framework Flexibility

Can use any framework or prompt format with built-in support for many popular ones

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