Anyscale
AI engineering teams and machine learning developers who need to scale data-intensive AI workloads across distributed infrastructure.
Overview
Anyscale is the company behind Ray, the open-source distributed computing framework, and offers a managed platform for building and running production-scale AI workloads. Founded in 2019 and headquartered in San Francisco, Anyscale provides infrastructure that helps teams scale data-intensive AI tasks such as distributed training, data curation, and batch inference. The platform is designed to work across any cloud or on-premises environment, with a focus on reliability, cost efficiency, and developer experience. Anyscale has raised significant funding, including a $40M Series B led by NEA, and has a total of $281M in funding across four rounds.
What it does
Anyscale provides a managed platform built on Ray that enables developers to build, run, and scale AI workloads. It supports distributed model training, multimodal data curation, batch embedding generation, and post-training workloads like LLM inference and fine-tuning. The platform offers features such as elastic scaling, GPU observability, workload scheduling, and multi-cloud deployment, allowing teams to run Ray clusters on their own GPUs or on Anyscale's hosted infrastructure. It integrates with popular AI libraries like PyTorch, vLLM, SGLang, and XGBoost, and provides tools for development, debugging, and monitoring.
Key features
- Distributed model training with elastic scaling and GPU observability
- Multimodal data curation pipelines for videos, images, text, and audio
- Batch embedding generation for search, retrieval, and training
- Post-training support for LLM inference and fine-tuning frameworks like SkyRL and veRL
- Multi-cloud deployment on any region or cloud, including Kubernetes or VMs
- Managed Ray clusters with autoscaling, resilience, and head node failover
- Developer workspaces with VS Code/Jupyter integration and fast startup
- Workload-specific dashboards with persistent logs for debugging and monitoring
Use cases
- Scaling distributed training of large language models across GPU clusters
- Curating and preparing large multimodal datasets for foundation model training
- Generating embeddings at scale for downstream search or retrieval
- Running batch inference on video or other media at scale
- Fine-tuning LLMs for specific tasks like entity recognition
- Processing robotics datasets for visual language model training
Pricing
| Plan | Price |
|---|---|
| Hosted | — |
| Bring Your Own Cloud (BYOC) | — |
Pay-as-you-go pricing with no monthly fixed fees. Compute costs vary by instance type, e.g., CPU at $0.0135/hr, NVIDIA T4 at $0.5682/hr, NVIDIA A100 at $4.9591/hr. H100 and newer families require sales contact. $100 in credits for new users.
View current pricingPricing is gathered from the company's public pages and may change. Check the vendor's site before buying.
Pros and cons
Strengths
- Built on Ray, the widely adopted open-source AI compute engine
- Supports multiple clouds and on-premises deployment with a single control plane
- Pay-as-you-go pricing with no monthly fixed fees
- Offers a free tier with $100 in credits to get started
- Provides enterprise-grade support options including 24x7 coverage
- Integrates with popular AI libraries like PyTorch, vLLM, and XGBoost
Limitations
- Pricing is usage-based and can be complex, with costs varying by instance type
- No self-serve option for H100 or newer GPU families; requires sales contact
- Enterprise features like SSO and audit logs may require a paid plan
- Requires familiarity with Ray and distributed computing concepts
What sets it apart
- Anyscale is the creator of Ray, the world's most widely adopted AI compute engine
- Provides a production-grade platform around Ray with developer tooling and observability
- Supports multi-cloud and on-premises deployment with a single control plane
- Offers a fully managed runtime with no vendor lock-in
Ecosystem
Integrations
Frequently asked questions
What is the difference between Ray and Anyscale?
Ray is an open-source distributed computing framework, while Anyscale is a managed platform built on Ray that provides production-grade infrastructure, developer tooling, and observability.
What workloads does Anyscale support?
Anyscale supports data processing, model training, batch inference, and online serving, including multimodal data curation, distributed training, and post-training workloads.
Where can I run Anyscale?
Anyscale can be deployed on any cloud or region, on Kubernetes or VMs, and on-premises, with options for hosted or bring-your-own-cloud (BYOC) deployment.
How much does Anyscale cost?
Anyscale uses pay-as-you-go pricing with no monthly fixed fees. You only pay for the compute you use, with costs varying by instance type. New users get $100 in credits.
Do you provide support options?
Yes, Anyscale offers support options including business hours support with 5 case submissions for hosted plans, and enterprise SLAs with 24x7 coverage and unlimited case submissions for BYOC plans.
Sources
This profile was compiled from the company's own pages and public web research.
- https://www.anyscale.com/
- https://www.anyscale.com/platform
- https://www.anyscale.com/customers
- https://www.anyscale.com/pricing
- https://www.anyscale.com/about
Last researched August 11, 2026.
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