Anyscale
To make scalable computing effortless, allowing developers to build and scale AI workloads without managing complex infrastructure.
At a Glance
- Enterprise AI
- Machine Learning Teams
- AI Startups
- Data Science Organizations
AI Tools by Anyscale
(1)Anyscale
Ray AI Platform for Scale
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Latest News
Anyscale Names Christian Stano as Field CTO
Anyscale Collaborates with Microsoft to Deliver AI Compute Service on Azure
Ray Summit 2025 Keynotes feature leaders from OpenAI, xAI, and NVIDIA
Anyscale Cuts Multimodal AI Data Processing Costs by 80% with NVIDIA Integration
Products & Services
An open-source distributed computing framework that makes it easy to scale AI and Python applications.
A fully managed platform for Ray that simplifies building, deploying, and managing distributed applications at scale across clouds.
A serverless API service for serving and fine-tuning popular open-source LLMs with optimized performance and cost.
Market Position
Anyscale positions itself as the 'managed Ray' company, offering the easiest path for organizations to scale Python and ML workloads compared to DIY Kubernetes or cloud-specific scaling tools.
Leadership
Founders
Robert Nishihara
Co-creator of Ray; PhD in Computer Science from UC Berkeley (RISELab); previously a researcher focusing on distributed systems and machine learning.
Philipp Moritz
Co-creator of Ray; PhD in Computer Science from UC Berkeley (RISELab); expert in distributed computing and machine learning infrastructure.
Ion Stoica
Professor at UC Berkeley; Co-founder of Databricks and Conviva; leader of the RISELab and AMPLab; renowned for work on distributed systems like Spark and Ray.
Executive Team
Keerti Melkote
Chief Executive Officer
Founder and former CEO of Aruba Networks (acquired by HPE); former President of HPE Intelligent Edge.
Robert Nishihara
Co-founder
Co-creator of Ray and former CEO of Anyscale.
Board of Directors
Founding Story
Anyscale was founded by the creators of Ray, an open-source project started at UC Berkeley's RISELab in 2016-2017. The founders saw the need for a unified framework to handle the growing computational demands of ML and AI, leading them to build Anyscale to provide a fully managed environment for Ray.
Business Model
Revenue Model
Consumption-based/Usage-based billing for compute and managed services, plus enterprise subscription tiers for BYOC.
Pricing Tiers
Managed infrastructure, limited regions, pay-as-you-go compute.
Enterprise SLAs, VPC deployment, 24/7 support, unlimited cases.
Target Markets
- Enterprise AI
- Machine Learning Teams
- AI Startups
- Data Science Organizations
- Large Language Model (LLM) Training
- Real-time Inference
- Distributed Batch Processing
- Reinforcement Learning
- Generative AI Fine-tuning
- Uber
- OpenAI
- Shopify
- Amazon