Anyscale is a managed compute platform from the creators of Ray, enabling developers to instantly scale Python and AI applications from a laptop to the cloud without managing complex infrastructure.
Anyscale provides a fully managed platform built on the open-source Ray framework to simplify distributed computing for Python applications. It specifically targets data scientists and machine learning engineers who struggle with scaling AI workloads from development to production. The platform abstracts away infrastructure management, offering a serverless experience for running demanding training and inference jobs. Its unique value proposition lies in its deep integration with the Ray ecosystem and its creation by Ray's original authors. Anyscale essentially acts as the operational layer for AI, providing the necessary tooling, security, and scalability for enterprise-grade applications.
Machine learning engineers, data scientists, and Python developers who need to build, run, and scale AI and distributed applications without managing underlying infrastructure.
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2019
San Francisco, USA
Free
For individuals getting started with Ray and Anyscale. Includes a monthly allowance of Anyscale Compute Units (ACUs) and basic features.
Free
Standard
For teams and production workloads. Billed based on ACU consumption. Includes unlimited users, production SLAs, and support for running on your own cloud account (AWS/GCP).
Pay-as-you-go
Enterprise
For large organizations with advanced security and compliance needs. Includes all Standard features plus private networking, SSO/SAML, custom SLAs, and dedicated support.
Custom
Choose Databricks for a unified platform that combines data engineering, data science, and ML on top of Spark, covering a broader data lifecycle than Anyscale's Ray-focused compute.
Choose SageMaker if you are heavily invested in the AWS ecosystem and need a broad suite of tightly integrated tools for the entire ML lifecycle, from data labeling to model monitoring.
Choose Vertex AI for a unified ML platform within the Google Cloud ecosystem, offering managed services for training, deployment, and MLOps similar to SageMaker.
Choose to self-host the open-source Ray framework on Kubernetes or cloud VMs for maximum control and flexibility, if you have the engineering resources to manage the infrastructure.
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