Anyscale | Scalable Compute for AI and Python

Anyscale Platform with RayTurbo is an enterprise-grade AI compute platform that supercharges AI development and production at scale. Built around RayTurbo, Anyscale provides Pythonic APIs, precision orchestration, fault-tolerant infrastructure, and end-to-end tooling to run, monitor, and optimize AI workloads across any cloud, accelerator, or stack. It is designed to accelerate model evaluation, training, fine-tuning, and serving while maximizing GPU/compute utilization and minimizing cloud costs. The platform emphasizes developer experience, governance, security, observability, and seamless path from research to production.

How It Works

• RayTurbo powers scalable compute with Pythonic APIs to run workloads across CPUs/GPUs at any scale.
• Precision orchestration optimizes workloads for the chosen accelerator, cloud, or on-prem environment.
• Built-in governance, fault tolerance, lineage, and high-availability support mission-critical AI workloads.
• Anyscale provides enterprise-grade controls for security, privacy, admin, billing, and observability.
• The platform integrates with familiar ML libraries, frameworks, and MLOps tools, enabling a smooth development-to-production workflow.

Use Cases

• Scaling distributed training and data processing for large language models and ML workloads.
• Optimizing model evaluation, fine-tuning, and deployment at enterprise scale.
• Running multi-modal and RAG-based AI workloads with flexible data modalities.
• Managing governance, costs, and security in private cloud deployments.

Why Choose Anyscale

• RayTurbo: A supercharged Ray engine optimized for performance, efficiency, reliability, and planet-scale workloads.
• Pythonic APIs: Leverage standard Python code to describe distributed computation across a cluster.
• End-to-end Platform: From development on laptops to production-grade scale with observability and debugging tools.
• Enterprise Readiness: Governance, admin controls, security/privacy, and dedicated support.
• Observability: Deep visibility into experiments, production runs, and cost metrics to drive optimization.

Features

• RayTurbo: A supercharged version of Ray optimized for scale, efficiency, and reliability
• Pythonic APIs for distributed compute across GPUs/CPUs
• Precision orchestration for any accelerator, cloud, or on-prem setup
• Fault tolerance, lineage, and high availability for mission-critical workloads
• Enterprise governance: security, privacy, admin, billing, usage monitoring
• Observability and debugging tools for end-to-end ML workflows
• Seamless dev-to-prod workflow with minimal setup and minimal drama
• Compatibility with popular ML libraries, frameworks, and MLOps tools
• Private, configurable environment with option for on-prem or private cloud deployments