Overview
Anyscale is a platform for distributed AI compute built on Ray, the popular open-source framework for scaling Python applications. Founded by the creators of Ray at UC Berkeley, Anyscale provides infrastructure for training, fine-tuning, and serving LLMs at scale. The platform is used by companies like OpenAI, Uber, and Shopify for distributed AI workloads.
Anyscale Endpoints provides hosted access to open-source models with optimized inference, while Anyscale Workspaces offers cloud-based development environments for AI. The platform excels at complex distributed workloads that benefit from Ray's architecture.
Key Features
**Ray-Based**: Built on proven distributed computing framework**Model Serving**: Optimized inference for open models**Distributed Training**: Scale training across clusters**Workspaces**: Cloud IDE for AI development**Auto-Scaling**: Elastic compute resources**Private Deployments**: VPC and dedicated clusters**Fine-Tuning**: Distributed model training**Multi-Cloud**: AWS, GCP supportWhen to Use Anyscale
Anyscale is ideal for:
Organizations already using RayLarge-scale distributed AI workloadsTraining and fine-tuning custom modelsTeams needing development infrastructureApplications requiring auto-scalingComplex distributed computing needsPros
Built on proven Ray frameworkExcellent for distributed workloadsUsed by leading AI companiesStrong for training and fine-tuningAuto-scaling capabilitiesGood for complex AI pipelinesPrivate deployment optionsStrong technical foundationCons
Requires Ray knowledgeMore complex than simple inference APIsPrimarily for advanced use casesSteeper learning curveExpensive for simple applicationsLess focus on foundation model APIsSmaller model selection than competitorsOverkill for basic inference needsPricing
**Endpoints**: $1 per 1M tokens (varies by model)**Workspaces**: Compute-based pricing**Enterprise**: Custom pricing for dedicated clusters**Serverless**: Pay for what you use