Anyscale

Anyscale is a managed platform built on Ray, the open-source AI compute engine created by the same team, purpose-built for teams running data-intensive AI workloads at production scale. It targets foundation model builders and AI engineering teams who need to orchestrate distributed training, curate massive multimodal datasets, generate batch embeddings, and run post-training pipelines without hand-managing Kubernetes or GPU infrastructure. Instead of wrestling with cluster orchestration, you express your workload as Python functions and classes decorated with Ray's @ray.remote, and Anyscale handles elastic scaling, hardware allocation, and observability across thousands of nodes. A core strength is its fine-grained hardware control: you can compose workloads where different distributed functions run concurrently on CPUs, GPUs, TPUs, or even accelerator racks like NVL72. Anyscale relies on Ray's in-memory distributed object store and RDMA transport for high-throughput communication between workers. The platform natively integrates with familiar AI libraries—PyTorch, vLLM, SGLang, XGBoost, and sentence-transformers—so you can scale existing code with minimal refactoring. For post-training, it supports frameworks like SkyRL and veRL natively on Ray, combining LLM inference and training for RLHF-style workloads. The typical use case portfolio includes four signature workload patterns: multimodal data curation pipelines (e.g., filtering video and image datasets), distributed model training via TorchTrainer, batch embedding generation for search or retrieval, and post-training with distributed trajectory generation. Each can be launched from a simple Python script that scales to dozens of GPUs. Anyscale offers advanced observability into GPU utilization and price-performance optimizations to keep idle compute low. Anyscale can be deployed in two modes: Hosted, where infrastructure is fully managed, or Bring Your Own Cloud (BYOC), which lets you run inside your own