Anyscale | Scalable Compute for AI and Python

Anyscale offers several key features for scaling AI workloads. It supports multimodal data curation with large-scale pipelines for processing videos, images, text, and audio. Distributed model training is orchestrated across GPU clusters with elastic scaling and GPU observability. Batch embedding generation processes and generates embeddings at scale for search, retrieval, or training. Post-training frameworks like SkyRL and veRL are natively supported for LLM inference and training. Anyscale also provides fine-grained machine control, a seamless agent-first experience, multi-cloud orchestration, and price-performance optimized Ray workloads.

Anyscale is used for a variety of AI workloads. It is ideal for foundation model builders who need to scale distributed training, multimodal data curation, embedding generation, and post-training. The platform supports data-intensive pipelines for curating and preparing multimodal data, training models across GPU clusters, generating embeddings for downstream tasks, and running LLM inference and training with frameworks like SkyRL and veRL. Anyscale is also used for batch inference and can scale existing AI libraries like PyTorch, vLLM, SGLang, and XGBoost.

Anyscale offers a free start with $100 credit. For detailed pricing information, users are encouraged to get a demo or visit the Anyscale website.