Anyscale provides a managed AI compute platform built on Ray, the open-source distributed computing engine its founders created. The platform lets organizations scale distributed training, multimodal data curation, embedding generation, batch inference, and post-training workloads across GPU clusters, with elastic scaling, GPU observability, and integration with libraries such as PyTorch, vLLM, SGLang, and XGBoost.
Anyscale's core value is operationalizing Ray. Teams can develop on the open-source framework and then run production-scale workloads on Anyscale's managed environment, gaining orchestration, scaling, and observability without building and maintaining their own distributed-systems infrastructure.
The company was founded in 2019 by Robert Nishihara, Philipp Moritz, and Ion Stoica, who created Ray out of UC Berkeley's RISELab. Nishihara serves as CEO, Moritz as CTO, and Stoica as executive chairman, giving Anyscale deep distributed-systems and academic research roots.
Anyscale is well funded by leading investors. It raised a $100 million Series C co-led by Addition and existing backers, with participation from Andreessen Horowitz, NEA, Intel Capital, and Foundation Capital, contributing to substantial cumulative funding and reflecting strong investor confidence in Ray as foundational AI infrastructure.
Ray's broad open-source adoption across the AI industry gives Anyscale a strong top-of-funnel: many teams already use Ray, and Anyscale monetizes the managed, production-grade experience around it, including support for modern training and inference stacks.
The platform is best for ML and AI infrastructure teams running large-scale distributed training, data, or inference workloads who want managed Ray rather than self-hosted clusters. Small teams with light workloads, or those not using distributed compute, may not need the platform's scope.