Anyscale

Anyscale's Canva case study describes a deployed AI platform and reports 50% lower cloud costs, peak GPU saturation and substantially faster model training, including an image-classification epoch reduced from 90 to six minutes.

Ray and Anyscale are used for production AI and distributed computing workloads across multiple enterprise contexts.

Ray and Anyscale are used for production AI and distributed computing workloads across multiple enterprise contexts.

Anyscale is built on Ray, a distributed-computing system designed to scale Python and AI workloads from a laptop to large clusters; the platform unifies multimodal data processing, distributed training, post-training and high-throughput inference across heterogeneous accelerators.

Anyscale commercializes Ray, a differentiated distributed computing framework for scalable AI workloads.

Anyscale commercializes Ray, a differentiated distributed computing framework for scalable AI workloads.

Anyscale maintains the Ray ecosystem while operating an enterprise cloud platform and engineering organization.

Anyscale maintains the Ray ecosystem while operating an enterprise cloud platform and engineering organization.

Anyscale entered a definitive agreement to join Nscale in July 2026, combining its production Ray software layer with Nscale GPU, data-center and power infrastructure while expanding investment in Ray, open source and customer compute capacity.

Ray and Anyscale span multimodal data processing, distributed training, post-training and inference; Ray supports CPUs, GPUs, TPUs and accelerator racks, integrates with PyTorch, vLLM, SGLang and XGBoost, and runs across AWS, GCP, Azure, Nebius, CoreWeave, Kubernetes, on-premises and laptops.

Distributed systems, ML infrastructure and Ray skills apply across model training, inference and data-intensive AI companies.

Distributed systems, ML infrastructure and Ray skills apply across model training, inference and data-intensive AI companies.