What's new
The defining workload of the cloud era is no longer a web service or a database. It is a distributed AI training or inference job that needs thousands of GPUs, a coherent data plane, and a way for a handful of engineers to operate all of it without losing their weekends. Getting there is still harder than it should be. GPU capacity is fragmented across regions and SKUs, data is scattered across the lifecycle, and identity and credentials sprawl with every cluster a team adds.
Today, I’m excited to announce the public preview of Anyscale on Azure, bringing Anyscale’s managed Ray platform and the Anyscale Runtime natively to Azure, all running on Azure Kubernetes Service (AKS). It is the fastest path I have seen from a single notebook to a multi-region distributed AI job, running on the AKS clusters your platform team already operates.
Bringing the power of Ray to Azure
When I talk with customers who are scaling AI on Kubernetes, the same three problems come up:
- Capacity is fragmented. GPU supply lives across regions, SKUs, and quotas, and a single cluster is rarely enough to satisfy a serious training run.
- Data is stranded. Training, fine-tuning, and inference each pull from different stores, in different formats, with different access patterns, and that gap quietly slows every team down.
- Operations don’t scale. Credentials, identity, observability, and upgrade paths pile up cluster by cluster until the platform team becomes the bottleneck.
Anyscale and Microsoft have been working together to solve all three of these challenges, and today that work is ready for customers to try in public preview.
“At Anyscale, we’re building a company around Ray. Ray is the open-source distributed compute engine for scaling python and AI workloads. What we’re bringing with Anyscale is the production layer on top: a performance-enhanced runtime, developer tooling, and managed operations so teams can stay focused on building. And now this is all delivered in an Azure-native manner with enterprise readiness on AKS.”
Robert Nishihara, Co-founder, Anyscale
What’s available in public preview today
Starting today, you can provision and manage Anyscale on Azure directly from the Azure Portal or the Azure CLI, the same tools your platform team already uses for everything else. From Azure, you get native billing, identity, and cluster lifecycle management. From the Anyscale platform, your ML engineers get workspaces, job scheduling, and the Anyscale Runtime to build, train, and serve models without stitching infrastructure together. The solutions can help your organization across these three key areas:
- Scaling GPU capacity: Anyscale on Azure delivers elastic scalability through multi-cluster, multi-region capacity aggregation. A single Ray job can pull GPUs from wherever they happen to be available across your Azure footprint, so you stop building your training plan around the limits of any one cluster.
- Reliability across the AI lifecycle: The Anyscale Runtime, paired with Azure storage, gives you a unified data plane that spans experimentation, training, fine-tuning, and inference. Training data, checkpoints, and inference artifacts live in one place from dev to production. Engineers stop stitching pipelines together and start shipping models.
- Governance and security: Anyscale on Azure uses Microsoft Entra workload identity with automated credential management on the AKS-native control plane. Your platform team keeps the operational model they already know, with the same RBAC, the same audit trail, and the same policies, without rolling new identity systems for every new AI workload.
What customers are already building
Wayve is a self-driving startup training the next generation of self-driving models that power autonomous vehicles. Their work depends on aggregating GPU capacity at a scale no single region or cluster can deliver, which makes Anyscale on Azure’s elastic, multi-region capacity model exactly the unlock their training teams need.
“Wayve and Microsoft have a deep partnership focused on scaling embodied AI and the infrastructure behind it. As Wayve’s AI platform and data operations have grown, Azure has become a core part of its large-scale compute and ML stack.
Wayve uses Ray, and increasingly Anyscale on Azure to run distributed ML and data pipelines across large CPU and GPU fleets, supporting large-scale inference, analytics, and dataset processing with improved efficiency and resiliency. This enables Wayve to train and deploy its autonomous driving AI at the speed and scale needed for safe, real-world deployment globally."
Girish Venkataramani, VP of Engineering, Wayve AI
Xoople gathers global, high-quality Earth observation data through satellites, sensors, and other remote-sensing tools, then turns it into AI models that help customers understand the planet. Pipelines like theirs need to scale on demand without forcing a small team to stand up and operate Ray themselves, which is exactly what the managed model in Anyscale on Azure delivers.
"What Xoople is doing is incredibly important as they are creating Earth's System of Record, the data layer AI has been missing, whilst also defining a new category that will transform decision making for the Enterprise.
With Anyscale on Azure, Xoople can reliably run massively distributed AI workloads over planetary-scale satellite imagery, transforming complex spectral data into decision-ready intelligence.
Anyscale lets our teams focus on models and outcomes rather than infrastructure, dramatically accelerating experimentation to deployment. For our product teams and theirs, this means a faster stream of information, more agility and improved risk management.”
Milos Colic, VP of Engineering, Xoople
Built on the AKS you already run
Anyscale on Azure runs on standard Azure Kubernetes Service. There is no fork or special cluster type. It composes with the AI work AKS has shipped over the last year: Dynamic Resource Allocation for GPUs, Multi-Instance GPU, NVIDIA Dynamo for multi-node inference, KAITO for fine-tuning and RAG, and Azure Container Storage v2 for stateful AI workloads. If you prefer open-source Ray, KubeRay on AKS continues to be a great path. Anyscale on Azure is for teams that want a managed Ray platform with the Anyscale Runtime, fully integrated with the AKS clusters and Azure services they already use.
Frequently asked questions
What’s the release status?
Public preview, available today.
Do I need to migrate off open-source KubeRay?
No. Open-source Ray on AKS continues to be fully supported. Anyscale on Azure is for teams that want a managed Ray platform with the Anyscale Runtime. Learn how to easily onboard your open-source Ray into Anyscale on Azure.
How does pricing work?
Pricing is usage-based with two components: the Azure infrastructure you consume, and the Anyscale service layer.
- Azure infrastructure. You pay Azure for the underlying AKS infrastructure you use, including compute and GPUs, and those charges scale directly with actual usage.
- Anyscale service. Pay-as-you-go through Azure service meters with no upfront commitment, based on CPU, memory, and GPU type. Anyscale charges for the orchestration and management layer, not for GPU capacity itself. Rates align with Anyscale’s global pricing list, with no regional multipliers at the Anyscale service layer. Anyscale Enterprise Support is included with the service.
Get started
- Start a workspace on Anyscale on Azure → aka.ms/AnyscaleonAzureOverview
- Watch our session from Microsoft Build on AKS and the Anyscale announcement: BRK222: The honest practitioner's take on agentic AI on Kubernetes
A huge thank-you to the Anyscale team and to the Ray community. This work would not exist without either of them. This is the start of the work, not the end. We have more coming on the roadmap, including expanded region coverage, deeper integration with the AKS AI stack, and continued investments in elastic, multi-region distributed training and inference. I cannot wait to see what you build.
Microsoft