With the Anyscale on Azure public preview, enterprises can now run foundation-model-scale AI workloads, from multimodal data preparation to training and inference, entirely inside their own Azure tenancy all while achieving up to 90% cost savings.
, /PRNewswire/ — Anyscale, the AI compute platform and creator of the Ray open-source project, today announced the public preview of Anyscale on Azure. Built on Azure Kubernetes Service (AKS) and Azure Resource Manager (ARM), this Azure native integration allows enterprises to build and operate production-scale AI workloads entirely within their own Azure tenancy, with the same security, identity, billing, and operating model as other Azure services. Organizations can build their own models and serve them on infrastructure they govern, keeping proprietary data and AI assets inside their cloud and replacing the unpredictable per-token economics of externally hosted model APIs with compute they own.
As enterprises move from AI experimentation to production, many are discovering that relying exclusively on externally hosted AI APIs creates growing costs and governance. In response, more are choosing to build their own AI systems on open-source and self-trained models, running on infrastructure they control, instead of depending completely on closed model endpoints from third-party providers. The motivations go beyond cost and governance to a larger ambition: turning proprietary data into AI that compounds as a long-term competitive advantage. Anyscale on Azure is purpose-built for this shift, giving enterprise AI and platform teams a unified compute foundation for the entire AI lifecycle, not just one stage of it.
“AI has quickly become one of the largest and least predictable line items in the enterprise IT budget,” said Keerti Melkote, CEO of Anyscale. “The companies pulling ahead are not necessarily spending less on AI. They are gaining more control over how that spend scales. Instead of only renting intelligence through APIs, they are building and operating AI systems inside their own cloud. Anyscale is for the teams that have decided their AI is core enough to own. We give them one platform to curate massive multimodal datasets, develop their own models, and deploy those models at scale, all now possible on the Microsoft Azure infrastructure they already trust.”
“There’s growing interest from enterprise customers in building AI inside their own Microsoft Azure cloud environment, on their own data, with more control over how costs scale,” said Brendan Burns, Technical Fellow and CVP, Azure Cloud Native, Microsoft. “Anyscale on Azure brings the popular open-source Ray engine directly into Azure, giving customers a great option to build and operate AI systems within their existing Azure environments.”
Xoople, a Europe-based geospatial AI company, was a natural fit for Anyscale on Azure because its mission sits at the center of where enterprise AI is headed: using proprietary, high-scale data to build AI systems that create lasting competitive advantage. Anyscale on Azure provided the foundation helping Xoople move faster from raw data to operational intelligence while keeping engineering teams focused on models and outcomes, not infrastructure.
“With Anyscale on Azure, Xoople can reliably run massive AI workloads over planetary-scale satellite imagery, transforming complex spectral data into decision-ready intelligence,” said Milos Colic, VP of Engineering at Xoople. “Anyscale lets our teams focus on models and outcomes rather than infrastructure, dramatically accelerating the path from experimentation to deployment. For our product teams and theirs, this means a faster stream of information, more agility, and improved risk management.”
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 collaboration focused on scaling embodied AI and the infrastructure behind it. As Wayve’s AI platform and data operations have grown, Microsoft 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
One platform to drive cost efficiency for the full AI lifecycle
As enterprises move from AI experimentation to production, many are discovering that AI cost is not driven only by model usage. It is also driven by the fragmented systems required to prepare data, train and fine-tune models, evaluate outputs, serve inference, and generate or process datasets with those models. To scale from development to production, AI and platform teams often start by stitching together CPU-oriented, cloud-native data processing platforms with hosted-model APIs for GPU-intensive workloads, creating brittle architectures that increase operational overhead, obscure cost visibility, and make production AI harder to govern.
Anyscale closes this gap with its runtime engine, purpose-built to handle both CPU and GPU workloads at scale. Built on Ray, the open-source compute framework that powers the processing demands of leading AI companies, including Cursor, Physical Intelligence, and xAI, Anyscale enables teams to run distributed multimodal data processing, training, fine-tuning, reinforcement learning, inference, and agentic workloads on one platform inside their Azure environment.
By reducing platform fragmentation, Anyscale helps teams improve GPU utilization, eliminate unnecessary data movement, streamline infrastructure operations, and replace unpredictable per-token API economics with compute they can govern directly. Customers report up to 4 times faster experimentation and up to 90% lower AI total cost of ownership versus fragmented stacks that combine cloud-native data processing engines with hosted model APIs.
An Azure Native Integration for enterprise AI security and sovereignty
Anyscale on Azure is delivered as an Azure Native Integration, the result of deep engineering collaboration between Anyscale and Microsoft. The platform runs entirely inside the customer’s own Azure tenancy on Azure Kubernetes Service (AKS), so proprietary data, training pipelines, and model weights never leave the boundary of the customer’s account.
The solution is provisioned through Azure Resource Manager (ARM) and inherits Azure’s native security and identity model. Every Anyscale resource is created, tagged, and governed like any other first-party Azure resource. Enterprises apply the same Microsoft Entra ID policies, role-based access controls, resource policies, , and audit controls they already use across the rest of their Azure account. For organizations in regulated industries, including financial services, healthcare, public sector, and others with strong security postures, this also means data residency, and expanding existing governance that carries forward to AI workloads.
Sovereignty here isn’t a label bolted on after the fact. It is the architectural starting point: customer-owned data and customer-owned models in the customer-owned tenancy and governance estate. All deployed and managed from the Azure Portal like any other Azure service.
Anyscale and Microsoft, a collaboration for the next wave of enterprise AI
This wave of enterprise AI will be defined by organizations operating their own AI systems, with their own models, on their own data, on infrastructure they govern. As AI workloads become increasingly distributed, multimodal, and GPU-intensive, enterprises are looking for a unified compute layer that can support the full AI lifecycle cost-efficiently and securely.
Anyscale on Azure is designed for this new era of control of enterprise AI: open source at the core, combining the proven scale of Ray and Kubernetes, and deployed where the data already lives.
And because the solution is delivered natively through Azure, Anyscale consumption draws down against existing Microsoft Azure Consumption Commitments (MACC), meaning enterprises can start building sovereign AI today against budgets they have already committed, with no new procurement cycle to navigate.
Learn more
- Connect with Anyscale at BUILD, Microsoft’s developer conference. Booth G201 [event page]
- To learn about the Anyscale on Azure features and functionality, read the launch blogs from Microsoft and Anyscale
- Get started with Anyscale on Azure directly from your Azure Portal
About Anyscale
Anyscale is the AI compute platform built by the creators of Ray, the most widely adopted open-source framework for scaling Python and AI workloads. Anyscale powers AI at companies including Coinbase, Bedrock Robotics, and Runway, and is used to train, fine-tune, serve, and process multimodal data for some of the largest AI systems in production.
SOURCE Anyscale
