Microsoft’s 2017 announcement with Cray described customer-specific Cray XC and Cray CS supercomputers, with ClusterStor storage, installed in select Azure data centers and connected to Azure services. It was a plan for provisioned systems—not evidence of an on-demand supercomputer anyone could rent instantly, or confirmation that this exact offer is available today.
What did Microsoft announce with Cray in Azure?
On October 23, 2017, Microsoft and Cray said they planned to offer Cray XC and Cray CS supercomputer systems in select Microsoft Azure data centers. The systems would include Cray ClusterStor storage, connect directly to Azure’s network, and integrate with Azure services such as virtual machines, Data Lake storage, Microsoft’s AI platform, and Azure Machine Learning. Data Center Knowledge’s account of the announcement described the approach as customer-specific provisioning.
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That distinction matters: the article interpreted customer-specific provisioning as deployment arranged case by case, rather than a machine instantly available through a routine self-service purchase. That was the publication’s interpretation, not a quoted contractual definition of Microsoft’s service terms.
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The announcement pointed toward access to supercomputer capacity through Azure, but it did not establish a standard rental plan, public price list, immediate self-service signup, or current availability of the specific Cray XC/CS configuration. A business evaluating such an arrangement would need to confirm the deployment model and commercial terms with the provider rather than assume that “cloud service” means ordinary pay-as-you-go access.
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The 2017 plan’s appeal was the proposed connection between specialized HPC hardware and Azure’s network and services. That could support workflows that need supercomputer-scale compute alongside cloud-based data or machine-learning resources. The announcement itself did not provide workload performance guarantees or a comparison with other cloud providers.
What workloads use cloud supercomputing?
Supercomputing is useful when a job needs substantial parallel computing, high-throughput data processing, or both. The 2017 coverage cited genome sequencing, weather-pattern analysis, and aircraft aerodynamics as examples of compute-intensive work. More broadly, the announcement reflected how data analytics and AI were expanding demand for high-performance computing beyond traditional scientific research.
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Whether a cloud supercomputer suits a particular workload depends on more than peak compute capacity. Buyers should establish the required hardware and interconnect, storage throughput and capacity, software compatibility, data-transfer needs, geographic location, access model, and the performance of their own applications.
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How did Azure supercomputing develop after the 2017 announcement?
Later Azure announcements show other HPC and AI infrastructure projects, but they should not be mistaken for proof that the 2017 Cray XC/CS offer remains available.
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- Reverse (Tails): The iconic cylindrical form of the Cray-1 supercomputer takes center stage - its distinctive C-shaped bench seating and stacked circuit modules rendered in striking numismatic detail. The design pays tribute to Seymour Cray's revolutionary engineering vision and the Wisconsin facility where the future of computing was born.
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UK Met Office deployment
In 2021, Microsoft described a separate HPE Cray EX supercomputing solution for the UK Met Office, with operations expected to begin in July 2022. Microsoft said the first generation would provide more than 1.5 million processor cores and more than 60 petaflops of aggregate peak capacity, alongside an active archive with nearly 4 exabytes of data capacity. The company presented it as an integrated, managed service combining Cray systems, Azure technologies, and a high-performance data archive. These figures describe that specific Met Office project, not a general Azure customer configuration. Microsoft Azure’s announcement provides the project details.
AI infrastructure and dedicated clusters
Microsoft’s 2022 account of Azure AI infrastructure described interconnected NVIDIA GPUs and InfiniBand. It cited the November 2022 Top500 list, reporting that Azure ranked in the top 15 and that five systems in the list’s top 50 used Azure infrastructure with NVIDIA A100 GPUs. Those are Microsoft’s reported results for that dated list edition—not current rankings or evidence that every Azure customer could access those systems. Microsoft’s infrastructure article explains its account.
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Microsoft said in 2022 that Meta would use a dedicated Azure cluster of 5,400 NVIDIA A100 GPUs for some large-scale AI research workloads. That customer example demonstrates a dedicated deployment, not general availability or a performance guarantee for other customers. Microsoft Azure’s announcement about Meta describes the arrangement.
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Microsoft’s 2020 OpenAI supercomputer
In 2020, Microsoft announced a supercomputer in Azure for OpenAI training and called it an initial step toward a platform for other organizations and developers. The announcement quoted Microsoft Chief Technical Officer Kevin Scott saying, “The supercomputer will enable ‘new applications that are hard to even imagine right now.’” That was a forward-looking statement, not a measured result or confirmation of the 2017 Cray service’s status. Microsoft News’ announcement gives the original context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to verify before buying cloud supercomputing capacity
The 2017 announcement is best understood as a proposal to bring customer-specific supercomputer deployments into Azure, not as a complete service catalog. For a present-day purchase decision, ask the provider to document:
- Deployment and access: Is the system dedicated, shared, or provisioned for one customer, and how are capacity and access scheduled?
- Hardware and interconnect: Which processors, accelerators, and networking technologies are included?
- Storage: What capacity, throughput, and data-retention options are available, and how is storage connected to compute?
- Software integration: Which cloud services, machine-learning tools, and HPC software environments are supported?
- Location and terms: Where can the system be deployed, what are the commercial terms, and what support is included?
- Workload performance: Can the provider validate performance using a representative version of your application and data?
These checks are essential because the historical announcement and later project examples do not establish a current, like-for-like comparison of Azure’s supercomputing offers with those of other providers.
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