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Enterprise Layer Intelligence
Categories/DevOps & CI/CD/Azure CycleCloud
Azure CycleCloud

Azure CycleCloud

Founded by Microsoft in 2018

Manage and autoscale high performance computing clusters on Azure cloud infrastructure

Pricing model

Paid

Rating

★ Positive

Time to value

Immediate

You can use Azure CycleCloud to provision and manage High Performance Computing environments in the cloud. It allows you to set up compute clusters running standard schedulers like Slurm, PBS Pro, and Grid Engine. You can automatically adjust cluster size based on workload demands, connect distributed file systems like NFS or Azure NetApp Files, and control user access across your organization. CycleCloud lets administrators define custom cluster topologies using declarative templates while maintaining existing workflows without rewriting job scripts or queues.

What Azure CycleCloud does

Deploy compute clusters using predefined or custom configuration templatesConfigure autoscaling rules based on scheduler job queue depthMount shared network storage systems to cluster compute nodesManage access permissions and user authentication for cluster environmentsMonitor virtual machine performance metrics and resource consumptionTrack infrastructure spending across running high performance computing workloadsAutoscaling cluster compute nodes according to active job queuesNative compatibility with popular schedulers including Slurm and PBS ProDeclarative templating system for defining custom cluster topologiesCentralized web console and CLI for managing cluster environmentsIntegrated support for mounting NFS and parallel file systemsCost tracking and governance via Azure Cost Management integrationCustom scheduler support through an open autoscaling REST API

Pricing breakdown

Pricing model: Paid

Frequently asked

Microsoft Entra IDMicrosoft Entra ID

Used by

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Numan Hassan

@ Wan

In their stack

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Azure CycleCloud, HPC, High Performance Computing, Slurm, PBS Pro, IBM LSF, Grid Engine, HTCondor, cluster management, autoscaling, cloud infrastructure, batch computing, Azure VMs, parallel file systems, MPI workloads