# Azure Machine Learning Compute Clusters With Nonzero Minimum Nodes Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/azure-machine-learning-compute-clusters-with-nonzero-minimum-nodes Azure Machine Learning compute clusters (AmlCompute) scale up when training or batch inference jobs are submitted and scale back down to the configured... By: PointFive Updated: 2026-09-28 [Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) The short version Azure Machine Learning compute clusters (AmlCompute) scale up when training or batch inference jobs are submitted and scale back down to the configured minimum node count when jobs finish. PointFive Research Cloud cost research at PointFive Azure service [Azure Machine Learning](https://www.pointfive.co/efficiency-hub/cloud-services/azure-machine-learning) Category [AI](https://www.pointfive.co/efficiency-hub/service-category/ai) Reference CER-0457 Type Inefficient Configuration ## Explanation Why the waste happens and who it affects. When the minimum is set above zero, usually to avoid waiting a few minutes for nodes to start, that many nodes stay allocated and billed around the clock even when no jobs run. Microsoft states that any value larger than 0 keeps that number of nodes running even if they are not in use. The cost is highest on GPU clusters and persists quietly because the cluster looks healthy and ready. Clusters created for a project or a demo often keep their warm nodes long after the work ends. ## Billing model The pricing dimensions that drive this cost. Cluster nodes are billed as the underlying VMs while they are allocated. Allocated nodes Each node is billed per hour at its VM size rate while allocated, whether or not a job is running on it Minimum nodes The cluster never scales below min\_instances, so those nodes bill continuously; with 0, idle nodes are deallocated and stop billing Idle time before scale down Nodes above the minimum stay allocated for this idle period after jobs finish, 120 seconds by default Load balancer Every 50 nodes of a compute cluster carry one standard load balancer billed per day, until the cluster is deleted ## How to detect 5 checks to find it in your estate. - List compute clusters with az ml compute list --type AmlCompute (or in studio under Compute \> Compute clusters) and flag those whose min\_instances is greater than 0 - Use az ml compute list-nodes on flagged clusters to see allocated nodes, and compare with job history to find nodes that sit idle between jobs or for days - Prioritize clusters with GPU VM sizes and dedicated tier, where idle minimum nodes cost the most - Check idle\_time\_before\_scale\_down on rarely used clusters; long values keep scaled-up nodes billed after each job - In Cost Management, review the VM costs attributed to each cluster over periods with no training runs ## How to fix 5 ways to remove the waste. - Set the minimum nodes to 0 with az ml compute update --min-instances 0, the SDK or studio, so Azure Machine Learning can deallocate nodes when no jobs run - Tune idle time before scale down to the team's iteration pattern: shorter for occasional jobs, longer only for rapid dev/test iteration where constant scale-up and scale-down would cost more - Where start-up latency matters, accept a few minutes of node provisioning instead of paying for always-on nodes, or move ad hoc jobs to Azure Machine Learning serverless compute, which removes cluster lifecycle management - Use low-priority tier for interruptible, checkpointed training and batch jobs; since March 31, 2026 these nodes are allocated as Spot VMs at variable Spot rates - Delete clusters that are no longer used, which also releases their quota and load balancer charges ## Documentation Vendor references for pricing and configuration. - [Manage and optimize costs - Azure Machine Learning learn.microsoft.com](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-optimize-cost) - [Create compute clusters - Azure Machine Learning learn.microsoft.com](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster) - [Plan to manage costs - Azure Machine Learning learn.microsoft.com](https://learn.microsoft.com/en-us/azure/machine-learning/concept-plan-manage-cost) - [az ml compute learn.microsoft.com](https://learn.microsoft.com/en-us/cli/azure/ml/compute) - [Pricing - Azure Machine Learning azure.microsoft.com](https://azure.microsoft.com/en-us/pricing/details/machine-learning/) ## Related inefficiencies [Browse the library](https://www.pointfive.co/efficiency-hub) - Azure Machine Learning CER-0456 ### [Azure Machine Learning Compute Instances Without Idle Shutdown](https://www.pointfive.co/efficiency-hub/inefficiencies/azure-machine-learning-compute-instances-without-idle-shutdown) An Azure Machine Learning compute instance is a single-user development VM for notebooks, VS Code and experiments. Microsoft notes that when you create a compute instance the VM stays on so it's available for your work, and it is billed... AI - Azure Machine Learning CER-0458 ### [Idle Managed Online Endpoint Deployments in Azure Machine Learning](https://www.pointfive.co/efficiency-hub/inefficiencies/idle-managed-online-endpoint-deployments-in-azure-machine-learning) Managed online endpoints serve models for real-time inference. Each deployment under an endpoint runs on its own dedicated VM instances, chosen by instance type and instance count, and Microsoft states that costs apply to the virtual... AI - Azure Cognitive Services CER-0238 ### [Always-On PTUs for Seasonal or Cyclical Azure OpenAI Workloads](https://www.pointfive.co/efficiency-hub/inefficiencies/always-on-ptus-for-seasonal-or-cyclical-azure-openai-workloads-ccb97) Many Azure OpenAI workloads - such as reporting pipelines, marketing workflows, batch inference jobs, or time-bound customer interactions-only run during specific periods. When PTUs remain fully provisioned 24/7, organizations incur... AI --- Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.