Plan to manage costs for Azure Machine Learning
This article describes how to plan and manage costs for Azure Machine Learning. First, you use the Azure pricing calculator to help plan for costs before you add any resources. Next, as you add the Azure resources, review the estimated costs.
After you've started using Azure Machine Learning resources, use the cost management features to set budgets and monitor costs. Also review the forecasted costs and identify spending trends to identify areas where you might want to act.
Understand that the costs for Azure Machine Learning are only a portion of the monthly costs in your Azure bill. If you are using other Azure services, you're billed for all the Azure services and resources used in your Azure subscription, including the third-party services. This article explains how to plan for and manage costs for Azure Machine Learning. After you're familiar with managing costs for Azure Machine Learning, apply similar methods to manage costs for all the Azure services used in your subscription.
For more information on optimizing costs, see how to manage and optimize cost in Azure Machine Learning.
Cost analysis in Cost Management supports most Azure account types, but not all of them. To view the full list of supported account types, see Understand Cost Management data.
To view cost data, you need at least read access for an Azure account. For information about assigning access to Azure Cost Management data, see Assign access to data.
Estimate costs before using Azure Machine Learning
- Use the Azure pricing calculator to estimate costs before you create the resources in an Azure Machine Learning workspace. On the left, select AI + Machine Learning, then select Azure Machine Learning to begin.
The following screenshot shows the cost estimation by using the calculator:
As you add new resources to your workspace, return to this calculator and add the same resource here to update your cost estimates.
For more information, see Azure Machine Learning pricing.
Understand the full billing model for Azure Machine Learning
Azure Machine Learning runs on Azure infrastructure that accrues costs along with Azure Machine Learning when you deploy the new resource. It's important to understand that additional infrastructure might accrue cost. You need to manage that cost when you make changes to deployed resources.
Costs that typically accrue with Azure Machine Learning
When you create resources for an Azure Machine Learning workspace, resources for other Azure services are also created. They are:
- Azure Container Registry Basic account
- Azure Block Blob Storage (general purpose v1)
- Key Vault
- Application Insights
When you create a compute instance, the VM stays on so it is available for your work. Set up a schedule to automatically start and stop the compute instance (preview) to save cost when you aren't planning to use it.
Costs might accrue before resource deletion
Before you delete an Azure Machine Learning workspace in the Azure portal or with Azure CLI, the following sub resources are common costs that accumulate even when you are not actively working in the workspace. If you are planning on returning to your Azure Machine Learning workspace at a later time, these resources may continue to accrue costs.
- Load Balancer
- Virtual Network
Each VM is billed per hour it is running. Cost depends on VM specifications. VMs that are running but not actively working on a dataset will still be charged via the load balancer. For each compute instance, one load balancer will be billed per day. Every 50 nodes of a compute cluster will have one standard load balancer billed. Each load balancer is billed around $0.33/day. To avoid load balancer costs on stopped compute instances and compute clusters, delete the compute resource. One virtual network will be billed per subscription and per region. Virtual networks cannot span regions or subscriptions. Setting up private endpoints in vNet setups may also incur charges. Bandwidth is charged by usage; the more data transferred, the more you are charged.
Costs might accrue after resource deletion
After you delete an Azure Machine Learning workspace in the Azure portal or with Azure CLI, the following resources continue to exist. They continue to accrue costs until you delete them.
- Azure Container Registry
- Azure Block Blob Storage
- Key Vault
- Application Insights
To delete the workspace along with these dependent resources, use the SDK:
APPLIES TO: Python SDK azure-ai-ml v2 (current)
from azure.ai.ml.entities import Workspace ml_client.workspaces.begin_delete(name=ws.name, delete_dependent_resources=True)
If you create Azure Kubernetes Service (AKS) in your workspace, or if you attach any compute resources to your workspace you must delete them separately in Azure portal.
Using Azure Prepayment credit with Azure Machine Learning
You can pay for Azure Machine Learning charges with your Azure Prepayment credit. However, you can't use Azure Prepayment credit to pay for charges for third party products and services including those from the Azure Marketplace.
Review estimated costs in the Azure portal
As you create compute resources for Azure Machine Learning, you see estimated costs.
To create a *compute instance *and view the estimated price:
- Sign into the Azure Machine Learning studio
- On the left side, select Compute.
- On the top toolbar, select +New.
- Review the estimated price shown in for each available virtual machine size.
- Finish creating the resource.
If your Azure subscription has a spending limit, Azure prevents you from spending over your credit amount. As you create and use Azure resources, your credits are used. When you reach your credit limit, the resources that you deployed are disabled for the rest of that billing period. You can't change your credit limit, but you can remove it. For more information about spending limits, see Azure spending limit.
As you use Azure resources with Azure Machine Learning, you incur costs. Azure resource usage unit costs vary by time intervals (seconds, minutes, hours, and days) or by unit usage (bytes, megabytes, and so on.) As soon as Azure Machine Learning use starts, costs are incurred and you can see the costs in cost analysis.
When you use cost analysis, you view Azure Machine Learning costs in graphs and tables for different time intervals. Some examples are by day, current and prior month, and year. You also view costs against budgets and forecasted costs. Switching to longer views over time can help you identify spending trends. And you see where overspending might have occurred. If you've created budgets, you can also easily see where they're exceeded.
To view Azure Machine Learning costs in cost analysis:
- Sign in to the Azure portal.
- Open the scope in the Azure portal and select Cost analysis in the menu. For example, go to Subscriptions, select a subscription from the list, and then select Cost analysis in the menu. Select Scope to switch to a different scope in cost analysis.
- By default, cost for services are shown in the first donut chart. Select the area in the chart labeled Azure Machine Learning.
Actual monthly costs are shown when you initially open cost analysis. Here's an example showing all monthly usage costs.
To narrow costs for a single service, like Azure Machine Learning, select Add filter and then select Service name. Then, select virtual machines.
Here's an example showing costs for just Azure Machine Learning.
In the preceding example, you see the current cost for the service. Costs by Azure regions (locations) and Azure Machine Learning costs by resource group are also shown. From here, you can explore costs on your own.
You can create budgets to manage costs and create alerts that automatically notify stakeholders of spending anomalies and overspending risks. Alerts are based on spending compared to budget and cost thresholds. Budgets and alerts are created for Azure subscriptions and resource groups, so they're useful as part of an overall cost monitoring strategy.
Budgets can be created with filters for specific resources or services in Azure if you want more granularity present in your monitoring. Filters help ensure that you don't accidentally create new resources that cost you additional money. For more about the filter options when you when create a budget, see Group and filter options.
Export cost data
You can also export your cost data to a storage account. This is helpful when you need or others to do additional data analysis for costs. For example, a finance teams can analyze the data using Excel or Power BI. You can export your costs on a daily, weekly, or monthly schedule and set a custom date range. Exporting cost data is the recommended way to retrieve cost datasets.
Other ways to manage and reduce costs for Azure Machine Learning
Use the following tips to help you manage and optimize your compute resource costs.
- Configure your training clusters for autoscaling
- Set quotas on your subscription and workspaces
- Set termination policies on your training job
- Use low-priority virtual machines (VM)
- Schedule compute instances to shut down and start up automatically
- Use an Azure Reserved VM Instance
- Train locally
- Parallelize training
- Set data retention and deletion policies
- Deploy resources to the same region
- Delete instances and clusters if you do not plan on using them in the near future.
For more information, see manage and optimize costs in Azure Machine Learning.
- Manage and optimize costs in Azure Machine Learning.
- Manage budgets, costs, and quota for Azure Machine Learning at organizational scale
- Learn how to optimize your cloud investment with Azure Cost Management.
- Learn more about managing costs with cost analysis.
- Learn about how to prevent unexpected costs.
- Take the Cost Management guided learning course.
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