Coverage
Azure Machine Learning cost optimisation for MSPs
Compute instances keep running after creation until someone stops them, and costs carry on even when nobody is working. Dependent resources such as the storage account and container registry also keep accruing after a workspace is deleted, so spend leaks wherever shutdown and clean-up aren't enforced.
18
automated finders for Azure Machine Learning
Read-only, across every connected client tenant, tied to real billing data.
- Modernise
- Remove unused
- Rightsizing
- Scaling
- Off-hours schedules
Where Azure Machine Learning spend goes to waste
The main cost levers Microsoft documents for Azure Machine Learning, and the ones an MSP should check across every client.
Shut down idle compute instances
Turn on idle shutdown with a threshold between 15 minutes and three days of inactivity, and add start and stop schedules to match working hours. A running custom application or an open VS Code connection stops an instance counting as idle. [2]
Scale training clusters to zero
Set each compute cluster's minimum node count to 0 so nodes are deallocated once jobs finish; any higher minimum keeps those nodes running and billed while idle. Shorten the scale-down delay for occasional training. [3]
Run interruptible work on Spot capacity
Clusters configured for low priority are allocated as Spot VMs, billed at a variable regional rate that never exceeds pay-as-you-go. Use them for training that checkpoints or batch scoring that can be resubmitted. [3]
Rightsize and autoscale online endpoints
Managed online endpoints charge for the VMs behind each deployment, so choose instance type and count to fit expected load. Add autoscale rules driven by metrics such as CPU utilisation, by a schedule for peak hours, or both. [4]
Move non-urgent scoring to batch endpoints
Batch endpoints provision cluster compute when a scoring job starts and release it when the job finishes, and the endpoints themselves carry no charge. They suit large, latency-tolerant inference. [5]
Cap spend with quotas
Compute quota is set per VM family and region for each subscription, and workspace-level quotas give finer control over what each workspace can use, including blocking VM families it should not. [3]
How Cerberus Cloud covers Azure Machine Learning
Cerberus Cloud runs 18 automated finders for Azure Machine Learning across every connected client tenant. Each one ties a technical signal to real billing data, so findings arrive with an estimated recoverable value, ranked by impact, effort and risk, with evidence and remediation scripts for your engineers to review.
Access is read-only, so nothing changes in a client environment until your team decides it should. See how it works or the multi-tenant portfolio view.
- Modernise
- Remove unused
- Rightsizing
- Scaling
- Off-hours schedules
Frequently asked questions
- How can we enforce idle shutdown across every client's workspaces?
- Assign the built-in Azure Policy definition requiring idle shutdown on compute instances, using Audit to flag gaps or Deny to block non-compliant instances. It can be assigned at subscription or resource group scope in each client environment.
- What keeps costing money after a client's workspace is deleted?
- Deleting a workspace through the portal or CLI leaves its container registry, storage, Key Vault and Application Insights behind, and they keep accruing charges until removed. Attached Kubernetes compute also has to be deleted separately.
- Can we see what each online endpoint costs?
- Yes. In Cost Analysis, filter to the workspace resource and use the azuremlendpoint and azuremldeployment tags to see each endpoint and deployment on its own. You need Billing Reader access or higher on the subscription.
See Azure Machine Learning findings on a sample portfolio
A 15-minute walkthrough: tenant connection, prioritised findings, remediation scripts and savings history.
Sources
Last checked . Product details change; tell us if something here is out of date.
- Microsoft Learn: Plan to manage costs for Azure Machine Learning
- Microsoft Learn: Create a compute instance (idle shutdown and schedules)
- Microsoft Learn: Manage and optimise Azure Machine Learning costs
- Microsoft Learn: Online endpoints
- Microsoft Learn: Batch endpoints
- Microsoft Learn: Azure Policy for Azure Machine Learning
- Microsoft Learn: View costs for managed online endpoints