What is AVD auto-scaling and why is it essential for cost management?
Azure Virtual Desktop (AVD) auto-scaling is the process of automatically adjusting the number of active session host virtual machines (VMs) in your environment to match real-time user demand. This practice is the single most effective strategy for managing AVD costs, as it prevents you from paying for idle compute resources during off-peak hours. By dynamically scaling your environment up and down, you can significantly reduce your Azure bill without impacting user performance.
This blog post was created in close collaboration with Nerdio, together with our Azure experts Stefan Beckman (MVP & NVP) and Pratheep Sinnathurai (MVP).
baseVISION is also a Nerdio Gold Partner, giving us direct access to best practices and deep product knowledge – to the benefit of our customers and the community.
Foundational Concepts of AVD Costs and Scaling
Understanding where your money goes is the first step toward effective cost management. In any AVD environment, several components contribute to your monthly Azure invoice, but one is far more significant than the others.
What are the primary cost drivers in an Azure Virtual Desktop environment?
While storage and networking have associated costs, the primary driver of your AVD operational expense is VM compute. You are billed for the time your session host VMs are running, making it the most critical area to optimize.
- Compute Costs: This is the hourly cost for your session host VMs. It is the largest and most variable expense in an AVD deployment and the main target for auto-scaling cost optimization.
- Storage Costs: These are the costs for the managed disks attached to your VMs and the storage for user profiles, typically on Azure Files or Azure NetApp Files using FSLogix.
- Networking Costs: This includes costs for data transfer (egress), which are usually minimal unless you are moving very large files out of Azure frequently.
- Licensing Costs: AVD entitlement is included with many common Microsoft 365 and Windows licenses, so there are often no additional licensing fees for the service itself.
How does auto-scaling directly reduce AVD operational expenses?
Auto-scaling reduces your expenses by ensuring you only pay for the compute resources you are actively using. By automating the process of turning VMs on and off, you can align your costs directly with your organization’s work patterns.
- Eliminating Waste: Auto-scaling powers off VMs during nights and weekends, which can prevent paying for thousands of hours of idle compute time per month. For example, shifting from 24/7 operation to a 10-hour workday, 5 days a week, can reduce VM compute costs by over 70%.
- Matching Supply to Demand: The system automatically adds VM capacity when user load increases and removes it when load decreases, preventing both performance bottlenecks and costly over-provisioning.
- Optimizing Session Host Usage: Effective scaling logic consolidates users onto the fewest possible VMs before shutting down empty hosts, maximizing the efficiency of your running resources.
Native Azure Methods for AVD Auto-Scaling
Azure provides built-in tools to help you implement basic auto-scaling for your host pools. These native options offer a starting point for cost optimization but come with complexities and limitations you should be aware of.
What are the built-in scaling plans in Azure for AVD?
The primary native tool for AVD auto-scaling is called a scaling plan. A scaling plan is a set of rules and schedules you associate with a host pool to define when session hosts should start and stop.
- Core Components: A scaling plan is built around schedules for different phases of the day (e.g., ramp-up, peak hours, ramp-down, off-peak).
- Triggers and Parameters: During ramp-down, you can configure scaling based on session limits per host to consolidate users and shut down empty VMs.
- Load Balancing: You can choose between two models. Breadth-first spreads users across all available VMs, while depth-first fills one VM before moving to the next. Depth-first is typically used for cost savings.
What are the limitations of using only native Azure scaling plans?
While useful, Azure’s native scaling plans can be rigid and may lack the intelligence required for dynamic work environments. This often results in more administrative overhead and less effective cost optimization.
- Time-Based Rigidity: Scaling plans are primarily schedule-based. They do not react well to unexpected demand, like users working late or an early start to the day.
- Limited Triggers: The logic is basic. There are no native triggers for scaling based on CPU/memory usage, active vs. disconnected sessions, or pre-staging hosts before users arrive.
- Management Overhead: Managing and customizing scaling plans for multiple host pools requires navigating the Azure portal or writing and maintaining your own code (via tools such as Bicep, Terraform, or PowerShell), which can become complex at scale.
- No Integrated Cost Analytics: Native tools do not provide built-in reporting to show you exactly how much money your scaling plan is saving, making it difficult to measure ROI.
How Advanced Automation Platforms Enhance AVD Auto-Scaling
To overcome the limitations of native tools, many organizations use specialized AVD management platforms. These solutions provide more intelligent, flexible, and powerful auto-scaling capabilities that deliver greater cost savings with less administrative effort.
How do specialized platforms simplify AVD auto-scaling and cost management?
Specialized AVD platforms act as a centralized management and automation layer on top of Azure. They are designed to simplify all aspects of AVD operations, especially the complex and critical task of auto-scaling.
- Unified Management: They provide a “single pane of glass” to manage scaling, host pool creation, image updates, and user session monitoring from one intuitive interface.
- Template-Based Configuration: You can configure sophisticated scaling logic once and apply it as a template to dozens of host pools, ensuring consistency and saving hours of administrative work.
- No Complex Scripting: These platforms replace the need for custom automation with user-friendly, GUI-based controls, making advanced automation accessible to any IT professional.
What advanced auto-scaling features does Nerdio Manager provide?
Tools like Nerdio Manager for Enterprise are built specifically to optimize AVD environments with advanced, cost-saving automation. They offer granular control and intelligence that goes far beyond the capabilities of native Azure scaling plans.
- Predictive and Reactive Scaling: Nerdio can analyze historical usage patterns to predictively start VMs just before users are expected to log in, ensuring resources are ready without being wasted. It also reacts in real-time to scale based on CPU, memory, or active session counts.
- Cost-Based Optimization: You can set scaling to target specific cost savings goals, giving you direct financial control over your environment.
- Granular Scaling Triggers: Nerdio provides a rich set of triggers, including the ability to differentiate between active and disconnected sessions, ensuring hosts are only shut down when they are truly no longer needed.
- Integrated Cost Reporting: The platform includes built-in dashboards that show your realized savings compared to a 24/7 baseline, making it easy to prove the ROI of your optimization efforts.
Key Auto-Scaling Strategies and Best Practices
Implementing effective auto-scaling requires more than just setting a schedule. Understanding key technical concepts and best practices will help you fine-tune your strategy for maximum savings and a seamless user experience.
What is the difference between breadth-first and depth-first scaling?
Choosing the right load-balancing method is critical for cost optimization. Your choice determines how user sessions are distributed across the available VMs in your host pool.
- Breadth-First: This method distributes new user sessions evenly across all running VMs. While it balances the load, it keeps more VMs active and is therefore less effective for aggressive cost savings.
- Depth-First: This method fills all available slots on one VM before placing a session on the next one. This is the preferred method for auto-scaling because it consolidates users, allowing empty VMs to be deallocated more quickly.
Platforms like Nerdio Manager allow you to easily configure the load balancing method that best fits your scaling strategy.
How should you handle disconnected user sessions in your scaling logic?
A common challenge is that disconnected sessions can keep a VM running, preventing it from being scaled in and costing you money.
- The Problem: A user who simply closes the Remote Desktop client without logging off leaves a disconnected session running. If this is the last session on a VM, that VM cannot be shut down.
- The Solution: Best practice is to set policies that automatically log off disconnected sessions after a specific period (e.g., 30-60 minutes). This can be done via Group Policy or, more easily, through an AVD management platform like Nerdio, which can be configured to handle this automatically as part of its scaling logic.
How to get started
Discuss with your basevision representative how an automation platform can help you implement a right-sizing strategy tailored to your needs and user work patterns.