Autoscaling in Azure VMS: Strategies for 2025 & Beyond

Autoscaling in Azure VMs goes beyond just setting thresholds. Engineers must monitor granular metrics, such as CPU, memory, and custom metrics, to ensure resources scale efficiently. Misconfigurations such as overprovisioned VMs, idle resources, and improper scaling settings can drive up costs. While Azure’s built-in tools help identify these inefficiencies, automating the scaling process is where true savings happen. Sedai bridges this gap by continuously optimizing VM scaling, adjusting resources in real-time, and preventing cost spikes through smart, automated actions.

Managing Azure VMs efficiently can be challenging, especially when unused capacity quietly increases cloud costs. This is a common issue for engineers managing dynamic workloads, where demand can rise or fall without warning. The core challenge lies in scaling resources in a way that truly reflects actual usage.

Without a clear autoscaling strategy, teams often overpay for underutilized resources, run into performance issues during traffic spikes, or spend extra time managing complex configurations. Configuring Azure VM autoscaling correctly helps align capacity with workload needs while reducing operational overhead.

As Azure continues to expand rapidly, with Microsoft Cloud revenue reaching US$ 42.4 billion in a single quarter and Intelligent Cloud growing 21% year over year, effective autoscaling is more important than ever for balancing cost and performance.

In this blog, you’ll learn best practices for autoscaling Azure VMs, including setting precise scaling rules, using custom metrics, optimizing performance monitoring, and automating scaling decisions for more reliable and cost-efficient operations.

What are Azure VMs & Why Does It Matter?

Azure Virtual Machines (VMs) are scalable compute resources offered through Microsoft’s Azure cloud platform as part of its Infrastructure-as-a-Service (IaaS) portfolio.

They enable your teams to deploy custom applications, operate development or testing environments, and run complex enterprise workloads without the overhead of managing physical servers.

Each VM can be configured to match specific application requirements, giving you full control over compute, storage, networking, and operating system settings. Here’s why it matters:

1. Scale with Predictable Behavior

Azure VMs support both vertical and horizontal scaling to accommodate fluctuating workloads. VM Scale Sets automate this process by adding or removing instances in response to metrics such as CPU utilization, memory consumption, queue depth, or custom-defined thresholds.

This ensures that applications maintain consistent performance during peak demand while minimizing unused capacity during quieter periods.

2. Optimize Runtime Costs

The pay-as-you-go billing model, combined with autoscaling, enables you to avoid unnecessary compute costs. Your team can refine scaling thresholds to reduce waste, deallocate idle VMs during off-peak hours, and select the most efficient VM families for their workload profiles.

3. Design for High Availability

Deploying Azure VMs across availability zones and regions provides resilience against datacenter or zone-level outages. Your team can use this architecture to build fault-tolerant systems, distribute VMs across fault domains, and enable rapid failover with minimal interruption.

Once you know the importance of Azure VMs, it's easier to understand how autoscaling can optimize their performance.

Suggested Read: Azure VM Sizes & Pricing: A 2025 Guide for Engineering Teams

What is Azure VM Autoscaling & How Does It Benefit?

Azure VM autoscaling is a powerful capability that automatically adjusts the number of VM instances based on demand, ensuring applications scale efficiently without manual intervention. You can configure autoscaling for Azure Virtual Machines to match workload fluctuations, helping maintain performance while controlling operational costs.

Here's how Azure VM autoscaling benefits:

1. Dynamic Capacity Management

Azure VM Autoscaling intelligently adjusts the number of VM instances in a scale set according to real-time workload demand. This ensures applications can handle sudden traffic spikes efficiently while scaling down during periods of low usage, preventing unnecessary over-provisioning and resource waste.

2. Consistent Application Performance

By automatically scaling out when CPU, memory, or custom metrics exceed thresholds, autoscaling helps maintain consistent and predictable application performance. This ensures SLA requirements are met and reduces the risk of performance bottlenecks or downtime during sudden workload increases.

3. Cost Optimization

Autoscaling minimizes costs by automatically scaling in idle VMs during periods of low demand. You can define minimum and maximum instance limits, achieving the right balance between cost efficiency and performance reliability.

4. Reduced Operational Complexity

Manual monitoring and scaling of VM resources are no longer required. Predefined scaling rules and Azure Monitor metrics handle decisions automatically, freeing your teams from routine capacity management tasks and allowing them to focus on higher-value activities.

5. Controlled and Predictable Scaling

Scaling actions respect cooldown periods and adhere to minimum and maximum instance constraints. This prevents rapid oscillations or unintended resource spikes, giving teams predictable and reliable control over capacity planning, budgeting, and performance.

6. Smooth Integration with Azure Services

Azure VM Autoscaling works natively with Azure Monitor, Load Balancer, and Application Gateway. This allows scaling decisions to be driven by real-time metrics without the need for custom scripts or third-party tools, simplifying operations and ensuring smooth integration into existing Azure environments.

Once you understand Azure VM autoscaling, it's easier to see how it works.

How Does Azure VM Autoscaling Work?

Azure VM Autoscaling works by continuously monitoring metrics from Azure Monitor or custom sources to determine workload demand. Based on predefined scaling rules and thresholds, it automatically adjusts the number of VM instances in a scale set, while respecting minimum, maximum, and cooldown settings.

Here's how Azure Virtual Machines autoscaling works:

1. Define Autoscale Rules

To manage autoscaling effectively, you need to define rules that align with workload requirements. Azure Monitor allows up to 10 rules per autoscale profile, and each profile can define a minimum, maximum, and default number of instances.

These rules adjust capacity based on key performance metrics and enable automated scaling without manual input.

• Metrics-Based Scaling: Engineers configure autoscale rules using metrics such as CPU usage, memory usage, or custom metrics from Azure Monitor. For example, if CPU usage exceeds 80 percent for five minutes, scaling out adds additional VMs to handle the increased load.
• Custom Metrics: For more specific scenarios, engineers use custom indicators like response time or queue length to trigger scaling. If a queue exceeds a defined threshold, additional VMs can be deployed to prevent delays and maintain responsiveness.

2. Scaling Actions

After rules are set, Azure automatically adjusts VM instances in response to defined triggers, ensuring resources align with real-time demand.

• Scale Out: When demand rises, Azure adds VM instances. For example, during a product launch or promotional event, scaling out helps maintain performance during traffic spikes.
• Scale In: When demand decreases, Azure deallocates VMs to reduce costs. This prevents paying for idle instances during low-usage periods such as nights or weekends.

3. Integration with VM Scale Sets (VMSS)

VM Scale Sets simplify large-scale VM management by automating scaling and integrating with supporting Azure services.

• VMSS Autoscaling: VMSS manages identical VMs as a group, enabling smooth autoscaling. Azure Load Balancer distributes traffic during scaling operations to maintain balanced performance.
• High Availability: Integrating VMSS with Azure Traffic Manager and Load Balancer ensures traffic is routed only to healthy VMs, providing consistent availability during scaling events.

4. Autoscale Policies

Autoscale policies help refine scaling behavior, preventing unnecessary actions and aligning resource changes with both real-time activity and planned events.

• Cooldown Periods: You can configure a cooldown period (for example, 5 minutes) to prevent rapid, consecutive scaling actions. This stabilization period ensures resources are not adjusted too frequently, avoiding unnecessary churn.
• Scheduled Scaling: For predictable patterns, scheduled autoscaling adjusts VM counts at specific times. Scaling up during business hours and scaling down after hours optimizes performance and cost for workloads with consistent usage cycles.

5. Performance Monitoring and Alerts

Monitoring ensures autoscaling actions deliver the intended results. You can rely on Azure Monitor to assess performance and refine scaling rules.

• Azure Monitor: Key metrics such as CPU, memory, and disk performance help engineers adjust thresholds and optimize scaling behavior.
• Autoscale Reports: Azure provides reports detailing scaling actions, including added or removed VMs. These insights help identify patterns and refine rules for better efficiency.

6. Automation and Continuous Adjustment

Automation and ongoing refinement help maintain efficient scaling with minimal manual effort.

• Self-Optimizing Systems: Azure autoscaling adjusts resources based on predefined rules. Integrating Azure Automation further simplifies scaling tasks, keeping workloads aligned with demand.
• Predictive Scaling: For complex environments, predictive scaling anticipates future demand using historical data. This helps prevent performance issues during expected traffic surges, such as marketing campaigns or seasonal peaks.

As you are aware of how Azure VM autoscaling works, it's time to apply smart practices for optimizing its performance.

Smart Practices for Better Azure VM Autoscaling

Autoscaling plays a key role in keeping Azure VMs both efficient and cost-friendly. But to get the best results, you need to use smart practices that help the system scale in a more reliable and predictable way. Here are some best practices that can help you fine-tune your Azure VM autoscaling setup:

1. Use Custom Metrics for Fine-Tuned Scaling

Open Azure Monitor and create a custom metric like queue length, response time, or request rate for your application. Then head to the VMSS (Virtual Machine Scale Set) settings, go to Scale out or Scale in rules, and pick your custom metric as the trigger. 

For example, you can scale out when the queue length crosses 100 or when the request rate goes beyond 200 requests per second.

2. Use Predictive Scaling to Identify Traffic Surges

To enable predictive scaling, use Azure Machine Learning or historical data from Azure Monitor to understand usage patterns. Azure supports predictive autoscale for VM Scale Sets. It uses machine learning on historical CPU usage to forecast load and scale out up to 60 minutes in advance of predicted spikes.

3. Use Availability Zones for High Availability

In VMSS settings, set your deployment to run across multiple availability zones in the same region. This makes sure autoscaling spreads resources across zones, improving reliability. For example, if one zone goes down, Azure Traffic Manager can still route users to healthy VMs in other zones.

4. Automate Autoscaling Management with Azure CLI

To simplify scaling configuration, use Azure CLI or ARM templates. Engineers can create scripts to manage autoscaling across several VMSS instances. For example, the command az vmss update --name <VMSS Name> --resource-group <Resource Group> --set sku.capacity=<desired capacity> lets you update scaling settings programmatically.

Once you're aware of some smart autoscaling practices, advanced techniques can further improve the efficiency of your Azure VMs.

Must Read: Rightsizing for Azure Virtual Machines

Advanced Autoscaling Techniques for More Efficient Azure VMs

To achieve optimal performance, cost-efficiency, and scalability, you can use advanced autoscaling techniques for Azure VMs. These methods extend beyond the basic setup, providing more control over how resources are allocated and utilised. Here are some advanced techniques for fine-tuning autoscaling in Azure VMs:

1. Combine Autoscaling with Auto-Healing for Resilience

Autoscaling becomes more reliable when paired with auto-healing. Health probes in VMSS monitor VM status and automatically replace any instance that fails. This ensures scaling actions continue without interruption, even when some VMs become unhealthy.

Example:

For a microservices environment, configure health probes to monitor the health of services or containers. If a probe fails, VMSS replaces the instance and scaling proceeds as expected, improving overall resilience.

2. Use Multi-Region Autoscaling for Disaster Recovery

Multi-region autoscaling provides redundancy and supports disaster recovery. Use VMSS across multiple regions and use Azure Traffic Manager to route traffic to the most available location. Each region uses its own autoscaling rules and responds to local demand.

Example:

For a global SaaS platform, run VMSS in both East US and West Europe. Configure Traffic Manager to direct traffic to the region with the best capacity. Each region scales independently and maintains uptime even if one region faces issues.

3. Use Vertical Scaling for Resource-Intensive Workloads

Vertical scaling helps when workloads need larger VM sizes instead of additional instances. Configure VMSS to resize VMs automatically when CPU or memory usage exceeds defined thresholds. This is useful for databases or compute-heavy workloads that benefit from larger SKUs.

Example:

For an SQL database reaching high memory usage, set VMSS to scale up from Standard_DS2_v2 to Standard_DS3_v2 when memory exceeds 85% for 10 minutes. This gives the workload more headroom without increasing the VM count.

Understanding advanced autoscaling techniques helps make sense of how Azure Virtual Machine Scale Sets (VMSS) put them into practice.

Autoscaling With Azure Virtual Machine Scale Sets (VMSS)

Azure Virtual Machine Scale Sets (VMSS) provide a robust, scalable solution for managing large numbers of identical VMs. With autoscaling enabled, VMSS dynamically adjusts the number of VM instances based on workload demand, optimizing both performance and cost efficiency.

1. Configuring Autoscaling Policies

• Metrics-based scaling: Use Azure Monitor metrics such as CPU, memory, or custom metrics to define autoscaling triggers. For example, scale out when CPU exceeds 75% and scale in when it drops below 25%.
• Scaling rules: Define minimum and maximum instance counts, target instance sizes, and cooldown periods to prevent rapid fluctuations and maintain system stability.

2. Integrating Load Balancing

Connect your VMSS to Azure Load Balancer or Application Gateway to automatically distribute traffic across VM instances as they scale. This ensures consistent application performance during scaling events.

3. Advanced Scaling with Custom Metrics

For more granular control, incorporate custom metrics from Azure Monitor or third-party tools to track application-specific indicators. This enables scaling decisions based on nuanced performance signals beyond standard CPU and memory thresholds.

4. Managed OS and Disk Configuration

VMSS automates VM disk management using managed disks for high availability and resilience. It also supports automatic OS patching and upgrades, minimizing downtime and ensuring VMs remain healthy during scale operations.

5. Cost Optimization

Use Azure Spot VMs for non-critical workloads to reduce costs during low-demand periods. Spot VMs enable cost-efficient scaling without compromising application performance.

By effectively implementing VMSS autoscaling, engineering teams can achieve optimized performance, cost efficiency, and simplified management for large-scale VM deployments.

How Sedai Improves Autoscaling in Azure VMs?

Managing Azure VMs and scaling them effectively as demand fluctuates is often challenging. Many teams still rely on basic autoscaling methods based on static thresholds, which struggle to respond in real time to workload changes. This approach frequently leads to inefficient resource usage, underutilized capacity, and unnecessary cloud spend.

Sedai improves Azure VM autoscaling through autonomous, AI-driven optimization. The platform continuously learns from real-time workload behavior and dynamically adjusts compute, storage, and networking resources to closely match demand and eliminate inefficiencies.

By proactively managing scaling decisions, Sedai keeps your Azure environment responsive, cost-efficient, and operating at peak performance, without requiring constant manual oversight from engineering teams.

What Sedai Offers:

• Dynamic VM rightsizing: Sedai continuously analyzes real-time performance metrics across Azure VM instances and dynamically adjusts CPU and memory allocation. This ensures resources are sized based on actual demand, reducing cloud costs by 30% or more while improving application performance.
• Optimal instance selection: Sedai evaluates VM usage patterns to identify and select the most efficient Azure VM instance types for each workload. This reduces idle capacity and unnecessary spend, delivering up to 75% improvement in application performance.
• Autonomous scaling decisions: Powered by machine learning, Sedai makes scaling decisions based on real demand patterns rather than fixed thresholds. This results in fewer performance issues, as scaling actions reflect live workload behavior instead of predefined limits.
• Proactive resource management: Sedai detects early signs of performance degradation, resource pressure, or VM instability and resolves them before workloads are affected. Automating these actions significantly reduces manual intervention and increases engineering productivity by up to 6x.
• Full-stack cost and performance optimization: Sedai optimizes compute, storage, networking, and commitment levels together to ensure autoscaling remains cost-efficient without compromising performance. This holistic approach can deliver savings across Azure infrastructure.
• Multi-region and multi-cloud support: Sedai operates seamlessly across multiple Azure regions and integrates with AWS and GCP, providing a unified optimization layer. Whether running in a single region or a multi-cloud setup, Sedai scales infrastructure consistently and efficiently.
• SLO-driven scaling: Sedai aligns scaling decisions with application Service Level Objectives (SLOs) and Service Level Indicators (SLIs), maintaining consistent performance during traffic spikes and low-demand periods.

With Sedai, Azure VM instances scale autonomously in real time, staying aligned with workload demand while maintaining cost efficiency. By eliminating guesswork and manual tuning, Sedai helps keep your environment optimized, resilient, and high-performing.

If you’re looking to improve Azure VM autoscaling with Sedai, use our ROI calculator to understand how much you can save by reducing inefficiencies, improving performance, and eliminating manual intervention.

Final Thoughts

Autoscaling in Azure VMs helps maintain performance and cost efficiency, but one strategy that often gets overlooked is using a mix of VM types. By combining Reserved Instances for steady, baseline workloads with Spot VMs for unpredictable demand, engineers can cut long-term costs while maintaining flexibility.

This approach ensures your infrastructure remains optimized for performance, while taking advantage of lower-cost options whenever demand fluctuates. With Sedai, optimizing your Azure environment becomes effortless.

By continuously analyzing workload behavior and predicting resource needs, Sedai automatically adjusts VM types, keeping your environment both cost-efficient and high-performing.

This blend of intelligent autoscaling and resource optimization creates a responsive cloud infrastructure that consistently aligns with your workloads and business demands.

See your entire Azure environment in one place, optimize autoscaling automatically, and stop cloud spend from going to waste with Sedai’s continuous optimization.

FAQs

Q1. How do I choose the right VM size for autoscaling in Azure?

A1. Choosing the right VM size depends on your workload’s resource needs, such as CPU, memory, I/O, and traffic patterns. For dynamic environments, choose VM sizes that support both vertical and horizontal scaling. Azure Advisor can guide you on resource requirements, and combining VMSS with autoscaling rules ensures your VMs are right-sized.

Q2. Can Azure VMSS autoscale based on custom metrics beyond CPU or memory usage?

A2. Yes, Azure VMSS can scale based on custom metrics such as queue length, response time, or request rate. By using Azure Monitor to define these metrics, you can trigger scaling actions that better reflect real workload demands.

Q3. How does autoscaling impact Azure VM performance during scaling events?

A3. Autoscaling automatically adjusts resources, but performance can be affected if scaling happens too frequently or isn’t aligned with actual demand. Setting cooldown periods and monitoring key performance indicators, like CPU and memory, allows you to fine-tune thresholds and keep applications stable during scaling events.

Q4. How does Azure autoscaling integrate with other Azure services like Azure Load Balancer and Traffic Manager?

A4. Azure autoscaling works smoothly with Load Balancer and Traffic Manager to distribute traffic across scaled instances. When VMSS scales out, the Load Balancer routes traffic only to healthy VMs, preventing overload on any single instance.

Q5. How can I prevent over-scaling in Azure VMSS while maintaining performance?

A5. To avoid over-scaling, set scaling thresholds carefully so autoscaling triggers only under sustained demand. Define minimum and maximum instance counts to ensure your environment scales within reasonable limits, balancing performance needs with cost efficiency.