Cloud computing represents a fundamental shift in how businesses and individuals access and use technology resources. Instead of purchasing, owning, and maintaining their own physical data centers and servers, organizations can access technology services on an as-needed basis from a cloud provider. These services include computing power, storage, databases, networking, software, analytics, and intelligence. This model offers several key advantages over traditional on-premises infrastructure. These include cost savings, as users only pay for what they consume, eliminating the need for large upfront capital expenditures. It also provides global scale, allowing applications to be deployed in multiple regions around the world with just a few clicks. Cloud computing delivers superior performance, leveraging a global network of secure, modern, and efficient data centers. Furthermore, it enhances speed and agility, as new resources can be provisioned in minutes, allowing organizations to innovate and deploy applications much faster than before. Finally, it shifts the burden of hardware maintenance and data center management to the cloud provider, freeing up IT teams to focus on more strategic, value-adding activities. This paradigm shift has enabled a new wave of innovation, powering everything from simple mobile apps to complex artificial intelligence and data science workloads.
Understanding Microsoft Azure
Microsoft Azure is one of the leading public cloud computing platforms, offering a vast and ever-expanding collection of services designed to help organizations build, deploy, and manage applications. It provides a comprehensive suite of solutions that fall into several categories. Infrastructure as a Service (IaaS) allows users to rent IT infrastructure, such as virtual machines, storage, and networks, on a pay-as-you-go basis. Platform as a Service (PaaS) provides a platform for customers to develop, run, and manage applications without the complexity of building and maintaining the underlying infrastructure. Software as a Service (SaaS) delivers software applications over the internet, managed by the provider. Beyond these core models, Azure offers a rich portfolio of services for data and analytics, artificial intelligence and machine learning, the Internet of Things (IoT), security, and much more. This integrated approach allows developers and IT professionals to use the tools and frameworks they prefer. Azure’s global network of data centers ensures that applications and data can be placed close to users, providing low latency and high availability. It also supports hybrid cloud scenarios, allowing organizations to seamlessly integrate their on-premises data centers with the public cloud, providing a flexible and consistent environment. This comprehensive and flexible platform makes Azure a popular choice for businesses of all sizes, from small startups to large multinational corporations looking to digitally transform their operations.
The Challenge of Cloud Cost Management
While the cloud offers significant cost-saving potential, it also introduces new and complex challenges related to cost management. The pay-as-you-go model, which is a primary benefit, can also be a double-edged sword. Resources are easy to provision, but they are just as easy to forget. A developer might spin up a powerful virtual machine for a temporary test and forget to deprovision it, leading to unnecessary charges that can accumulate rapidly. This phenomenon is often referred to as “cloud sprawl.” As organizations scale their cloud usage, tracking and attributing costs across different departments, projects, or environments becomes increasingly difficult. Without clear visibility and accountability, cloud spending can quickly spiral out of control, eroding the very return on investment that motivated the move to the cloud in the first place. Effective cloud cost management requires a multi-faceted strategy. This includes implementing robust monitoring and reporting tools to gain visibility into consumption patterns, setting budgets and alerts to prevent overspending, and establishing strong governance policies for resource tagging and provisioning. It also involves continuous optimization, which means regularly reviewing resource utilization and right-sizing instances, deleting unused assets, and leveraging cost-saving purchasing options offered by the cloud provider. Managing cloud costs is not a one-time task but an ongoing process that requires collaboration between finance, IT, and business units to ensure that cloud investments are efficient and aligned with strategic goals.
What Are Virtual Machines?
Virtual machines, or VMs, are a foundational component of modern cloud computing and fall under the Infrastructure as a Service (IaaS) model. In essence, a virtual machine is a digital emulation of a physical computer. It runs on a physical server in a cloud provider’s data center but behaves like a completely separate computer system. A powerful physical server, known as a hypervisor, can host multiple, isolated virtual machines simultaneously. Each VM has its own virtual operating system (which can be Windows, Linux, or another OS), virtual CPU, virtual memory (RAM), virtual storage (disks), and virtual network interface. From the perspective of the user or the applications running on it, the virtual machine is indistinguishable from a physical machine. This technology, known as virtualization, allows for incredible flexibility and efficiency. Instead of dedicating an entire physical server to a single application, cloud providers can partition their hardware to serve many different customers and workloads. For the user, this means they can request a VM with a specific configuration, suchas two virtual CPUs, eight gigabytes of RAM, and a fifty-gigabyte solid-state drive, and have it provisioned for them in minutes. They can run their applications on this VM, install software, and configure it just as they would a physical server. When they are done, they can simply delete the VM and stop paying for it. This ability to rapidly provision and de-provision computing resources is a core advantage of the cloud.
Introducing Azure Spot Virtual Machines
Azure Spot Virtual Machines, also referred to as Spot instances, are a specific purchasing option for Azure VMs that offers a powerful way to tackle the challenge of cloud cost management. These are not a different type of hardware; they are the exact same virtual machines that are available through the standard pay-as-you-go model. The difference lies in how they are provisioned and their pricing model. Spot VMs leverage the concept of spare, unused computing capacity within Azure’s massive global network of data centers. At any given time, Azure has a certain amount of server capacity that is not being used by on-demand or reserved customers. Rather than letting this capacity sit idle, Azure offers it at a massive discount. This discount can be significant, often reaching up to 90 percent compared to the standard on-demand price. This dramatic cost reduction makes Spot VMs an extremely attractive option for a wide variety of computing tasks. However, this low price comes with a crucial trade-off: unpredictability. Because Spot VMs run on spare capacity, Azure reserves the right to reclaim that capacity at any time if it is needed for regular on-demand or reserved instances. This process is known as eviction. When an eviction occurs, the Spot VM is either stopped or deleted with very little notice. This inherent uncertainty means that Spot VMs are not suitable for all workloads.
The Core Concept of Spare Capacity
Understanding the concept of spare capacity is fundamental to understanding how and why Azure Spot VMs work. A massive cloud provider like Microsoft operates data centers all over the world, each filled with tens of thousands of physical servers. These servers host virtual machines for all of their customers. To ensure that they can always meet demand, even during peak times, they must maintain a significant amount of excess, or spare, capacity. If a large customer suddenly needs to deploy thousands of new VMs for a major product launch, or if a popular website experiences a sudden surge in traffic, Azure must have the resources available to handle it. This spare capacity is a necessary component of providing a reliable on-demand cloud service. However, from a financial perspective, any server that is powered on but not generating revenue is an inefficient use of capital. These idle servers still consume power, require cooling, and take up physical space, all of which represent a cost to the provider. Azure Spot VMs were created as a mechanism to monetize this otherwise unused capacity. By offering this spare capacity at a steep discount, Azure creates a win-win situation. The customer gets access to compute power at a fraction of the normal cost, and Azure generates revenue from resources that would have otherwise been idle. This is the central economic principle that underpins the entire Spot VM model.
Why Spot VMs Are So Inexpensive
The primary reason Azure Spot VMs are so inexpensive is directly tied to their reliance on spare capacity and the associated risk of eviction. The pricing is based on a dynamic market model. The price for a Spot VM is not fixed; it fluctuates based on the current supply of and demand for spare capacity within a specific Azure region and for a specific VM size. When there is a large amount of unused capacity (high supply) and not many people are requesting Spot instances (low demand), the price will be very low, often hitting the maximum discount of 90 percent or more. Conversely, if demand for compute in a particular region rises, perhaps due to a large-scale event or widespread adoption, the spare capacity will shrink. As supply decreases and demand for Spot VMs remains, the Spot price will increase. This pricing model is what allows Azure to offer such deep discounts. They are not guaranteeing that you will have the VM for any specific length of time. The standard on-demand price, in contrast, includes a premium for this guarantee of availability. When you pay the on-demand price, you are paying for the right to use that VM until you decide to turn it off, and Azure cannot take it away from you for capacity reasons. With Spot VMs, you are explicitly forgoing that guarantee in exchange for a significantly lower price. You are accepting the risk of interruption, and that accepted risk is what you are being compensated for through the massive cost savings.
Ideal Workloads for Spot VMs
Given the risk of eviction, Azure Spot VMs are not suitable for every type of application. The ideal workloads for Spot VMs are those that are interruptible, fault-tolerant, and not time-critical. These are often referred to as “stateless” or “loosely coupled” workloads. Batch processing jobs are a classic example. Consider a task like processing large volumes of data, rendering 3D animations, or running financial simulations. These jobs are often broken down into many small, independent tasks that can be processed in parallel. If one of the Spot VMs processing a task is evicted, the system can simply requeue that task and assign it to another VM, either a new Spot VM or an on-demand one, without losing the progress of the entire job. Development and testing environments are another excellent use case. Developers and quality assurance teams need environments to build and test new software features. These environments do not typically need to be available 24/7. Using Spot VMs to run these dev/test servers can drastically reduce the cost of the software development lifecycle. If a VM is evicted, the developer might experience a minor interruption but can simply restart the instance or acquire a new one. Other suitable workloads include large-scale scientific computing, data analysis tasks, and stateless web application front-ends that are part of a larger, auto-scaling group where the loss of a single instance is easily tolerated.
Unsuitable Workloads for Spot VMs
Just as important as knowing when to use Spot VMs is knowing when not to use them. Any workload that is critical, stateful, or cannot tolerate sudden interruptions should not be run on Spot VMs. The most obvious example is a production database. A relational database like SQL Server or PostgreSQL stores critical business data and requires constant, uninterrupted availability. A sudden eviction of the server hosting this database could lead to data corruption, data loss, and significant application downtime, which would be catastrophic for a business. Similarly, stateful applications that store important data or user session information in the local memory or disk of the VM are poor candidates. If the VM is evicted, all of that state is lost, leading to a poor user experience and potential data loss. Mission-critical line-of-business applications, such as an e-commerce checkout system, a payment processing gateway, or a company’s primary customer relationship management (CRM) software, must always be available. The potential cost savings from running such a critical workload on a Spot VM are dwarfed by the potential business cost of an outage. Other examples include single-instance applications that do not have any redundancy, or long-running computations that do not have a mechanism to save their progress, known as checkpointing. For these types of workloads, the stability and guaranteed availability of on-demand or Reserved Instances are the appropriate choice.
How Spot Pricing Works
The pricing for Azure Spot VMs is fundamentally different from the fixed, predictable pricing of on-demand or Reserved Instances. Spot pricing is dynamic and is determined by the supply of and demand for spare Azure capacity within a specific geographical region, for a specific VM size. This creates a fluctuating market price for compute. Azure makes the current Spot price for various VM instances publicly available. This price represents the cost you will pay per hour for the VM at that exact moment. The key feature is that you, the customer, can set a maximum price you are willing to pay. When you deploy a Spot VM, you can specify this maximum price. Your VM will only run as long as the current Spot price is at or below your specified maximum price. If the Spot price rises above your maximum bid, Azure will evict your virtual machine. This gives you a level of cost control. You can, for example, decide that you are only willing to run your workload if the cost is 70 percent less than the on-demand price. You would then set your maximum price to that 70 percent threshold. If you do not want to set a specific maximum price, you also have the option to set your maximum to be the standard on-demand price. In this scenario, you are essentially saying you are willing to pay up to the full regular price, but you want to take advantage of any available Spot discount. Your instance would then only be evicted for capacity reasons, not because the price exceeded your cap.
Eviction Types: Capacity Only vs. Price or Capacity
When you configure an Azure Spot VM, you must choose an eviction type. This setting directly determines the conditions under which your instance can be reclaimed by Azure. There are two options: “Capacity only” and “Price or capacity.” Understanding the difference is critical to managing your workloads. If you select “Capacity only,” you are telling Azure that you are not setting a specific maximum price you are willing to pay. Instead, your maximum price will be set to the standard on-demand price for that VM. With this setting, your Spot VM will only be evicted when Azure needs the capacity back for its on-demand or reserved customers. Your VM will continue to run and you will pay the fluctuating Spot price, whatever it may be, as long as it is below the on-demand price. This is a simpler model and is often recommended, as it protects you from price-based evictions. The second option is “Price or capacity.” When you select this, you must also specify a maximum price in US dollars per hour that you are willing to pay. With this setting, your VM can be evicted for two different reasons. First, just like the “Capacity only” option, it can be evicted if Azure needs the capacity back. Second, it can be evicted if the current Spot price rises above the maximum price you set. This option gives you more granular control over your costs, ensuring you never pay more than your specified limit. However, it also increases the likelihood of eviction, as your VM is now vulnerable to both capacity demands and price fluctuations.
Understanding the Eviction Policy: Deallocate vs. Delete
Once you have decided on your eviction type, the next critical configuration is the eviction policy. This policy defines what happens to your virtual machine and its associated resources at the moment an eviction occurs. Again, you have two choices: “Deallocate” and “Delete.” The “Deallocate” policy is the most common and generally recommended option for workloads where you might want to resume your work later. When a VM with this policy is evicted, Azure stops the VM and deallocates its compute resources (the CPU and RAM). The key thing is that the VM’s managed disks, which store the operating system and any data, are not deleted. You will stop paying for the VM compute costs, but you will continue to pay the small cost for the disk storage. The VM still exists in your Azure subscription in a “stopped (deallocated)” state. If Spot capacity for that VM size becomes available again in that region, the VM can be automatically or manually restarted, and it will resume from its previous state with its data intact. The “Delete” policy is more permanent. When a VM with this policy is evicted, the entire VM and all of its associated resources, including its operating system disk and any temporary disks, are permanently deleted. This is a destructive action. This option is only suitable for workloads that are completely stateless, where no valuable data is stored on the VM itself. For example, a task in a batch processing job might be configured with a “Delete” policy. If it gets evicted, the job orchestrator simply creates a new, fresh VM to pick up the task. The main advantage of the “Delete” policy is that you stop paying for all resources, including storage, immediately upon eviction.
How to Handle Evictions: Azure Scheduled Events
The single most important technical feature for building resilient applications on Spot VMs is Azure Scheduled Events. This is a metadata service that runs inside Azure and provides information to your virtual machine about impending platform maintenance or, crucially, Spot VM evictions. Your application running inside the VM can query a special, non-routable IP address to check for these events. When Azure decides to evict your Spot VM, it does not just immediately pull the plug. Instead, it sends a scheduled event notice to the VM, giving it a short warning before the eviction occurs. This warning period is typically very short, often just 30 seconds. While this is not a lot of time, it is enough for a well-designed application to perform critical shutdown tasks. Upon receiving the eviction notice, your application should immediately stop accepting new work. It should then attempt to save its current state or progress. This is known as “checkpointing.” This could involve writing in-progress data to a persistent storage service like Azure Blob Storage, updating a status in a database, or notifying a central queue manager that the task needs to be requeued. This “graceful shutdown” process is the key to fault tolerance. It ensures that minimal work is lost during an eviction and that the workload can be safely resumed on another instance. Any serious application designed to run on Spot VMs must integrate with the Azure Scheduled Events service to handle these pre-eviction notices.
Monitoring Spot VM Evictions
Beyond reacting to evictions in real-time with Scheduled Events, it is also important to monitor eviction events from an operational and analytical perspective. Azure provides several tools to help you track the “why” and “when” of your Spot VM evictions, which can help you optimize your deployments. The Azure portal provides a high-level view. For any given Spot VM or Spot VM Scale Set, you can view its properties and status. If an instance has been evicted, the portal will often display the reason. A common reason you might see is “Capacity,” indicating Azure reclaimed the resources for on-demand customers. For more detailed and programmatic analysis, you can use Azure Monitor. Azure Monitor collects metrics and logs from your Azure resources. You can create queries in Azure Monitor Logs (using the Kusto Query Language, or KQL) to find all eviction events that have occurred within a specific time frame. You can also set up Azure Monitor Alerts. For example, you could configure an alert to send an email or a text message to your operations team whenever a Spot VM is evicted. This allows youS to be proactively notified of disruptions. By analyzing this data over time, you can identify patterns. You might discover that a specific VM size in a specific region has a very high eviction rate, prompting you to switch your workload to a different VM size or a different region to improve stability.
Historical Pricing and Eviction Rates
Making an informed decision about where to run your Spot workloads involves more than just the current price. You also need to consider the historical stability of that VM size in that region. A VM that is 10 percent cheaper but gets evicted ten times as often may not be a good choice. To help with this, Azure provides historical data on both Spot pricing and eviction rates. This data is available through the Azure portal and via APIs. In the portal, when you are selecting a VM size, you can often view a chart showing the Spot price history for that size in your selected region over the past several weeks or months. This can help you identify price volatility. More importantly, Azure provides data on eviction rates. The eviction rate is expressed as a percentage, representing the likelihood of an instance being evicted within a given period. Azure often categorizes VM sizes by their eviction rate, using a simple scale (e.g., Low, Medium, High) or providing an actual percentage range. For example, a VM size might have an eviction rate of 0-5 percent, making it a very stable choice, while another might have a 15-20 percent eviction rate, making it suitable only for the most interruptible tasks. Before deploying a large-scale Spot workload, it is a critical best practice to research this historical data. You should aim to deploy your workload in regions and on VM sizes that offer the best combination of low price and low historical eviction rates to maximize both cost savings and stability.
Limitations and Considerations
While Spot VMs are a powerful tool, it is important to be aware of their limitations. First, not all Azure VM sizes and series are available as Spot VMs. While the selection is broad and includes many of the most popular general-purpose, compute-optimized, and memory-optimized instances, some specialized or very new VM types may not be offered on the Spot market. Second, quota limitations still apply. Your Azure subscription has quotas that limit the total number of vCPUs you can deploy in a particular region, and this quota is often split between standard on-demand cores and Spot VM cores. If you plan to deploy a very large-scale Spot workload, you may need to request a quota increase from Azure support beforehand. Another consideration is that Spot VM availability is not guaranteed. Just because a VM size is offered as a Spot instance does not mean there will be spare capacity for it when you need it. You may request a Spot VM and find that your request cannot be fulfilled dueTo a lack of available capacity in that region. Your deployment script or application logic must be prepared to handle this scenario, perhaps by retrying in a different region or falling back to a different, (perhaps smaller), VM size. Finally, Spot VMs cannot be converted to on-demand VMs. An instance deployed as a Spot VM will always be a Spot VM and will always be subject to eviction. If your workload needs to become permanent, you would need to deploy a new on-demand VM and migrate your application to it.
Spot VMs vs. Low-Priority VMs
For those who have been using Azure for some time, the concept of Spot VMs may sound familiar. Before the current “Spot VM” branding, Azure offered a similar service called “low-priority VMs,” which were primarily used within Azure Batch. It is important to understand that Azure Spot VMs are the evolution of this concept and have now largely replaced low-priority VMs. Spot VMs are the strategic, platform-wide solution for accessing spare capacity, available across single VMs, Virtual Machine Scale Sets (VMSS), and other services. Low-priority VMs were a more limited, legacy offering. While the core idea is the same (accessing spare capacity at a discount), Spot VMs offer more features and flexibility. For instance, the pricing model for Spot VMs is more dynamic and transparent. Spot VMs also introduced the “Deallocate” eviction policy, which was a significant improvement over the “Delete” only policy of low-priority VMs. While you may still encounter the term “low-priority” in some older documentation or specific corners of the Azure Batch service, for all new deployments and planning, you should focus on Azure Spot VMs. They are the modern, mainstream offering for this capability. Any new development or architectural planning should be based on the features, terminology, and behaviors of Azure Spot VMs as described in this series.
Checking for Spot Capacity
Before you attempt to deploy a large number of Spot VMs, it is a good practice to proactively check for available capacity. As mentioned, just because you can deploy a VM size as Spot does not mean there is currently enough spare capacity in your region to fulfill your request. A failed deployment can be disruptive, so checking first is a valuable optimization. Azure provides several ways to do this. You can use the Azure command-line interface (CLI) or Azure PowerShell to query the “Resource Skus” API. This API can be filtered to show you the current availability and restrictions for Spot VMs in a specific region. The API response will indicate if a particular VM size is currently constrained or unavailable for Spot deployment in your chosen location. The Azure portal also provides a visual representation of this. When you are creating a VM and you select a region and a VM size, the portal may display a notification if that combination has Spot capacity constraints. By checking for capacity before you initiate a deployment, your automation scripts can be made much smarter. If your primary choice of VM size (e.g., Standard_D4s_v3) is not available in your primary region (e.g., East US 2), your script can automatically check your secondary VM size (e.g., Standard_D2s_v3) or your secondary region (e.g., Central US) and deploy the workload there, rather than simply failing.
Methods of Deployment
When it comes to deploying Azure Spot Virtual Machines, you are not limited to a single method. Azure provides a flexible and powerful set of tools that cater to different user preferences and automation needs. The three primary methods for deploying Spot VMs are the Azure portal, the Azure Command-Line Interface (CLI), and Azure PowerShell. The Azure portal is a web-based graphical user interface (GUI) that is excellent for beginners, for visual learners, or for performing one-off tasks and exploring settings. The Azure CLI is a cross-platform command-line tool that allows you to manage Azure resources using concise commands in your terminal. It is favored by developers and administrators who prefer a Linux or macOS environment and is excellent for automation scripts. Azure PowerShell is a module that adds Azure-specific commands (cmdlets) to PowerShell, the powerful scripting and automation framework built by Microsoft. It is a natural choice for administrators who are already managing Windows environments. Beyond these three, you can also deploy Spot VMs using Azure Resource Manager (ARM) templates or third-party Infrastructure as Code (IaC) tools like Terraform. In this part, we will focus on the three primary methods for deploying both single Spot VMs and scalable groups of Spot VMs.
Deploying a Single Spot VM via the Azure Portal
The Azure portal provides the most visual and guided experience for creating a Spot VM. This method is perfect for your first deployment or for development and testing scenarios where you need to configure a single machine quickly. The process begins just like creating any other virtual machine. You first log in to the Azure portal and navigate to the “Virtual machines” service. You then click “Create” and select “Virtual machine.” This brings you to the main “Create a virtual machine” wizard. On the “Basics” tab, you fill in the standard details, such as your subscription, the resource group (a logical container for your resources), the VM name, the region you want to deploy in, and the operating system image you want to use (e.g., Ubuntu Server, Windows Server). The key step is on this same “Basics” tab. You will see a checkbox or option labeled “Run with Azure Spot discount.” You must check this box to designate the VM as a Spot instance. Once you check this, new options will appear immediately, allowing you to configure the Spot-specific settings. This simple checkbox is the only thing that differentiates the start of a Spot VM deployment from a standard on-demand one in the portal.
Step-by-Step Portal Configuration for Spot
After checking the “Run with Azure Spot discount” box in the portal, you are presented with the critical Spot configurations we discussed in Part 2. First, you must select the “Eviction type.” Your choices are “Capacity only” or “Price or capacity.” If you are just starting, “Capacity only” is the simplest and recommended choice, as it sets your maximum price to the on-demand price and only evicts you for capacity reasons. If you choose “Price or capacity,” a new field will appear where you must enter your “Maximum price” in US dollars per hour. You must be careful with this value; if you set it too low, your VM may be evicted frequently or may not even be deployed at all if the current Spot price is already higher than your bid. Next, you must choose the “Eviction policy.” Your options are “Deallocate” or “Delete.” For most workloads, “Deallocate” is the safer choice, as it preserves your OS disk, allowing you to restart the VM later. You would only choose “Delete” if your VM is completely stateless and you want all resources, including the disk, to be removed upon eviction. After configuring these Spot settings, the rest of the VM creation process is standard. You proceed through the “Disks,” “Networking,” and “Management” tabs to configure your storage, virtual network, and other settings just as you would for any VM. You then click “Review + create,” and Azure will validate your configuration and deploy your Spot VM.
Deploying a Single Spot VM with Azure CLI
The Azure CLI provides a powerful and repeatable way to deploy Spot VMs using your command terminal. This is ideal for automation scripts and for developers who prefer the command line. The primary command for creating a VM is az vm create. To make this VM a Spot instance, you need to add a few specific parameters. The most important parameter is –priority Spot. This is what tells Azure that you are requesting a Spot VM instead of a standard on-demand VM. Next, you must specify the eviction policy using the –eviction-policy parameter. Your options are Deallocate or Delete. For example, you would add –eviction-policy Deallocate. If you want to set a maximum price (the “Price or capacity” model), you would also include the –max-price parameter, followed by your maximum price in US dollars. For example, –max-price 0.05 would cap the hourly price at 5 cents. If you omit the –max-price parameter, Azure CLI defaults to the “Capacity only” model, setting your maximum price to the on-demand price, which is a common and simple configuration. A complete command would look something like this, (along with other required parameters like –name, –resource-group, and –image): az vm create –resource-group MyResourceGroup –name MySpotVM –image UbuntuLTS –priority Spot –eviction-policy Deallocate –admin-username azureuser –generate-ssh-keys. This single command can be saved and reused, making your deployments consistent and repeatable.
Deploying a Single Spot VM with Azure PowerShell
For users who are more comfortable in the Windows ecosystem or who already use PowerShell for automation, Azure PowerShell is the tool of choice. The process is conceptually identical to the Azure CLI but uses different syntax. The main cmdlet for creating a new VM configuration is New-AzVmConfig. To set up a Spot VM, you add specific parameters to this configuration. First, you set the priority using -Priority Spot. Then, you set the eviction policy using -EvictionPolicy, with the values Deallocate or Delete. For example, New-AzVmConfig -Priority Spot -EvictionPolicy Deallocate. Just like the CLI, if you want to specify a maximum price, you use the -MaxPrice parameter. If you omit this parameter, it defaults to the “Capacity only” model. Creating a VM in PowerShell is typically a multi-step process. You first define the VM configuration, including the Spot parameters, and store it in a variable (e.g., $vmConfig). Then you add other configurations like networking and disks. Finally, you pass this configuration object to the New-AzVM cmdlet to actually create the VM. A simplified example might start with: $vmConfig = New-AzVmConfig -VMName “MySpotVM” -VMSize “Standard_DS1_v2” | Set-AzVMOperatingSystem -Linux -ComputerName “MySpotVM” -Credential $cred | Set-AzVMSourceImage -PublisherName “Canonical” -Offer “UbuntuServer” -Skus “18.04-LTS” -Version “latest”. You would then add the Spot parameters to this configuration before running New-AzVM. This modular approach makes PowerShell very powerful for building complex and custom VM configurations.
Introduction to Virtual Machine Scale Sets (VMSS)
While deploying single Spot VMs is useful, the true power of Spot VMs is often realized when they are used in groups. Most scalable, fault-tolerant applications, like a web server farm or a data processing queue, do not run on a single VM. They run on a pool of identical VMs. Azure Virtual Machine Scale Sets, or VMSS, is the Azure service designed to manage these pools. A VMSS allows you to deploy and manage a set of identical, auto-scaling virtual machines. You define a “model” or “template” for the VM, including its size, OS image, and application. Then, you can simply tell the VMSS, “I need 10 of these,” and it will create and manage all 10 for you. If one instance fails, the VMSS will automatically detect it and create a new one to replace it. VMSS also integrates with Azure Autoscale, allowing youto automatically increase or decrease the number of instances in the set based on performance metrics (like CPU usage) or a fixed schedule. For example, you can set a rule to add more VMs when the average CPU load goes above 70 percent and remove them when it drops below 30 percent. This combination of automated management and auto-scaling is essential for building modern cloud applications.
Deploying Spot VMs in a Scale Set
Virtual Machine Scale Sets are perfectly integrated with Azure Spot VMs. This combination is one of the most powerful and cost-effective patterns in Azure. It allows you to create a large, scalable pool of compute power at a fraction of the normal cost. When you create a VMSS, you can specify that all instances in the set should be Spot VMs. The deployment process is very similar to creating a single Spot VM. If you are using the Azure portal, when you create a Virtual Machine Scale Set, there is a “Run with Azure Spot discount” checkbox, just like there is for a single VM. You check this box and then configure the “Eviction type” (Capacity only or Price or capacity) and “Eviction policy” (Deallocate or Delete) for the entire scale set. These settings will apply to all VM instances that are created by the scale set. The real power comes when you combine Spot VMSS with autoscaling. You can configure your scale set to have a minimum of, for example, 2 on-demand instances to guarantee baseline availability, and then allow it to scale out using up to 100 Spot VM instances when demand is high and Spot capacity is available. This “mixed-instance” or “flexible orchestration” model gives you a blend of high availability and low cost. The scale set will always try to deploy Spot VMs first to save you money, and if it cannot acquire them (due to lack of capacity), it can be configured to fall back to deploying on-demand VMs to meet your application’s scaling needs.
VMSS Deployment with Azure CLI
Deploying a Spot-enabled Virtual Machine Scale Set with the Azure CLI is done using the az vmss create command. The parameters are identical to the az vm create command for a single VM. You include the –priority Spot flag to designate the scale set as Spot-based. You then specify the eviction policy with –eviction-policy Deallocate or –eviction-policy Delete. If you wish to set a maximum price per instance, you would add the –max-price parameter. If you omit –max-price, all instances in the set will default to the “Capacity only” model. A typical command might look like: az vmss create –resource-group MyResourceGroup –name MySpotVMSS –image UbuntuLTS –priority Spot –eviction-policy Deallocate –instance-count 3 –admin-username azureuser –generate-ssh-keys. This command creates a new scale set with three Spot VM instances. If one of these instances is evicted by Azure, the VMSS will automatically try to create a new Spot instance to replace it and bring the instance count back to the desired capacity of three. This automatic self-healing is a core benefit of using VMSS. You can then use the az vmss scale command to manually change the instance count or configure autoscale rules to manage the size of the set automatically.
VMSS Deployment with Azure PowerShell
Similarly, you can create a Spot-enabled VMSS using Azure PowerShell. The process involves configuring a New-AzVmssConfig object and then passing it to the New-AzVmss cmdlet. To configure the scale set for Spot, you use the same parameters as for a single VM: -Priority Spot and -EvictionPolicy ‘Deallocate’ (or ‘Delete’). If you want to set a maximum price, you would add the -MaxPrice parameter with your bid. An example of the configuration step might be: $vmssConfig = New-AzVmssConfig -Location “EastUS” -Priority “Spot” -EvictionPolicy “Deallocate” -SkuName “Standard_D2s_v3” -InstanceCount 2. You would then complete this configuration object with details about the operating system, networking, and administrator credentials. Finally, you would create the scale set by running: New-AzVmss -ResourceGroupName “MyResourceGroup” -VMScaleSetName “MySpotVMSS” -VirtualMachineScaleSet $vmssConfig. Just like with the CLI, this scale set will now be managed by Azure. It will maintain the desired instance count of two, and if any instance is evicted, the VMSS will automatically try to provision a new Spot instance to take its place, ensuring your application’s capacity remains stable. This “set it and forget it” management is why VMSS is the recommended method for any scalable Spot VM workload.
The Cardinal Rule: Design for Interruption
This is the most important concept in this entire series. If you take away only one thing, let it be this: you must design your application with the non-negotiable assumption that your Azure Spot VM will be evicted. There is no “if” about it, only “when.” An application that runs perfectly on a standard on-demand VM will likely fail catastrophically on a Spot VM if it was not designed to handle sudden, involuntary shutdowns. You cannot treat a Spot VM as a reliable, long-running server. You must treat it as an ephemeral, temporary, and disposable resource. This shift in thinking is the key to successfully using Spot instances. Instead of building an application that fears failure, you must build an application that expects failure and knows how to recover from it gracefully. This “design for interruption” philosophy changes how you approach application architecture, state management, and task distribution. Every design decision should be made with the question, “What happens if this instance vanishes with a 30-second warning?” By embracing this “chaos-first” mindset, you can build systems that are not only cost-effective but also incredibly resilient and fault-tolerant, even more so than applications built on traditionally “safe” infrastructure.
Ideal Workloads for Spot VMs
We touched on this in Part 1, but it is worth exploring in more detail. The best workloads for Spot VMs are those that are, by their very nature, tolerant of interruption. These are often called “stateless” or “loosely coupled” workloads. Batch processing jobs are the quintessential example. Imagine you have one million photos that need to be resized. You can create a system where a “job” (a photo to be resized) is placed in a queue. A pool of Spot VMs in a Virtual Machine Scale Set pulls jobs from this queue, processes them, and puts the resulting resized photo in a storage account. If one of these worker VMs is evicted mid-process, the job is not lost. The system simply fails to receive an “all clear” for that job, and after a timeout, the job is placed back in the queue, ready for another worker to pick it up. No data is lost, and the overall process is merely delayed by a few minutes. Other ideal workloads include: render farms for 3D animation, large-scale scientific simulations (especially those that can be broken into parallel tasks), continuous integration builds and automated tests (which run, report a result, and are then destroyed), and stateless web application front-ends that are part of a large, load-balanced scale set (where the loss of one server is unnoticeable to users).
Unsuitable Workloads for Spot VMs
It is just as critical to understand what not to run on Spot VMs. Attempting to run an unsuitable workload on a Spot instance to save money is a false economy; the savings will be instantly erased by the cost and damage of an application outage. The most obvious unsuitable workload is any kind of single, stateful database, suchas a production SQL Server, MySQL, or PostgreSQL database. These systems are the single source of truth for an application, they store all their state on their local disks, and they require constant, uninterrupted availability. A sudden eviction would, at best, cause a complete application outage, and at worst, could lead to database corruption and permanent data loss. Stateful applications of any kind are poor candidates. For example, a legacy application that stores user session data in the server’s local memory would provide a terrible user experience on Spot. A user might be in the middle of filling out a form, only to have the server evicted, at which point all their data is lost, and they are logged out. Other unsuitable workloads include: critical infrastructure components like domain controllers or central jump boxes, interactive desktop sessions, and any long-running, single-process computation that has no ability to save its progress (i.e., it cannot be checkpointed). For these critical, stateful, and long-running tasks, the guaranteed availability of on-demand or Reserved Instances is the only appropriate choice.
Implementing Checkpointing
For long-running tasks that are not easily broken down into small pieces, the primary strategy for making them “Spot-safe” is checkpointing. Checkpointing is the process of periodically saving the “state” or progress of a computation to durable, external storage (suchas Azure Blob Storage or a database). The application is designed to save its work every 10 minutes, for example. When it starts, it first checks this external storage. If a checkpoint file exists, it loads that file and resumes its work from the last saved state, rather than starting from the very beginning. This technique is combined with the Azure Scheduled Events service we discussed in Part 2. Your application listens for the 30-second eviction notice. When it receives this notice, it does not stop and save its state then (30 seconds may not be enough time). Instead, it simply stops accepting new work and allows its most recent, regularly scheduled checkpoint to be its final state. When a new Spot VM is provisioned to take over the work, it finds the last successful checkpoint and continues from there. Instead of losing hours of processing, you only lose, at most, the 10 minutes of work since the last checkpoint. This makes even very long-running jobs (like training a machine learning model or running a multi-day simulation) feasible on Spot VMs.
Designing Stateless Applications
The most resilient architecture for Spot VMs is a stateless architecture. A stateless application is one that stores no persistent data or user session information on the local VM. Every VM in the cluster is identical and interchangeable. Any “state” required by the application is stored in a separate, durable, and highly available backend service. For example, a stateless web application would not store user session data in the server’s memory. Instead, it would store it in a distributed cache service like Azure Cache for Redis. When a user makes a request, the load balancer can send that request to any VM in the scale set. That VM then fetches the user’s session from the external Redis cache, processes the request, and updates the cache if needed. In this model, a Spot VM can be evicted at any time without any negative impact on the user. The load balancer will simply stop sending traffic to the evicted instance. The user’s next request will be routed to a different, healthy VM, which will then load their session from the same Redis cache, and the user will be completely unaware that a server just vanished. This architectural pattern is the gold standard for cloud-native applications. It makes your application incredibly resilient, scalable, and a perfect candidate for the massive cost savings of Spot VMs, as your compute instances become truly disposable commodities.
Using Queues for Decoupled Workloads
The batch processing example we used earlier relies on a key architectural pattern: decoupling with queues. This is a best practice that extends far beyond Spot VMs but is particularly valuable here. A queue (such as Azure Queue Storage or Azure Service Bus) acts as a buffer and a point of handoff between different parts of your application. In our photo resizing example, we have a “producer” (the part of the application that receives new photos) and a “consumer” (the pool of Spot VMs that do the resizing). The producer does not talk directly to the consumers. It simply places a “job message” (containing the location of the photo to be resized) onto the queue. This is a very fast operation. The producer can then immediately tell the user, “Your photo is being processed.” The pool of Spot VM consumers, working at their own pace, pulls messages from this queue. This is called a “decoupled” system. The producer and consumers do not need to know about each other or be available at the same time. This decoupling is what makes the system resilient to Spot evictions. If all the consumer VMs are suddenly evicted, the producer doesnot fail; it simply continues adding new job messages to the queue, which will grow. When new Spot VMs become available and spin up, they will connect to the queue and find a backlog of work, which they will then start to process. This pattern ensures no work is lost and that the system can gracefully handle a complete, temporary loss of its compute capacity.
Strategies for Fault-Tolerant Applications
Building on these patterns, we can define a set of key strategies for fault tolerance on Spot. First, use Virtual Machine Scale Sets (VMSS) instead of single VMs. VMSS provides the management, self-healing, and scaling capabilities that are required. If an instance is evicted, the VMSS will automatically try to provision a replacement, maintaining your application’s desired capacity. Second, use a mixed-instance VMSS. Do not rely on a single VM size. You can configure your VMSS to be able to deploy, for example, a Standard_D4s_v3, a Standard_D2s_v3, or a Standard_E4s_v3. The VMSS will then try to acquire any of these instances based on which one has the best combination of price and availability. This “VM size flexibility” dramatically increases your chances of acquiring Spot capacity and reduces your eviction rate, as you are not tied to the supply/demand of a single VM type. Third, combine Spot with on-demand. For critical workloads, use a VMSS configured for “flexible orchestration.” You can set a baseline of, for example, 3 “required” on-demand instances that will never be evicted. Then, you can configure the scale set to “scale out” with up to 50 Spot instances. This gives you a hybrid model: guaranteed stability for your core workload, with massive, low-cost elasticity to handle peak loads. This is often the best and most practical approach for production-grade applications.
Regional Capacity and VM Size Selection
A common mistake is to simply pick the cheapest VM size in your default region. This is not an optimal strategy. Spot prices and, more importantly, eviction rates, vary significantly between different Azure regions and different VM sizes. Before committing to a large-scale deployment, you must do your research. Use the historical pricing and eviction rate data that Azure provides. You may find that your primary region (e.g., East US 2) has a high eviction rate for the VM size you want, but a neighboring region (e.g., Central US) has a much lower eviction rate for a similar VM size, and the price is only marginally different. Deploying to the more stable region is almost always the correct choice, as it reduces disruption. Furthermore, do not fixate on a single VM size. As mentioned, flexibility is key. Your application should ideally not care if it is running on a D-series or an E-series VM, as long as it has the minimum required RAM and CPU. By building this flexibility into your application and your VMSS configuration, you make your workload far more resilient. If the Spot market for D-series VMs becomes constrained, your VMSS can simply start deploying E-series VMs instead, and your application will continue running without interruption.
Monitoring and Alerting
Once your Spot-based application is deployed, your job is not over. Effective monitoring and alerting are crucial. You cannot “set it and forget it” to the same degree as with on-demand instances. You must have a dashboard that shows you, at a glance, the state of your Spot VMSS. How many instances are running? How many evictions have occurred in the last hour? What is the current Spot price for your chosen VM types? Azure Monitor provides all the tools to build this. You should collect metrics on instance count, CPU/memory usage, and queue length (if you are using a queue). You should also collect the logs related to VMSS operations, which will show you every time an instance is deployed or evicted. Most importantly, you must set up alerts. You should have an alert that notifies your operations team if the number of running instances drops below a critical threshold. You should also have an alert if your job queue length grows beyond a certain point, as this could indicate that your VMSS is unable to acquire Spot capacity and your workload is falling behind. This proactive monitoring allows you to spot problems, such as a sudden spike in eviction rates, and manually intervene (e.g., by adding a new VM size to your scale set or temporarily falling back to on-demand) before it becomes a critical application outage.
Beyond Simple VMs: The Azure Ecosystem
So far, we have discussed Spot Virtual Machines largely in the context of single instances and Virtual Machine Scale Sets (VMSS). While VMSS is a powerful and flexible tool, in many real-world scenarios, you may not interact with it directly. Instead, you will use a higher-level Azure service that manages the compute infrastructure for you. Many of these “platform” services have been designed to integrate with Azure Spot VMs, allowing you to gain the cost benefits of Spot without having to manage the underlying VMSS and eviction logic yourself. These services provide an abstraction layer that is tailored to a specific problem, such as batch processing or container orchestration. Understanding how to use Spot instances within these managed services is key to maximizing your cost savings across your entire cloud estate, not just on your IaaS workloads. In this part, we will explore some of the most common and powerful integrations, including Azure Batch for large-scale parallel processing, Azure Kubernetes Service (AKS) for containerized applications, and the use of Spot VMs for CI/CD pipelines.
Using Spot VMs with Azure Batch
Azure Batch is a managed service built specifically for running large-scale parallel and high-performance computing (HPC) jobs. It is the perfect abstraction for the batch processing workloads we have repeatedly identified as ideal for Spot VMs. With Azure Batch, you do not create a VMSS directly. Instead, you define a “Batch pool,” which is a collection of compute nodes (VMs) that will execute your tasks. You then submit “jobs,” which are collections of “tasks,” to this pool. Azure Batch handles all the complex orchestration: it schedules tasks on nodes, monitors their execution, re-queues failed tasks, and scales the pool up or down. Azure Batch has first-class support for Spot VMs. When you create a Batch pool, you can choose to provision it using Spot instances instead of standard on-demand VMs. This is exposed as a simple configuration option. You define your pool, select your desired VM size, and set your target number of nodes. By choosing Spot, the Batch service will attempt to acquire these nodes at the deeply discounted Spot price. The service is inherently designed for interruption. If a node is evicted, the Batch scheduler will simply recognize that the task it was running failed to complete and will automatically reschedule that task on another available node in the pool. This integration is seamless and powerful. It allows data scientists, engineers, and researchers to run massive simulations or data processing jobs at a tiny fraction of the normal cost, without having to write any of the complex infrastructure management code themselves.
Configuring Batch Pools with Spot Instances
When you configure an Azure Batch pool to use Spot VMs, you have access to the same underlying controls, but they are presented in the context of the Batch service. You specify the VM size and the target number of nodes you want in your pool. You can also set a maximum price you are willing to pay per node, just as you would with a single Spot VM. The Batch service will then manage the acquisition and maintenance of this pool. One of the key features of Azure Batch is its ability to manage resource utilization effectively. If you submit a job with 10,000 tasks, Batch can automatically scale your Spot pool up to, for example, 1,000 nodes to process the work in parallel. As the queue of tasks diminishes, Batch will automatically scale the pool back down, ensuring you are only paying for the compute you are actively using. This combination of auto-scaling and Spot pricing is incredibly efficient. Furthermore, Azure Batch can be configured with a fallback mechanism. You can create a pool that targets a certain number of Spot VMs but also allows for a smaller number of on-demand VMs. If the Batch service is unable to acquire Spot capacity (due to high demand in the region), it can be configured to provision a few on-demand nodes to ensure that your high-priority work makes progress, albeit at a higher cost. This provides a safety net for time-sensitive batch processing.
Azure Kubernetes Service (AKS) and Spot Node Pools
Kubernetes has become the industry standard for container orchestration. Azure Kubernetes Service (AKS) is Azure’s managed Kubernetes offering, which simplifies the deployment and management of containerized applications. An AKS cluster is composed of a control plane (managed by Azure) and one or more “node pools.” A node pool is simply a Virtual Machine Scale Set that provides the compute resources (the “nodes” or “workers”) where your application’s containers will run. Just like with VMSS, AKS allows you to create node pools that are backed by Azure Spot VMs. This is a feature known as “Spot Node Pools.” This is an extremely powerful pattern for modern cloud-native applications. You can create an AKS cluster with multiple node pools. You might have one “system” node pool running on-demand VMs to host critical Kubernetes components and other essential services, ensuring they are always available. Then, for your application workloads, you can create one or more “user” node pools that are configured as Spot Node Pools. Your application’s container “pods” will be scheduled to run on these low-cost Spot nodes. This allows you to run your scalable, containerized microservices at a massive discount. Because Kubernetes itself is designed for failure (if a node or pod disappears, Kubernetes automatically reschedules it), it is a perfect partner for the interruptible nature of Spot VMs.
Benefits and Configuration of Spot Node Pools in AKS
The benefits of using Spot Node Pools in AKS are immense. For stateless, scalable microservices (like a web API or a message-processing worker), you can achieve the same “design for interruption” resilience we discussed in Part 4, but at the container level. If a Spot node is evicted, AKS detects the node failure. All the container pods that were running on that node are gracefully terminated and then rescheduled by the Kubernetes scheduler onto other healthy nodes in the cluster. For users, this means a brief, temporary reduction in capacity, which is often unnoticeable if you have designed your application to be scalable. When configuring a Spot Node Pool in AKS, you get the same controls you would with a VMSS. You can set the eviction policy (the default and recommended policy is “Deallocate”), and you can set a maximum price (or leave it blank to default to the on-demand price). AKS also adds a label to all nodes in a Spot pool (specifically, kubernetes.azure.com/spot=true). This allows you to use Kubernetes’ built-in scheduling features, like node affinities and taints, to control which workloads can or cannot run on your Spot nodes. For example, you can “taint” your Spot node pool to repel critical workloads, ensuring that only your interruptible, batch-style pods are ever scheduled to run on them. This gives you fine-grained control over your cluster’s topology and cost.
Using Spot VMs for Development and Testing Environments
We have mentioned development and testing environments as a great use case, and it is worth diving deeper into why. The software development lifecycle (SDLC) often generates a significant amount of “non-production” cloud spend. Developers need environments to write and debug code. Quality Assurance (QA) teams need environments to run automated test suites. These environments are often copies of the production environment but are only used intermittently. They might be used heavily during work hours (9 AM to 5 PM) but then sit completely idle overnight and on weekends. Running these environments on standard on-demand VMs is incredibly wasteful. This is a perfect scenario for Spot VMs. A development team can use Spot VMs for their individual development “sandboxes.” If a developer’s VM is evicted, it is a minor inconvenience; they simply restart the deallocated instance or provision a new one. The 10 minutes of disruption is a trivial price to pay for a 70-90 percent cost reduction on dozens of developer VMs. For QA environments, the case is even stronger. A test environment can be provisioned on a Spot VMSS, the automated test suite can be run, and once the results are reported, the entire environment can be spun down or deleted. The entire “testing” workload is, by its nature, temporary and interruptible, making it an ideal candidate for Spot.
CI/CD Pipelines with Spot-Based Agents
Expanding on the dev/test use case, we come to Continuous Integration and Continuous Delivery (CI/CD). Services like Azure DevOps Pipelines or GitHub Actions are used to automatically build, test, and deploy code every time a developer makes a change. This automation runs on “build agents” or “runners,” which are essentially VMs that execute the build and test scripts. A busy organization may have thousands of these builds running every day. These builds are typically short-lived (lasting 5-20 minutes) and highly parallelized. This is another perfect workload for Spot VMs. Instead of maintaining a fleet of expensive, always-on, on-demand build agents, you can configure your CI/CD system to use a Virtual Machine Scale Set configured with Spot instances. When a developer pushes new code, the CI system signals the VMSS to scale up. It provisions one or more Spot VMs to act as build agents. These agents pick up the build jobs, compile the code, run the tests, and report the results. After the build is complete, the agents are terminated. If a Spot VM is evicted in the middle of a build, the CI system is smart enough to detect this failure, and it simply re-queues the build to be picked up by another agent. By running your entire CI/CD infrastructure on Spot VMs, you can drastically cut the “overhead” costs of your software development process, freeing up that budget for value-adding activities.
Large-Scale Data Processing and Analytics
Modern data science and analytics often involve processing massive datasets. Whether you are using Apache Spark for data transformation or training a machine learning model, these workloads are compute-intensive and often parallelizable. Azure’s analytics services, such as Azure Databricks and Azure Synapse Analytics, are designed to integrate with Spot VMs. When you create a Databricks cluster, for example, you can configure its worker nodes to be Spot instances. This means your Spark jobs, which are already designed to be resilient to node failures, can run on deeply discounted hardware. This is a game-changer for data science teams. It makes it economically feasible to run more experiments, train more complex models, and re-process large historical datasets. A data scientist can experiment with a model, and when they are ready, they can launch a large cluster of hundreds of Spot VMs to train the final model overnight, all at a minimal cost. This integration lowers the barrier to entry for advanced AI and machine learning, as the compute cost is no longer a primary blocker. Instead of carefully rationing expensive compute time, teams can iterate and innovate more quickly, knowing that their large-scale processing is running on the most cost-effective infrastructure available.
Hybrid Models: Blending On-Demand and Spot
We have touched on this concept several times, but it is the key to deploying Spot in production. For many critical applications, a “100 percent Spot” architecture is too high-risk. The most robust and practical advanced scenario is a hybrid model. This is most easily implemented using a VMSS with “flexible orchestration” mode or an AKS cluster with multiple node pools. The pattern is always the same: you divide your workload into “critical” and “interruptible.” The critical part of your application (e.g., the minimum number of web servers to stay online, or the AKS system pods) runs on a small, fixed-size pool of on-demand VMs. This provides your baseline stability and is your “insurance policy.” Then, you configure the “elastic” part of your application to run on a large, auto-scaling pool of Spot VMs. This pool handles all traffic above your baseline. During normal operations, 90 percent of your traffic might be served by cheap Spot instances. If a massive eviction event occurs and you temporarily lose all your Spot capacity, your application does not fail. It simply degrades gracefully to its baseline, low-capacity state, supported by the on-demand instances. As Spot capacity becomes available again, the Spot pool scales back up, and full performance is restored. This hybrid model gives you the best of both worlds: the reliability of on-demand and the low cost of Spot.
Final Thoughts
Throughout this six-part series, we have journeyed from the basic concepts of cloud computing to the advanced architectural patterns of resilient, cost-optimized systems. We began by defining Azure Spot VMs as a mechanism to monetize spare data center capacity, offering massive discounts at the cost of evictability. We dove deep into the mechanics of pricing, eviction policies, and the critical Azure Scheduled Events service. We provided a practical, step-by-step guide to deploying Spot VMs via the portal, CLI, and PowerShell, emphasizing Virtual Machine Scale Sets as the key management tool. The core of our discussion focused on the architectural patterns necessary for success: designing for interruption, building stateless applications, implementing checkpointing, and using queues. We then saw how these patterns are abstracted and simplified by higher-level services like Azure Batch and Azure Kubernetes Service. Finally, we placed Spot VMs within the broader financial strategy, comparing them directly with On-Demand and Reserved Instances to create a comprehensive, blended cost-management model. Azure Spot VMs are not a simple “cheap mode” for your existing VMs. They are a powerful, strategic tool that requires a different way of thinking. They demand that we build applications that are resilient by default. By embracing the “design for interruption” philosophy, you can dramatically lower your cloud compute costs, freeing up valuable resources to invest in innovation, growth, and building the next generation of applications.