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Microsoft Azure is a global cloud provider, meaning you can provision resources anywhere in the world. You can provision resources rapidly to meet a sudden demand, or to test out a new feature, or on accident. If you accidentally provision new resources, you may not be aware of them until it’s time for your invoice. Cost Management is a service that helps avoid those situations.

What is Cost Management?

Cost Management provides the ability to quickly check Azure resource costs, create alerts based on resource spend, and create budgets that can be used to automate management of resources.

Cost analysis is a subset of Cost Management that provides a quick visual for your Azure costs. Using cost analysis, you can quickly view the total cost in a variety of different ways, including by billing cycle, region, resource, and so on.

You use cost analysis to explore and analyze your organizational costs. You can view aggregated costs by organization to understand where costs are accrued and to identify spending trends. And you can see accumulated costs over time to estimate monthly, quarterly, or even yearly cost trends against a budget.

Cost alerts

Cost alerts provide a single location to quickly check on all of the different alert types that may show up in the Cost Management service. The three types of alerts that may show up are:

• Budget alerts
• Credit alerts
• Department spending quota alerts.

Budget alerts

Budget alerts notify you when spending, based on usage or cost, reaches or exceeds the amount defined in the alert condition of the budget. Cost Management budgets are created using the Azure portal or the Azure Consumption API.

In the Azure portal, budgets are defined by cost. Budgets are defined by cost or by consumption usage when using the Azure Consumption API. Budget alerts support both cost-based and usage-based budgets. Budget alerts are generated automatically whenever the budget alert conditions are met. You can view all cost alerts in the Azure portal. Whenever an alert is generated, it appears in cost alerts. An alert email is also sent to the people in the alert recipients list of the budget.

Credit alerts

Credit alerts notify you when your Azure credit monetary commitments are consumed. Monetary commitments are for organizations with Enterprise Agreements (EAs). Credit alerts are generated automatically at 90% and at 100% of your Azure credit balance. Whenever an alert is generated, it’s reflected in cost alerts, and in the email sent to the account owners.

Department spending quota alerts

Department spending quota alerts notify you when department spending reaches a fixed threshold of the quota. Spending quotas are configured in the EA portal. Whenever a threshold is met, it generates an email to department owners, and appears in cost alerts. For example, 50 percent or 75 percent of the quota.

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The pricing calculator is a calculator that helps you understand potential Azure expenses. The pricing calculator is accessible from the internet and allows you to build out a configuration. The Total Cost of Ownership (TCO) calculator has been retired.

Pricing calculator

The pricing calculator is designed to give you an estimated cost for provisioning resources in Azure. You can get an estimate for individual resources, build out a solution, or use an example scenario to see an estimate of the Azure spend. The pricing calculator’s focus is on the cost of provisioned resources in Azure.

With the pricing calculator, you can estimate the cost of any provisioned resources, including compute, storage, and associated network costs. You can even account for different storage options like storage type, access tier, and redundancy.

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Azure shifts development costs from the capital expense (CapEx) of building out and maintaining infrastructure and facilities to an operational expense (OpEx) of renting infrastructure as you need it, whether it’s compute, storage, networking, and so on.

That OpEx cost can be impacted by many factors. Some of the impacting factors are:

• Resource type
• Consumption
• Maintenance
• Geography
• Subscription type
• Azure Marketplace

Resource type

A number of factors influence the cost of Azure resources. The type of resources, the settings for the resource, and the Azure region will all have an impact on how much a resource costs. When you provision an Azure resource, Azure creates metered instances for that resource. The meters track the resources’ usage and generate a usage record that is used to calculate your bill.

Examples

With a storage account, you specify a type such as blob, a performance tier, an access tier, redundancy settings, and a region. Creating the same storage account in different regions may show different costs and changing any of the settings may also impact the price.

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It’s important for software engineers to consider the impact of their software on users, and society in general; including considerations for its responsible use. When the application is imbued with artificial intelligence, these considerations are particularly important due to the nature of how AI systems work and inform decisions; often based on probabilistic models, which are in turn dependent on the data with which they were trained.

The human-like nature of AI solutions is a significant benefit in making applications user-friendly, but it can also lead users to place a great deal of trust in the application’s ability to make correct decisions. The potential for harm to individuals or groups through incorrect predictions or misuse of AI capabilities is a major concern, and software engineers building AI-enabled solutions should apply due consideration to mitigate risks and ensure fairness, reliability, and adequate protection from harm or discrimination.

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While you can perform many of the tasks needed to develop an AI solution directly in the Azure AI Foundry portal, developers also need to write, test, and deploy code.

Development tools and environments

There are many development tools and environments available, and developers should choose one that supports the languages, SDKs, and APIs they need to work with and with which they’re most comfortable. For example, a developer who focuses strongly on building applications for Windows using the .NET Framework might prefer to work in an integrated development environment (IDE) like Microsoft Visual Studio. Conversely, a web application developer who works with a wide range of open-source languages and libraries might prefer to use a code editor like Visual Studio Code (VS Code). Both of these products are suitable for developing AI applications on Azure.

The Azure AI Foundry for Visual Studio Code extension

When developing Azure AI Foundry based generative AI applications in Visual Studio Code, you can use the Azure AI Foundry for Visual Studio Code extension to simplify key tasks in the workflow, including:

• Creating a project.
• Selecting and deploying a model.
• Testing a model in the playground.
• Creating an agent.

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Azure AI Foundry is a platform for AI development on Microsoft Azure. While you can provision individual Azure AI services resources and build applications that consume them without it, the project organization, resource management, and AI development capabilities of Azure AI Foundry makes it the recommended way to build all but the most simple solutions.

Azure AI Foundry provides the Azure AI Foundry portal, a web-based visual interface for working with AI projects. It also provides the Azure AI Foundry SDK, which you can use to build AI solutions programmatically.

Azure AI Foundry projects

In Azure AI Foundry, you manage the resource connections, data, code, and other elements of the AI solution in projects. There are two kinds of project:

Foundry projects

Foundry projects are associated with an Azure AI Foundry resource in an Azure subscription. Foundry projects provide support for Azure AI Foundry models (including OpenAI models), Azure AI Foundry Agent Service, Azure AI services, and tools for evaluation and responsible AI development.

An Azure AI Foundry resource supports the most common AI development tasks to develop generative AI chat apps and agents. In most cases, using a Foundry project provides the right level of resource centralization and capabilities with a minimal amount of administrative resource management. You can use Azure AI Foundry portal to work in projects that are based in Azure AI Foundry resources, making it easy to add connected resources and manage model and agent deployments.

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Microsoft Azure provides a wide range of cloud services that you can use to develop, deploy, and manage an AI solution. The most obvious starting point for considering AI development on Azure is Azure AI services; a set of out-of-the-box prebuilt APIs and models that you can integrate into your applications. The following table lists some commonly used Azure AI services (for a full list of all available Azure AI services, see Available Azure AI services).

Considerations for Azure AI services resources

To use Azure AI services, you create one or more Azure AI resources in an Azure subscription and implement code in client applications to consume them. In some cases, AI services include web-based visual interfaces that you can use to configure and test your resources – for example to train a custom image classification model using the Custom Vision service you can use the visual interface to upload training images, manage training jobs, and deploy the resulting model.

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The term “Artificial Intelligence” (AI) covers a wide range of software capabilities that enable applications to exhibit human-like behavior. AI has been around for many years, and its definition has varied as the technology and use cases associated with it have evolved. In today’s technological landscape, AI solutions are built on machine learning models that encapsulate semantic relationships found in huge quantities of data; enabling applications to appear to interpret input in various formats, reason over the input data, and generate appropriate responses and predictions.Determining the specific AI capabilities you want to include in your application can help you identify the most appropriate AI services that you’ll need to provision, configure, and use in your solution.

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The growth in the use of artificial intelligence (AI) in general, and generative AI in particular means that developers are increasingly required to create comprehensive AI solutions. These solutions need to combine machine learning models, AI services, prompt engineering solutions, and custom code.

Microsoft Azure provides multiple services that you can use to create AI solutions. However, before embarking on an AI application development project, it’s useful to consider the available options for services, tools, and frameworks as well as some principles and practices that can help you succeed.

This module explores some of the key considerations for planning an AI development project, and introduces Azure AI Foundry; a comprehensive platform for AI development on Microsoft Azure.

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When comparing IT infrastructure models, there are two types of expenses to consider. Capital expenditure (CapEx) and operational expenditure (OpEx).

CapEx is typically a one-time, up-front expenditure to purchase or secure tangible resources. A new building, repaving the parking lot, building a datacenter, or buying a company vehicle are examples of CapEx.

In contrast, OpEx is spending money on services or products over time. Renting a convention center, leasing a company vehicle, or signing up for cloud services are all examples of OpEx.

Cloud computing falls under OpEx because cloud computing operates on a consumption-based model. With cloud computing, you don’t pay for the physical infrastructure, the electricity, the security, or anything else associated with maintaining a datacenter. Instead, you pay for the IT resources you use. If you don’t use any IT resources this month, you don’t pay for any IT resources.

This consumption-based model has many benefits, including:

• No upfront costs.
• No need to purchase and manage costly infrastructure that users might not use to its fullest potential.
• The ability to pay for more resources when they’re needed.
• The ability to stop paying for resources that are no longer needed.

With a traditional datacenter, you try to estimate the future resource needs. If you overestimate, you spend more on your datacenter than you need to and potentially waste money. If you underestimate, your datacenter will quickly reach capacity and your applications and services may suffer from decreased performance. Fixing an under-provisioned datacenter can take a long time. You may need to order, receive, and install more hardware. You’ll also need to add power, cooling, and networking for the extra hardware.

In a cloud-based model, you don’t have to worry about getting the resource needs just right. If you find that you need more virtual machines, you add more. If the demand drops and you don’t need as many virtual machines, you remove machines as needed. Either way, you’re only paying for the virtual machines that you use, not the “extra capacity” that the cloud provider has on hand.

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