Overview

Microsoft Azure is a comprehensive suite of cloud computing services that allows organizations to build, deploy, and manage applications and services through Microsoft's global network of data centers. Launched in 2010, Azure has evolved to offer a broad spectrum of services, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) capabilities as detailed in the Azure documentation. It provides computing power, storage, databases, networking, analytics, artificial intelligence (AI), Internet of Things (IoT), and developer tools.

Azure is frequently selected by enterprises for its deep integration with existing Microsoft technologies, such as Windows Server, SQL Server, Active Directory, and Visual Studio. This makes it a suitable choice for organizations with significant investments in the Microsoft ecosystem looking to extend their on-premises infrastructure to the cloud or implement hybrid cloud strategies. Its capabilities support a range of workloads, from hosting web applications and virtual machines to complex data analytics and machine learning applications.

The platform is designed to support diverse technical requirements, offering various operating systems, programming languages, frameworks, and databases. Developers can utilize SDKs for Python, JavaScript, Java, .NET, Go, C++, and Ruby to interact with Azure services as outlined in the Azure developer guides. Azure's global infrastructure, comprising numerous regions and availability zones, is engineered for high availability, disaster recovery, and data residency requirements, serving organizations with global reach or specific regulatory needs. The platform's compliance certifications, including SOC, ISO, GDPR, HIPAA, and PCI DSS Level 1, address stringent enterprise security and regulatory demands, making it a viable option for regulated industries according to Azure's compliance overview.

Key features

  • Virtual Machines (VMs): Provision Windows or Linux virtual machines in minutes, offering scalable compute capacity for various workloads.
  • Azure Kubernetes Service (AKS): Managed Kubernetes offering that simplifies deploying, managing, and scaling containerized applications.
  • Azure SQL Database: Managed relational database service based on the Microsoft SQL Server engine, providing scalability and high availability.
  • Azure Storage: Scalable cloud storage for data objects, file shares, block storage, and message queues.
  • Azure Functions: Serverless compute service that enables running event-driven code without provisioning or managing infrastructure.
  • Azure AI Platform: A suite of services for building, deploying, and managing AI and machine learning models, including cognitive services for vision, speech, and language.
  • Azure App Service: Fully managed platform for building, deploying, and scaling web apps, mobile backends, and API apps.
  • Hybrid Cloud Capabilities: Tools like Azure Arc and Azure Stack extend Azure services and management to on-premises environments, multi-cloud, and edge locations.
  • Developer Tooling Integration: Seamless integration with Visual Studio, Visual Studio Code, GitHub, and Azure DevOps for streamlined development workflows.

Pricing

Azure's pricing model is primarily pay-as-you-go, meaning users are charged only for the services consumed. This model offers flexibility, with costs varying based on service type, usage, and data transfer. Discounts are available through options like Reserved Instances, which provide significant savings for committing to a fixed term (1 or 3 years) for compute resources, and Azure Savings Plans for compute, which offer lower prices on eligible compute services in exchange for an hourly spend commitment as described on the Azure pricing page. A free account is available, offering 12 months of free services and a $200 credit for 30 days. Specific pricing for individual services can be estimated using the Azure pricing calculator.

Pricing Model Description Notes (as of 2026-05-07)
Pay-as-you-go Charges based on actual resource consumption (compute, storage, data egress, etc.). No upfront commitment; ideal for variable workloads.
Reserved Instances Commit to 1-year or 3-year terms for Virtual Machines and other services. Provides significant discounts (up to 72% off pay-as-you-go rates for VMs).
Azure Savings Plan for Compute Commit to an hourly spend for 1-year or 3-year terms across eligible compute services. Automatically applies discounts to eligible compute usage globally.
Free Account Access to select services free for 12 months and a $200 credit for 30 days. For new users to explore Azure services.

Common integrations

Alternatives

  • Amazon Web Services (AWS): A leading cloud provider offering a vast array of services for compute, storage, databases, analytics, and machine learning, with a strong focus on developer tools and global reach.
  • Google Cloud Platform (GCP): Known for its strengths in data analytics, machine learning (e.g., TensorFlow), and containerization (Kubernetes), leveraging Google's global infrastructure.
  • Oracle Cloud Infrastructure (OCI): Provides a range of IaaS, PaaS, and SaaS services, often chosen by enterprises for its strong database capabilities and competitive pricing for specific workloads.

Getting started

To get started with Azure using Python, you can provision a virtual machine and deploy a simple web application. The following example demonstrates how to create a resource group and a Linux virtual machine using the Azure CLI. This requires the Azure CLI to be installed and configured with your Azure account credentials as described in the Azure CLI installation guide.

# Log in to Azure (if not already logged in)
az login

# Create a resource group
RESOURCE_GROUP_NAME="myPythonWebAppResourceGroup"
LOCATION="eastus"
az group create --name $RESOURCE_GROUP_NAME --location $LOCATION

# Create a Linux Virtual Machine (Ubuntu LTS) with SSH access
VM_NAME="myPythonWebAppVM"
VM_USERNAME="azureuser"

az vm create \
    --resource-group $RESOURCE_GROUP_NAME \
    --name $VM_NAME \
    --image UbuntuLTS \
    --admin-username $VM_USERNAME \
    --generate-ssh-keys \
    --public-ip-sku Standard

# Get the public IP address of the VM
PUBLIC_IP=$(az vm show -d --resource-group $RESOURCE_GROUP_NAME --name $VM_NAME --query publicIps -o tsv)

echo "VM created with public IP: $PUBLIC_IP"
echo "You can now SSH into your VM using: ssh $VM_USERNAME@$PUBLIC_IP"

After creating the VM, you would typically SSH into it to install Python, a web server (like Nginx or Apache), and deploy your Python web application.