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AWS vs. Azure: a Comprehensive Comparison of Services AWS vs. Azure: a Comprehensive Comparison of Services

AWS vs. Azure: a Comprehensive Comparison of Services

Updated Apr 4, 2024

13 mins read

As business across the globe underwent a dramatic shift towards remote work, the cloud emerged as the essential backbone, enabling seamless collaboration, data accessibility, and operational continuity. From streamlining communication and project management to facilitating scalable infrastructure and empowering data-driven decision-making, the cloud has emerged as an indispensable enabler of business continuity and growth.

Based on Statista research for the fourth quarter of 2022, the three dominant cloud service providers are AWS, holding a 32% market share, Microsoft Azure with 23%, and Google Cloud with 10%. Azure’s expansive global infrastructure spans across 60 regions worldwide, a figure surpassing that of any other cloud provider. With over 95% of Fortune 500 companies utilizing Azure, it stands as the preferred choice for enterprises seeking robust cloud solutions.

As per MarketsandMarkets, the worldwide cloud computing market is anticipated to expand from $626.4 billion in 2023 to $1,266.4 billion by 2028, exhibiting a compound annual growth rate (CAGR) of 15.1% throughout the forecast period.

In this article, our focus will be on comparing AWS and Microsoft Azure, considering that while Google Cloud possesses notable strengths like robust data analytics capabilities and integration with Google’s ecosystem, it hasn’t attained the same extensive adoption as its competitors.

AWS vs. Azure: Services 

Both AWS and Azure provide a comprehensive suite of cloud services and solutions to meet the diverse needs of companies of all sizes. We will delve into the key features and offerings of each platform, examining unique capabilities. Gaining a deeper grasp of AWS and Azure services will empower you to make informed decisions when selecting the best cloud provider for your specific requirements. 


Compute Services

EC2(Elastic Compute)

Azure Virtual Machine

Load balancing for distributed applications

AWS Auto Scaling

Azure Scale Sets

Serverless web applications

AWS Elastic Beanstalk
AWS Elastic Container Service
AWS Lambda

Azure App Service
Azure Container Apps
Azure Functions

Deploying applications

AWS Cloud Formation

Azure Resource Manager, Azure Automation

Storage Services

S3 (Simple Storage Service), Elastic File System, AWS Storage Gateway

Blob Storage, Azure File Storage, Azure Disk Storage, Azure StorSimple

Database Services


SQL Server, MySQL, PostgreSQL and MariaDB databases

Containerization and container orchestration support

Amazon Elastic Kubernetes Services

Azure Kubernetes Services

Logging and monitoring

CloudWatch and CloudWatch Logs

Monitor Logs and Monitor Alerts

Computing Power and Application Deployment Capabilities 

AWS leverages elastic cloud computing (EC2) to provide users with the flexibility to build their virtual machines (VMs) by selecting from pre-configured machine images (MIs) or customizing existing MIs. They have granular control over important VM parameters such as power, size, and memory, and can determine the number of VMs required for their workload. 

Conversely, Microsoft Azure presents a formidable array of compute services, spearheaded by Azure Virtual Machines. Azure Virtual Machines, akin to its AWS counterpart, provides an extensive spectrum of instance types, catering to disparate workload requisites.

Load balancing for distributed applications

Web requests are frequently distributed across numerous instances to ensure reliability and boost performance. AWS Auto Scaling actively monitors applications and autonomously adjusts capacity to maintain consistent and predictable performance while optimizing cloud costs. Similarly, Azure Virtual Machine Scale Sets offer comprehensive management capabilities for applications spread across numerous virtual machines (VMs), enabling automatic scaling of resources and efficient load balancing of traffic. 

Serverless web applications

Both cloud providers offer services that simplify the deployment and management of applications, allowing developers to focus on their code without worrying about underlying infrastructure: 

  • AWS Elastic Beanstalk manages the hosting infrastructure, coding language interpreter, operating system, security, HTTPS service, and application layer on developer’s behalf. This allows for quick deployment and reduces management complexity while providing flexibility and control. Azure App Service empowers developers to build and host web apps, mobile back ends, and RESTful APIs in their preferred programming language. It eliminates the need to manage infrastructure and offers auto-scaling and high availability.
  • Amazon ECS is designed to effortlessly deploy, manage, and scale containerized applications. It comes with preconfigured AWS settings, making it easy to focus on application development rather than environment setup. Azure Container Apps, on the other hand, is a fully managed Kubernetes-based application platform. It eliminates the need to manually orchestrate intricate infrastructure setups when deploying apps from code or containers. By providing a centralized networking, dynamic scaling, and configuration environment, it enables developers to build diverse modern applications or microservices. 
  • AWS Lambda and Azure Functions are serverless computing services that simplify code deployment and execution without server management. With AWS Lambda, developers can organize their code into separate Lambda functions, and Lambda executes them as needed. It automatically scales to handle any number of requests, from thousands per second to just a few per day, based on demand. Azure Functions, similarly, offers pre-built blocks of code called “functions” that can be use to implement system logic. Whenever an important event occurs, developer can invoke one or more of these serverless functions, enabling developers to respond quickly without the need to manage servers or scale resources.


AWS CloudFormation and Azure Resource Manager (ARM) are Infrastructure-as-a-code services that simplify the management and deployment of resources in their respective cloud platforms. CloudFormation helps to model and configure AWS resources by creating a template that describes the desired resources. 

Similarly, Azure Resource Manager enables you to create, update, and delete resources in your Azure account. It provides a management layer with features like access control, locks, and tags for securing and organizing resources. Additionally, Azure Automation offers cloud-based automation, operating system updates, and configuration management for consistent management across Azure and non-Azure environments.

While there may be some nuances and distinctions between the two platforms, both AWS and Azure offer similar functionality and features when it comes to deploying applications.

Storage Services

A successful cloud deployment heavily relies on adequate storage capacity to effectively handle data and workloads. Fortunately, Azure and AWS excel in this domain, offering robust storage solutions. 

AWS excels in object storage with its highly scalable and durable Amazon S3. With different storage classes available, businesses can optimize cost and performance based on their data access patterns. AWS also provides Amazon EFS for reliable file storage and Amazon EBS for block storage, ensuring flexibility for diverse workloads.

Azure, on the other hand, offers Azure Blob Storage, a robust object storage solution with versatile tiers to meet various storage needs. Azure Files provides managed file shares, supporting both SMB and NFS protocols, while Azure Disk Storage offers reliable block storage for virtual machines.

AWS and Azure offer hybrid storage solutions, allowing businesses to integrate their on-premises infrastructure with the cloud. AWS Storage Gateway enables seamless integration of on-premises environments with AWS storage services, and Azure FileSync combines on-premises storage appliances with Azure cloud storage.

Both platforms offer unlimited allowed objects, but there are differences in the object size limit. AWS imposes a maximum object size limit of 5 TB, while Azure has a size limit of 4.75 TB.

Database Services

AWS and Azure have a comprehensive range of database services catering to structured and unstructured data, including Big Data. In terms of data management durability, AWS users benefit from Amazon relational database service (RDS), while Azure provides the Azure SQL Server database.

AWS’s RDS supports six database engines, namely MariaDB, Amazon Aurora, MySQL, Microsoft SQL, PostgreSQL, and Oracle. Azure offers reliable and efficient support for SQL Server, MySQL, PostgreSQL and MariaDB databases as well. 

While AWS provides a wider range of instance types to choose from, Azure distinguishes itself with its user-friendly tooling and intuitive interface, simplifying various database operations.

Containerization and container orchestration support

Amazon Elastic Kubernetes Service (EKS) and Azure Kubernetes Service (AKS) are the most popular managed Kubernetes services. These services provide a simplified and convenient way to leverage the power of Kubernetes without having to handle the complexities of manual installation, configuration, and management of the underlying infrastructure.

Amazon EKS simplifies the process of building, securing, and maintaining Kubernetes clusters. Amazon EKS integrates with core AWS services for easy monitoring, scaling, and load balancing of containerized applications. It also supports AWS App Mesh for enhanced observability, traffic controls, and security features. Amazon EKS provides a scalable and highly-available control plane for Kubernetes workloads. Developers can opt for EC2 instances or AWS Fargate to power containers when using Amazon EKS, similar to Amazon ECS.

Azure Kubernetes Service (AKS) simplifies managed Kubernetes deployment in Azure, handling critical tasks like health monitoring and maintenance. AKS automatically creates and configures the control plane, provided as a managed Azure resource at no extra cost. Developers only need to manage the nodes attached to the AKS cluster. During deployment, developers can specify node numbers, sizes, and configure advanced features such as networking, Azure Active Directory integration, and monitoring.

Logging and monitoring

Amazon CloudWatch simplifies infrastructure and application maintenance by collecting and displaying real-time logs, metrics, and event data in automated dashboards. Amazon CloudWatch Logs provides developers with the capability to monitor, store, and access log files from various sources such as Amazon EC2 instances, AWS CloudTrail, and Route 53. With CloudWatch Logs, developers can easily view, search, filter, and archive logs for analysis. It offers a powerful query language for log querying, supports data auditing and masking, and enables metric generation from logs. Additionally, the CloudWatch Alarms feature allows monitoring metrics and receiving notifications based on configured thresholds. 

Similarly, Azure Monitor consolidates and centralizes data from all layers and components of the system into a unified data platform. Azure Monitor Logs, collects and organizes log and performance data from monitored resources. With a powerful query language, developers can analyze Logs data quickly, whether they need to retrieve specific records or perform in-depth data analysis to identify crucial patterns in the monitoring data. Azure Monitor Alerts notify developers of potential issues with the infrastructure or application by detecting and addressing problems before users notice them. It uses Azure Monitor data to keep developers informed and take timely actions.

Top Cloud Service Providers: AWS, Azure, and GCP comparison by Contries

Introducing a comparative analysis of the leading cloud service providers across various countries, this table sheds light on the market landscape dominated by AWS, Azure, and GCP. From the United States to Brazil, we explore the market shares and distribution of these key players, providing valuable insights into their respective positions within each region.


United States




United Kingdom




























AWS Cost Management Tips

Right-size your EC2 instances. Optimizing your AWS costs involves selecting the appropriate size of EC2 instances for your application’s specific use case. With a range of types and sizes available, it’s crucial to align your instances with your application’s primary function or workload. By understanding whether your application requires CPU- or memory-intensive resources, you can focus on selecting the most suitable instance types to achieve optimal performance and cost efficiency. Common EC2 instance types:


C instances(compute-optimized)

They are ideal for web servers and video encoding.

M instances (general-purpose)

They offer a balance of compute, memory, and networking resources for applications like data processing and small databases.

R instances

They are recommended for memory-intensive applications such as high-performance databases and data mining.

Use Amazon EC2 Spot Instances. To reduce EC2 costs in AWS, consider utilizing Amazon EC2 Spot Instances if your workload is fault-tolerant. Spot Instances can lead to cost savings of up to 90% and are particularly suitable for workloads such as big data processing, containerized applications, CI/CD pipelines, web servers, and development/testing environments. It is important to note that Spot Instances can be interrupted with a two-minute notice. However, to ensure uninterrupted operation for critical components, Amazon offers the Spot Fleet feature, which allows you to run both on-demand and spot instances within the same Auto Scaling group.

Use specific Amazon S3 storage tiers. When initially using Amazon S3, the Standard storage tier is typically the default choice and suitable for most scenarios. However, if you have files that are rarely accessed for more than 30 days, you can take advantage of other S3 tiers to reduce costs. By utilizing S3 analytics, you can analyze the frequency of file access and receive recommendations on using the S3 Infrequently Accessed (S3 IA) tier, which offers lower-cost storage. You can further automate the process by implementing Lifecycle Policies to automatically move these files to the appropriate storage tier.


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Fundamentals of Efficient Cloud Cost Optimization

Azure Cost Management Tips

Rightsize Azure VM. Similarly to AWS, selecting the appropriate Azure VM types for your workload is essential for optimizing operational costs:


General Purpose Azure VMs (A, B, and D series)

For testing, development environments, small databases, and servers with low-to-moderate traffic

Compute Optimized Azure VMs (F-Series)

Ideal for web servers, batch processes, and mid-size business apps

High Performance Compute Azure VMs (H-Series)

Offer maximum compute power for demanding workloads like financial analysis

Memory Optimized Azure VMs (E, M, Mv, and D series)

Perfect for memory-intensive tasks such as relational databases and in-memory analytics

Storage Optimized Azure VMs (Ls series)

Designed for big data processing and running data warehouses

GPU Optimized VMs (N-series)

Deliver powerful performance for graphics-intensive workloads like video editing, rendering, modeling, and deep learning

Switch to Azure Spot Virtual Machines. Azure Spot VMs offer substantial cost savings, with discounts of up to 90% off pay-as-you-go rates for Azure’s spare compute capacity, similar to AWS Spot Instances. While utilizing Spot VMs, there is a possibility that Azure may require the capacity back due to various reasons. In such cases, you will receive a 30-second notice to transition from Spot VMs to pay-as-you-go instances. However, Azure Spot VMs remain an excellent choice for various temporary tasks, including batch jobs, rendering, development/testing, and advanced analytics

Utilize Azure Elastic Databases. By utilizing Azure SQL Database Elastic Pools effectively, you can achieve significant performance improvements and cost savings. Rather than allocating individual resources to each database to accommodate varying usage demands, Elastic Pools allocate resources collectively to the databases within the pool. This approach optimizes resource utilization and provides scalability across multiple databases.


The decision between AWS and Azure hinges upon the distinct needs and priorities of each company. Both platforms offer robust cloud solutions, and organizations can gain advantages by harnessing the strengths of each provider. As cloud technology continually evolves, it is essential for businesses to stay well-informed and regularly reassess their cloud strategies, maximizing the resources and capabilities offered by AWS and Azure.

In situations involving simple applications, the choice between platforms may not yield significant differences in performance or costs. However, when applications involve extensive data storage in Data Warehouse solutions, Big Data analysis, or the utilization of AI capabilities to enhance functionality, the decision becomes more complex. AWS and Azure exhibit substantial divergence in terms of available services for these specific use cases. There is no definitive formula for determining which provider is better suited for your specific use case. Seeking guidance from professional cloud development companies, like Leobit, can greatly facilitate the selection process, ensuring the right platform is chosen and a seamless transition to the cloud is achieved.

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Artem Matsa | Business Development Director