AWS vs. Azure: When Each Cloud Platform Works Better
AWS vs. Azure Cloud Platform [MB]

Comparison of AWS vs Azure: When Each Cloud Platform Works Best

Cloud platforms have become central to business operations, with 89% of businesses relying on multiple clouds. Amazon Web Services (AWS) and Microsoft Azure are the two cloud service providers (CSPs) dominating the market.

AWS holds 31% of the market and Microsoft Azure — 24%, according to Q4 2023 data from Synergy Research Group. Both have shown double-digit growth through 2023, 12% and 29%, respectively. 

Fundamentally, AWS cloud platform and Azure cloud platform have very similar capabilities in terms of on-demand computing, cloud data storage, networking, and pricing. Both offer flexible auto-scaling, self-service resource provisioning, a pay-per-use pricing model, robust security information and event management (SIEM) solutions, and big data analytics tools, among 100+ other capabilities.   

However, the devil is in the details. AWS and Azure have slight asymmetry in underlying technologies and capabilities selection. Azure offers access to a wider range of integrated frameworks, SDKs, and APIs for machine learning and AI development. AWS has fewer tools available out of the box but boasts simpler integrations with open-source technologies.  

In this post, we compare the key capabilities of each cloud platform and outline cases when Azure Cloud is a better solution than AWS (and vice versa).  

Comparison of AWS vs Azure: Capabilities Overview 



Launch year



Market share

31% (As of Q4 2023)

24% (As of Q4 2023)

Availability zones

106 in all global regions

113 in all global regions

Computing Power

Elastic Cloud Computing (EC2)

Virtual Machines (VMs) from Virtual Hard Disks (VHD)

Cloud storage

Amazon Simple Storage Service (S3)

Elastic Book Storage (EBS)

Amazon S3 Glacier

Azure Blob Storage

Azure Files

Azure Elastic SAN

Azure Disk Archive

Azure Data Lake


Relational databases: Amazon Aurora, Amazon RDS, Amazon RDS for Db2, and Amazon RDS on VMware 

Non-relational databases: Amazon DynamoDB, Amazon MemoryDB for Redis, Amazon Neptune, Amazon Keyspaces, Amazon Timestream

Relational databases: Azure SQL Database, Azure Database for MySQL, Azure Database for PostgreSQL 

Non-relational databases: Azure Cosmos DB, Azure Database for MariaDB, Azure Cache for Redis

Big data analytics tools

Amazon Athena: SQL-based querying service

Amazon Elastic MapReduce(EMR): A managed Hadoop framework

Amazon Elasticsearch Service for managing Elasticsearch clusters

Amazon Kinesis: Real-time data analytics

AWS Glue: Serverless data integration service

Azure Synapse Analytics: Integrated analytics service that combines big data and data warehousing

Azure HDInsight: A cloud-based service for deploying popular open-source frameworks

Azure Stream Analytics: A real-time data streaming service

Azure Data Factory: Data integration service

Machine Learning & AI services

Amazon SageMaker: Managed service for building, training, and deploying ML/DL models

Amazon Polly: A text-to-speech service

Amazon Rekognition: A service for adding image and video analysis features to applications

Amazon Textract: Optical character recognition service

Azure Machine Learning: An ecosystem of tools and services for building, training, and deploying ML/DL models

Azure Cognitive Services: Provides access to APIs for building intelligent applications

Azure OpenAI Service: Access to OpenAI generative AI models

Azure Bot Service: Chatbot and virtual assistant development platform

Azure AI Speech: SDK for creating voice-enabled apps

Azure AI Translator: Real-time translation across 100 languages

Azure AI Vision: OCR and computer vision service

Security & IAM Services

AWS Identity and Access Management service

Amazon GuardDuty: Intelligent threat detection

AWS Security Hub for security data aggregation and monitoring

AWS Shield: DDoS protection

Amazon Macie: Data discovery service

AWS WAF (Web Application Firewall) for filtering web traffic

AWS CloudHSM: A cloud-based hardware security module that facilitates encryption key generation and usage

Azure Security Center: A unified infrastructure security management system

Microsoft Entra (former Azure AD): IAM service

Microsoft Sentinel: Security analytics and threat intelligence tool

Microsoft Defender for Cloud: Cloud workload protection solution

Azure DDoS Protection service

Azure Web Application Firewall: Provides centralized protection of web applications through traffic filtering

Azure Key Vault: A secure secrets store for tokens, passwords, certificates, and API keys

Azure Policy: Helps enforce organizational standards and assess compliance at scale

Microsoft Purview: Data discovery and governance service

How to Choose Between AWS and Azure 

The decision between Azure Cloud vs AWS Cloud depends on your business and technology requirements. Consider these factors to make a weighted choice: 

  • Team competency and skill sets. Your IT staff may have a penchant for either of the cloud platforms, depending on their knowledge of the ecosystem or past experiences. Although it’s possible to re-train cloud engineers to work with another CSP, the learning curve may be steep and you may face some cultural resistance. Alternatively, you can hire a technology partner familiar with AWS or Microsoft Azure to deliver the project.
  • Current infrastructure. If you’re scaling an existing system or want to build integrations with a solution on AWS or Azure, it rarely makes sense to change CSPs (unless there are points of friction). 
  • Technical requirements. By design, Azure is a better option for businesses looking for a seamless Windows integration or a platform-as-a-Service (PaaS) provider, offering out-of-the-box access to certain capabilities. On the other hand, AWS is a better choice if you prefer an infrastructure-as-a-service (IaaS) solution or want to integrate more open-source technologies, for example. 
  • Estimation based on economic and technical efficiency. Compare the licensing costs for purchasing the same stack of services from either provider. AWS and Azure have on-site calculators for comparing usage costs. If you already have a Microsoft 365 subscription, you may benefit from discounts on Power Platform licenses, for example.

When Azure Cloud Is a Better Choice  

Azure is a natural choice for organizations already using Microsoft or SAP products. Azure offers license discounts for users of Windows, Office 365, and Dynamics 365 among other services. Compared to AWS, Azure includes a wider range of managed services and prefab capabilities that teams can immediately access.  

Comprehensive Set of Microsoft Azure Services

SAP Cloud Migration  

Microsoft and SAP have a strategic partnership, aimed at building a greater technological synergy. SAP on Azure migration or deployment is a rather straightforward process, thanks to the available integrations and migration tools. By moving SAP to Azure cloud, businesses can save up to 60% on storage costs and from 40% to 75% in TCO.  

Azure enables seamless data backups of the SAP HANA database to ensure effective disaster recovery and business continuity. SAP customers can now use Microsoft Entra ID service to implement single sign-on (SSO) using Microsoft credentials and streamline user identity management. SAP SuccessFactors Solutions also has direct integration now with Microsoft 365 Copilot and Copilot in Viva Learning, allowing users to benefit from Gen AI services.   

Managed NoSQL Databases  

If you want to access high-performance, scalable databases, Cosmos DB is the leading option on the market. A multi-master, NoSQL, managed database service, Cosmos DB is used by companies like OpenAI to create high-traffic web applications and ensure low-latency data processing in real-time.  

With streamlined management, multi-region data distribution, automatic scaling, updates, and patching, Azure Cosmos DB removes the burden of database administration. Users also get access to open-source APIs for big data analytics and seamless integration with Azure AI Services to support Retrieval Augmented Generation (RAG).  

Some of the most common use cases of Cosmos DB include IoT and telematics data processing, event-based ecommerce analytics, real-time content personalization, and more.  

Big Data Analytics Tools  

Azure Synapse Analytics combines enterprise-grade SQL data warehousing and integrated big data analytics tools. It gives access to proprietary (e.g., Azure Data Factory, Azure Machine Learning, Power BI) and open-source technologies like Apache Spark.  

If your company already uses Azure for data warehousing, going with Azure Synapse Analytics makes perfect sense due to pre-made integrations. Moreover, Azure Synapse directly integrates with Cosmos DB, enabling real-time business intelligence and advanced analytics scenarios.  

Low-Code Tools 

Microsoft is an undeniable leader in low-code technology. Power Platform combines visual app development and workflow automation tools with more advanced capabilities for quickly building back-end integrations to support a wide range of business use cases.  

Users can also integrate various Azure services into Power Apps — Azure functions for custom logic development to Azure Cognitive Services — to build truly innovative workplace products for 2-4X less the cost and time.  

Get Familiar with Power Platform

When AWS Works Better  

AWS appeals more to companies looking for an Infrastructure-as-a-Service (IaaS) solution — such that streamlines operational tasks without restricting the choice of technologies. Compared to Azure, AWS provides fewer pre-integrated tools and guardrails, but it does not restrict you as much in using open-source components. In some cases, AWS also has better native integrations with certain technologies. 

Linux-Based App Architectures  

AWS offers a secure, stable, and high-performance Linux operating system called Amazon Linux 2. It supports the latest EC2 instance capabilities, includes security updates, and helps configure the optimal performance of Linux-based applications. AWS also offers access to a wider range of open-source tools for developing Linux apps.  

SQL Relational Databases  

AWS provides stellar managed services, Amazon RDS and Amazon Aurora, for running MySQL and PostgreSQL databases in the cloud. Both simplify database management, improve scalability, and automate disaster recovery. AWS RDS enables users to scale their databases based on demand: increase compute and memory capacity, assign more storage or set up read replicas for improved performance.  

The key difference between AWS RDS and an equivalent relational database service from Azure (Azure SQL) is deployment. Azure SQL databases do not rely on specific virtual machines. Instead, users create an abstract container that provides a platform for database management and auto-scaling using Azure’s multi-tenant architecture.  

AWS RDS, in turn, lets you allocate E2C instances to databases and provision storage capacity separately. Since RDS charges storage separate from computing, its factor costs differ from Azure SQL, and can be lower if you apply cloud cost optimization techniques. This also allows running MySQL applications without modifying them.   

Flexible Cloud Storage  

Both Azure and AWS provide virtually limitless cloud storage, available on-demand. AWS S3, however, has greater flexibility and feature granularity. Because AWS offers an object-oriented storage setup you can assign different performance and price points to each object, including storage standard, auto-tiering, infrequent access, and Glacier for data archival. Azure only allows adding equivalent controls on an account level, meaning you have to set an entire Azure storage account as cold rather than individual blobs (objects) within it.  

How to Embrace More Value from AWS Services?

How Generative AI Adoption Impacts the Choice: Azure vs AWS 

Although AWS still has the largest market share, Microsoft is picking up new business faster as companies want to use Azure OpenAI to launch private Gen AI models. About 3% of Azure’s growth in Q4 was tied to AI. 

Thanks to an exclusive partnership (and an investment stake) in OpenAI, Azure users can access most of the foundational models as APIs. Effectively, companies can launch private Gen AI agents like ChatGPT on Azure cloud and fine-tune the models to use corporate data for result generation without risking data disclosures. Microsoft also contributes to Semantic Kernel aims to bring prompt engineering and LLM orchestration tools to C# and Python developers.  

Microsoft also provides one of the best vector databases, Azure Cosmos DB, and recently added semantic search support to Azure Cache for Redis Enterprise. Both make it easier to provision data for AI model training.  

Amazon entered the Gen AI race a bit later but aims to catch-up quickly. The three Gen AI services available include Amazon Bedrock, Amazon Titan, and Amazon Sagemaker JumpStart.  

SageMaker JumpStart offers an integrated developer environment for building and deploying ML models. In addition to the popular MLOps tools, the IDE lets users easily integrate and fine-tune open-source models from Hugging Face. Amazon Titan and Amazon Bedrock offer access to partner-supplied and proprietary gen AI models for text and image generation, semantic search, and retrieval augmented generation.  


AWS and Azure are two undeniable leaders in the cloud computing space. Each has its own stronger sides when it comes to specific tools (e.g., for big data analytics), system capabilities (e.g., more granular storage controls), and integrations with other popular business systems. Infopulse has extensive knowledge of both ecosystems and would be delighted to advise on the best option, based on your business requirements and technology stack.

Already decided on the cloud vendor?

Ask Infopulse to help you get the most from AWS or Azure cloud capabilities, contact our cloud engineering team. 

Get in touch!

About the Author

Vadym Popov has more than 20 years of experience in the field, as proven by numerous Microsoft certifications, including Azure Security Engineer, Implementing an Azure Data Solution, Microsoft 365 Identity, etc. At Infopulse, Vadym works closely with our clients, leading complex cloud migration and implementation projects on Azure and AWS cloud platforms.
Vadym Popov

Vadym Popov

Enterprise Cloud Architect

About the Author

Oleg has more than 20 years of IT experience and is a skillful engineer of Linux- and Unix-based solutions. For the last 4 years, Oleg has been involved in cloud solutions engineering projects, mainly using Azure and AWS services, in such industries as food and beverage, transportation, and electric power. Apart from engineering, Oleg has valuable IT support and DevOps expertise to provide clients with end-to-end, cost-effective solutions.
Oleg Drogayev

Oleg Drogayev

Cloud & DevOps Engineer

Next Article

We have a solution to your needs. Just send us a message, and our experts will follow up with you asap.

Please specify your request

Thank you!

We have received your request and will contact you back soon.