21st Oct 2022

AWS vs. Azure – A brief guide on main differences

Enterprise spending on cloud infrastructure has grown tremendously in recent years, surpassing the $50 billion mark in Q4 last year. Gartner predicts worldwide end-user spending on public cloud is expected to reach nearly USD 600 billion in 2023. 

This brief guide will shine a light on the competition and services between the two market leaders in cloud services. The market is dominated by AWS (the largest single player) with a 33% market share. Microsoft Azure has only held a 21% share until Q4 2021 (up 9%). Whereas, Google Cloud Platform only holds 10% of the market.  

                                                                                       Source: Synergy Research Group 

This brief guide will shine a light on the competition and services between the two market leaders in cloud services. The market is dominated by AWS (the largest single player) with a 33% market share. Microsoft Azure has only held a 21% share until Q4 2021 (up 9%). Whereas, Google Cloud Platform only holds 10% of the market.  

Azure Cloud Services 

Azure Cloud Services aids in the management of applications through Microsoft-operated data centers. The platform provides seamless services like analytics, computing, networking, and storage. Its extensive toolkit is constantly growing and competing in the market with a global footprint. 

Amazon Web Services (AWS) 

AWS is a subsidiary of the eCommerce and technology behemoth Amazon. It has 200+ services from its globally established data centers. The pricing model and its optimized costs enable users across start-ups, large enterprises, and leading global agencies to adopt AWS. 

 Where the difference lies? 

Gartner published a document that stated, “Public cloud services, such as Amazon Web Services (AWS), Microsoft Azure, and IBM Cloud, are innovation juggernauts that offer highly operating-cost-competitive alternatives to traditional, on-premises hosting environments.” 

  AWS  Azure 
Data warehouse  Amazon Redshift  Azure Synapse Analytics 
Availability  25+ regions worldwide  60+ regions worldwide 
Computing  Elastic computing (EC2)  Virtual machine 
Services  200+  200+ 
PaaS  Elastic beanstalk  App services 
Security  AWS Identity & Access Management  Azure Active Directory 
Serverless  AWS Lambda  Azure function 
Containers  AWS Elastic Containers  Azure Kubernetes Services (AKS) 
Rational database  AWS RDS  Azure SQL 

While both provide similar services, there is a certain differentiation factor as well. These can be segregated in terms of computing power, pricing, provisioning, computing, storage, features, and adaptability. 

1. Computing power, provisioning, and usage 

Scalability is one of the core elements of cloud computing. For Azure, users can create VMs from Virtual Hard Disks (VHD). It enables load balancing and uses virtual scale sets to ensure scalability. On the other hand, AWS uses EC2 instances in which the resource footprint may increase or shrink on demand. Users may constrict their virtual machines (VMs), which are pre-configured and users can modify them in terms of power, size, and memory. 

Service  AWS  Azure 
Computing power  Amazon Ec2 Instance  Azure Virtual machines 
VMware Cloud of AWS  Azure VMware Solution 
AWS Parallel Cluster  Azure Cycle Cloud 

In both virtual machines and servers, you can manage the OS. In both, users pay according to demand. The key differentiation is that EC2 may be customizable for various uses, whereas Azure Virtual Machines (VMs) can merge with other cloud deployment applications, even from third-party tools. 

2. Storage 

AWS and Azure both offer tier-based object storage. particularly directed at protecting stored data. AWS has S3 and Azure offers Azure Cool Blog Storage, both designed for infrequently accessed workloads. In terms of storage, both AWS S3-IA and Azure CBS are best for clod-tier or infrequently-accessed data loads where performance and latency are the keys. 

Both offer reliable storage services ideal for long-term usage, backup, and DR. However, there are certain differences in regional availability, pricing, and security features. 

3. Pricing – Comparing apples to apples 

The objective of pricing comparison isn’t necessarily to make a decision between service providers but to identify where costs can be saved by using the same services from different providers. Truth be told, AWS has different pricing models, but they’re complex enough to give bill-related surprises. Dedicated tools like AWS Cost Explore, AWS Calculator, and trusted advisors are developed for cost optimization. whereas Azure’s pricing strategy is easier to understand. 

Instances selected for Comparison  

(Region & Pricing Model): Middle east and Pay-as-you-go/on-demand model 

Azure provides a billing dashboard with a clear bill on spending and fewer hidden charges generated from instances like AWS zombie instances. Here is an example of cloud Pricing based on-demand rates: 

Cloud provider  Instance type  vCPU  RAM (GB) 
AWS (General purpose)  T3.Xlarge  4  16 
AWS (Compute-optimized)  c6i.xlarge  4  8 
Azure (General purpose)  B4ms  4  16 
Azure (Compute Optimized)  F4sV2  4  8 

Source: https://aws.amazon.com/ec2/pricing/on-demand/ 

Source: https://azure.microsoft.com/en-us/pricing/details/virtual-machines/windows/ 

Prices are as off 3/8/2022 

General Purpose 

Cloud provider  Instance type  On-demand (Hourly Rate) 
AWS  T3.Xlarge  $0.2742 
Azure  B4ms  $0.2660 

Source: https://aws.amazon.com/ec2/pricing/on-demand/ 

Source: https://azure.microsoft.com/en-us/pricing/details/virtual-machines/windows/ 

Prices are as off 3/8/2022 

Compute Optimized  

Cloud provider  Instance type  Price 
AWS  C6i.Xlarge  $0.3952 
Azure  F4sV2  $0.4360 

Source: https://aws.amazon.com/ec2/pricing/on-demand/ 

Source: https://azure.microsoft.com/en-us/pricing/details/virtual-machines/windows/ 

Prices are as off 3/8/2022 

4. Database 

The next most critical decision is which database, either SQL or Non-SQL. Here is a quick comparison table-top guide for you: 

Database Service  Descriptions  AWS  Azure 
Rational database  SQL delivers high performance, and reliability for numerous data-driven applications.  RDS  SQL Database 

(Postgres and MySQL) 

Non-SQL (Document storage)  Globally distributed database that supports multiple data models, key values, and documents  Dynamo DB 


Cosmos DB 
Non-SQL – Key/Value storage  Non-rational for semi-structured data  DynamoDB & Simple DB  Table Storage 
Caching  In-memory-based service that provides- high performance, and is typically used for non-transactional work  ElastiCache  Redis Cache 
Database migration support  Migration of database  Database Migration Service (Preview)  SQL Database Migration Wizard 

5. Analytics & Big Data processing  

Now every touch point in your business generates data. These huge data volumes cannot be simply whisked away, so valuable data insights can be unearthed with the help of AWS and Azure services. 

Both AWS and Azure provide the broadest selection of analytics services that are designed to enable organizations of all sizes to work with and reinvent their data. From data storage to big data analytics, streaming analytics, BI, and support for ML, both offer a broad range of services. 

Data Processing Service  Descriptions  AWS  Azure 
Bigdata processing  Provide technologies and tools to ingest large datasets into multiple jobs  Elastic MapReduce (EMR)  HD Insights 
Data orchestration  Move and process data within different services (usually on-prem)  Data Pipeline 


Azure Data Factory 
Cloud-based ETL data processing services that orchestrate and automate the movement of data  AWS Glue Data Catalog  Azure Data Factory + Data Catalog 
Analytics  Platforms that bring insights from the massive amount of data from multiple sources  Kinesis Analytics 
  • Steam Analytics 
  • Data Lake Analytics 
  • Data lake Store 
Steaming data 
  • Kinesis Streams 
  • Kinesis Firehouse 
  • Event Hubs 
  • Event Hubs Capture 
Data discovery 
  • AWS Data Exchange 
  • Amazon Athena 
  • Data catalog 
  • Azure SQL Data Discovery 

 6. Data Warehouse 

A data warehouse is a central repository for information that is extracted from multiple internal and external sources for business intelligence. The typical functionalities support data analysis, reporting, and business intelligence. The cost and technical capabilities depend on the vendors, but most data warehouses share the same traits. Compared to legacy systems, modern cloud-based data warehouse systems are agile enough to support today’s data and business intelligence requirements. Major vendors have multiple on-prem and cloud-based solutions. 

Amazon and Microsoft are two of the main players in cloud computing, followed by Google and others. Amazon Redshift is a cloud-based data management system, which allows petabytes of semi-structured and structured data in little time. 

Being a limitless information analysis service, Azure Synapse Analytics combines the advanced capabilities of enterprise data warehousing, data integration, and big data analytics. This allows you to query data depending on the requirements by utilizing dedicated and serverless resources. 

To clarify things, here is a quick comparison between Microsoft and Amazon services in data management functionalities. 

Data Warehouse  AWS Redshift  MS Azure 
Management and Administration  Depending upon the needs, the selection of the correct instance, size, and configuring manually  Dedicated and Serverless options are available 
Scalability  RA3 nodes computing and decoupled storage  For the dedicated option, storage exceeding the limit will be added manually. Whereas, in serverless option scales automatically 
Analytics Ecosystem  Its Analytics eco-system supports business intelligence with tools like AWS QuickSight  Analytics for business intelligence from PowerBI and NoSQL from CosmosDB 
Integrations Ecosystem  Data integration with AppFlow and DMS  Azure Data Factory for data integration 
Ingestion of Streaming Data  No built-in data support 

Options for ingesting streaming data: 

  • Kinesis Firehose: It is possible to set batch intervals as low as 60 seconds for scalable and neat real-time data loading into Redshift. 
Yes, Apache Spark streaming functionality 

Alternative methods: 

  • Azure’s Stream Analytics acts as a processing engine 
Columnar architecture  Yes 
Data recovery and backup  Yes 
Massively Parallel Processing (MPP)  Yes 
Price  Depends on the cluster configuration and reserved nodes can be purchased at a discount  Can access discounted reserved storage or pricing on demand 

Which one will you choose? 

Both platforms as discussed above have strong capabilities, and it’s difficult to pick one as a clear winner. AWS has more flexibility and extra features like pricing models. Azure, on the other hand, is great when it comes to the hybrid cloud due to its great integration capabilities with Microsoft’s stack. You can make the right pick as per the requirements of the organizations. Get in touch with our cloud experts, and start your cloud journey today.