AWS vs. Azure: What to Consider When Cloud Shopping
AWS vs. Azure is a tough comparison. These are two of the leading public cloud service providers, and they have both led the creation and innovation of new cloud services and products since the technology’s emergence.
In this article, we’ll compare these two giants of cloud computing to help you decide which to choose based on your organization’s specific needs. You’ll learn more about their core services, products, pricing models, and more.
Table of contents
- AWS vs. Azure: an overview
- Billing and pricing
- Scalability and compute features
- Data privacy and security
- Documentation and simplicity of use
- Logging and monitoring
- Database capabilities
- Open-source development
- Machine learning modeling
- Deploying applications
- Which should you choose?
AWS vs. Azure: an overview
|Billing and pricing||AWS is billed per hour||Azure is billed per minute|
|Compute features||AWS uses EC2 tailored to fit customer needs||Azure users can create a VM from a virtual hard disc (VHD)|
|Storage||AWS offers services like EBS, WAS S3, and Glacier||Azure Storage Services has disk storage, blob storage, and standard archive|
|Networking||AWS users can create private networks using AWS’ VPC||Azure utilizes VPN|
|Privacy and security||AWS does a great job selecting secure alternatives by default, which ensures strong privacy||Azure uses a dedicated Cloud Defender service, an AI-power tool|
|Licensing||AWS licenses are more feature-rich and configurable||Azure offers more software as a service (SaaS) features and it’s much easier to set up for Windows administrators|
|Documentation and simplicity of use||AWS offers a user-friendly and feature-rich dashboard and provides extensive documentation, although it does not store all information in one place||Azure stores all of the account information in one place, but its documentation system is less intuitive|
|Logging and monitoring||AWS’ SageMaker logs metrics and data via CloudWatch||Azure ML Studio uses MLFlow for data capturing and monitoring|
|Database capabilities||Amazon RDS supports the six standard database engines||Azure SQL Server Database and Cortana Intelligence Suite both support Spark, Storm, Hadoop, and HBase|
|Open-source development||AWS is great for open-source developers because it’s compatible with Linux and offers integrations for open-source applications||Azure is still working on embracing the open-source community|
|Machine learning modeling||AWS’ SageMaker grants users complete freedom and flexibility in building ML models||Azure ML Studio offers a codeless experiences with drag-and-drop elements|
|Deploying applications||AWS has Elastic Beanstalk, Lambda, container services, and more, but it does lack a couple app hosting features||Azure has a variety of app deployment options, like container services, cloud services, batches, functions, and more|
|Containerization||AWS runs containerized apps using Elastic Beanstalk, which supports Docker files with a command-line interface||Azure runs containerized apps using App Service, but you have to run the container inside of a web app so the process is slightly more complicated|
Now that you have an overview of some of the key differences between AWS vs. Azure, let’s dive into a bit more detail.
Billing and pricing
AWS and Azure both offer reasonable prices and a pay-as-you-go pricing model. They also both give new users free introductory packages to show how their systems can be integrated with on-premise software.
AWS is billed on an hourly basis. You can purchase instances either:
- On-demand: pay just for the services and resources you use
- Spot: bid for extra capacity availability
- Reserved: an upfront payment that reserves an instance for up to three years
Azure is billed by the minute, which means you get more exact pricing compared to AWS. Azure’s pricing model also allows you to use short-term commitments, choosing from prepaid or monthly charges.
These short-term plans grant users more flexibility. Additionally, pricing is available using BT MPLS ExpressRoute, which improves private corporate networks into the cloud with the necessary functionality.
However, Azure typically ends up being the more expensive option because Azure instances get more expensive as they grow in size. For example, Azure costs nearly twice as much as AWS for a 256GB RAM and 64vPCU configuration.
Scalability and compute features
Scalability is a common computing challenge.
AWS uses elastic cloud computing (EC2) to address scalability. With EC2, the resource footprint available may shrink or increase on-demand due to elastic cloud computing resource provisioning.
EC2 users can construct their virtual machines (VMs), choose the number of VMs they need, and change the power, size, and memory of required VMs. They can also pick machine images (MIs) that are pre-configured or modify their MIs.
Azure users have the option to create a VM from a virtual hard disc (VHD). This uses virtual scale sets to offer scalability and allow load balancing. The main difference is that EC2 may be customized for numerous uses, while Azure VMs work together with other cloud-deployment tools.
Cloud deployment success relies on having sufficient storage. When it comes to storage, AWS and Azure are nearly equal, however their offerings are different.
AWS offers services like Amazon simple storage service (S3), Glacier, and elastic block store (EBS). AWS S3 grants customers a scalable, secure, and robust storage solution for structured and unstructured data use cases.
Meanwhile, Azure Storage Services provides unstructured data storage, standard archive, and disk storage. Further, Azure offers data storage in Azure Blob, Azure Queues, Azure Disks, Azure Tables, and Azure Files.
Both AWS and Azure allow for an infinite number of acceptable objects. The main difference is that AWS has a 5 TB object size restriction, while Azure has a 4.75 TB limit.
AWS and Azure both offer cloud-compatible firewall alternatives to extend on-premises data centers into the cloud without risking data security or affecting business processes. However, outside of this similarity, AWS and Azure both take their own approach to creating isolated networks.
With AWS, users can utilize the cloud to generate isolated private networks using AWS’ virtual private cloud (VPC). Application programming interfaces (API) gateways are used for cross-premise connectivity.
Elastic load balancing guarantees smooth operation during network connectivity. Within a VPC, users have multiple options for making route tables, private IP ranges, network gateways, and more.
By contrast, Azure uses a virtual network rather than a VPC. A virtual private network (VPN) gateway offers cross-network communication.
Data privacy and security
AWS does a great job of selecting secure alternatives and settings by default, which ensures top-notch privacy.
Azure uses Microsoft’s Cloud Defender service for privacy and security. Cloud Defender is an AI-powered solution that protects against new and looming threats. But the downside here is that Azure services might not be completely secure by default. For example, virtual machine instances are deployed with all ports open unless they’re configured otherwise.
Most of the services offered on both platforms are identical. AWS and Azure both make sure their customers avoid dealing with licensing hassles or license mobility issues. Both platforms use a pay-as-you-go pricing structure, so customers only pay for the services they use.
AWS leads in terms of flexibility and adaptability. However, Azure has more software as a service (SaaS) features than AWS. This includes offerings like:
- Azure Site Recovery
- Azure Visual Studio Online
- Azure Scheduler
- Azure Event Hubs
If a customer previously paid for the service, they qualify for license mobility in Azure. Finally, while Azure is easier to set up for Windows administrators, AWS is better for configuration and features.
Documentation and simplicity of use
AWS is a good option for first-time cloud platform adopters because it provides greater ease of use. The AWS dashboard is user-friendly and feature-rich, making it simple for users to have a snapshot of their cloud environment. AWS also offers thorough documentation for its cloud services. The downside is that adding users and access rules is more complicated in AWS.
Azure lets users keep all accounts and information in one place, although its documentation system is less user-friendly and more difficult to search.
Logging and monitoring
AWS’ SageMaker logs model metrics and historical data in CloudWatch, which converts the data into a usable format and keeps the information for up to 15 months. It also lets you track model behavior and make updates as needed.
Azure ML Studio uses MLFlow for capturing and monitoring data. The overall procedure is intuitive due to visual presentation and graphical features. Users can set up automated logging, which eliminates the need to log statements explicitly.
Comparing the two, Azure beats SageMaker when it comes to simplicity of use and data presentation.
Both solutions provide a range of database services that can handle structured and unstructured data, as well as big data. When it comes to durability in data management, AWS users can utilize Amazon RDS. Azure offers the Azure SQL server database option.
Amazon’s relational database service (RDS) is compatible with all major database engines, including Microsoft SQL, Amazon Aurora, MariaDB, MySQL, PostgreSQL, and Oracle.
The Azure SQL server database solution is entirely based on MicrosoftSWL. When it comes to interface, Azure has a friendlier and more intuitive interface, while AWS offers more instances and better provisioning.
The two services are comparable when it comes to reach, with both offering analytics and big data capabilities. AWS uses Elastic MapReduce (EMR), and Azure offers HD Insights.
Additionally, Azure users can access the Cortana Intelligence Suite, which includes Hadoop, HBase, Storm, and Spark. Both systems are compatible with relational and NoSQL databases, so they’re durable, widely available, and offer automatic replication.
Compared to Azure, AWS offers a more mature environment for big data handling. While AWS offers additional instance types, Azure’s interface and tooling are easier to use, which makes it easy to complete various database operations.
AWS is a great option for open-source developers because it’s compatible with Linux. It also offers various integrations for different open-source applications.
Azure has an option for corporate customers that lets them employ existing active directory accounts to sign onto Azure and implement apps based on the .NET framework on Linux, Windows, and macOS environments.
Azure is still somewhat in the process of accepting the open-source community, which is part of the reason AWS dominates in the open-source cloud hosting space.
Machine learning modeling
Both AWS and Azure have studios for machine learning (ML) model development. Anyone looking to work with AWS artificial intelligence tools needs coding and data science skills.
AWS’ SageMaker provides total freedom and flexibility in building ML models. To take full advantage of AWS’ capabilities and best implement an idea, users should be experienced in Jupiter Notebook and an expert in Python. This makes SageMaker ideal for developers with lots of experience, strong coding skills, and data engineering expertise.
On the other hand, Azure ML Studio primarily offers a codeless experience. Users can build a comprehensive ML model with little to no programming experience; you don’t need to know Python or be an expert in advanced data science techniques to use it. Azure ML Studio is best for data analysts who prefer a simple interface and a visual presentation of elements.
A key difference between AWS SageMaker and Azure ML Studio is where resources are stored and how easy it is to find them. In SageMaker, resources and artifacts are stored in the same bucket and organized into different folders, which makes finding them pretty straightforward. In contrast, everything in Azure merges together. Resources related to the same model launch are frequently places in different locations, so it can be difficult to locate and study them.
A major benefit of using cloud providers is that it makes deploying applications extremely easy. When deploying applications, developers might want to execute their applications on multiple servers virtually by using platform as a service (PaaS) capabilities.
Azure has an assortment of app deployment options to support this, including cloud services, functions, batches, container services, and app services. AWS offers similar capabilities with Elastic Beanstalk, Lambda, batch, containers, etc.
However, AWS lacks a couple app hosting features, whereas Azure safeguards intellectual property and offers better processing for backend data streams.
AWS provides several container services for use cases, like IoT, the development of desktop computing environments, and mobile application development. It also offers native Docker support for containerization.
Azure is equally strong for containerization, if not a little stronger because it offers Hadoop support through Azure HDInsight. Windows containers and Hyper-V containers can both be integrated with Docker in Azure. Further, Windows or Linux containers can also run on the platform.
Containerized apps in AWS use Elastic Beanstalk, which can support Docker files with a command-line interface. The same functionality is performed in Azure by App Service, but the process is a bit more complicated because you have to run the container inside of a web app.
Which should you choose?
Both AWS and Azure are incredibly comprehensive cloud providers—you really can’t go wrong. Whichever you choose depends on your team’s priorities and strengths.
Unless you have short-term commitments and can benefit from Azure’s per minute billing, AWS is typically less expensive and is usually a better choice for first-time cloud adopters. However, Azure adopters are drawn to the availability of the larger Microsoft ecosystem, which includes Windows, productivity tools, and business apps. Discuss the pros and cons of each with your team to determine which cloud provider is the best fit for your organization.
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