AWS vs Azure 2018: A Comprehensive Comparison Guide

Both AWS and Azure have been leading the cloud computing market for years, but in 2018, they continued to evolve and improve their services. AWS had a significant lead in terms of market share and revenue.

In 2018, AWS launched 1,036 new services and features, more than doubling the number of services available on its platform. This is a testament to its commitment to innovation and customer satisfaction.

One of the key areas where AWS excelled in 2018 was in its machine learning capabilities. Its SageMaker platform allowed developers to build, train, and deploy machine learning models with ease.

Azure, on the other hand, made significant strides in its artificial intelligence capabilities, particularly with its cognitive services. Its Azure Cognitive Services allowed developers to build AI-powered applications with minimal coding required.

Virtualization and Infrastructure is a crucial aspect of cloud computing, and AWS and Azure are no exception. Each platform offers a range of virtual machines (VMs) with varying configurations.

AWS, Azure, and Google Cloud all provide compute services to run VMs, but they differ in their approach. AWS offers the biggest variety of data centers, while Azure uses real processor cores, which can deliver higher CPU performance for VMs.

If you need to run applications on VMs using real processor cores, Azure is the way to go. This is because Azure provides one processor with four real cores, whereas AWS and Google Cloud would create a VM processor with two cores and four threads using hyper-threading. Real cores can make a significant difference in performance, especially for resource-intensive applications.

Here's a brief comparison of the maximum VM computing configuration parameters for AWS, Azure, and Google Cloud:

Ultimately, the choice between AWS and Azure comes down to your specific needs. If you require a range of EC2 instances with different CPU and memory configurations, AWS might be the better choice.

All cloud-based platforms provide compute services to run virtual machines (VMs), select different configurations for VMs, and select a VM class. Virtual machines and storage are the most used services on cloud platforms.

AWS provides virtual machines running in Amazon Web Services, called Elastic Compute Cloud (EC2) instances. You can select EC2 instances with preconfigured settings or configure virtual hardware settings manually.

Azure virtual machines use real processor cores, one of Azure's main advantages. Real cores deliver higher CPU performance for VMs running in Azure versus VMs with similar configurations in other cloud platforms.

Google Cloud uses Google Compute Engine to run virtual machines in the Google Cloud Platform. Although it offers a smaller VM variety compared to AWS and Azure, Google is more focused on containers and Kubernetes for running horizontally scalable applications with a microservice architecture.

The maximum VM computing configuration parameters for AWS vs Azure vs Google cloud platforms are compared in the table below.

If your applications require running on VMs using real processor cores rather than virtual ones, opt for the Azure cloud.

AWS offers a secure, stable, and high-performance Linux operating system called Amazon Linux 2 that supports the latest EC2 instance capabilities.

This operating system includes security updates to ensure your Linux-based applications are protected from potential threats.

Amazon Linux 2 helps configure the optimal performance of Linux-based applications, making it an ideal choice for developers.

You can also access a wider range of open-source tools for developing Linux apps on AWS, giving you more flexibility and options for your projects.

By using Amazon Linux 2, you can take advantage of the latest advancements in Linux technology and ensure your applications run smoothly and efficiently.

AWS provides virtually limitless cloud storage, available on-demand, through its Simple Storage Service (S3). AWS S3 has greater flexibility and feature granularity compared to Azure.

Azure also offers virtually limitless cloud storage, but it only allows adding equivalent controls on an account level, whereas AWS allows you to assign different performance and price points to each object.

AWS S3 ensures data security through both server-side and client-side encryption, encrypting data at its origin before transmission and storage. This is a significant advantage over Azure, which does not have the same level of encryption capabilities.

AWS uses Advanced Encryption Standard (AES) with Galois Counter Mode (GCM) to enhance data security, and it also accommodates customer-provided keys (SSE-C) for server-side encryption.

AWS offers various encryption options, including SSE-KMS (Key Management Service) and SSE-S3, which relieve users from the complexities of key management.

Azure is actively working on implementing and improving the file backup and recovery functions, and its sub-service StorSimple has greatly benefited the efficiency of the company, allowing for saving up to 60% of the usual value.

AWS S3 has different performance and price points, including storage standard, auto-tiering, infrequent access, and Glacier for data archival, giving users more flexibility and control over their storage needs.

Here's a comparison of the cloud storage options with AWS vs Azure vs Google Cloud:

AWS uses security isolation as the default principle when you create an account, a virtual machine, or other objects to protect cloud resources against unauthorized access.

Amazon provides a high number of compliance certifications including GDPR, PCI-DSS, FIPS 140-2, HIPAA/HITECH, FedRAMP, and NIST 800-171.

You can get on-demand access to more than 2,500 security controls by using AWS Artifact.

Microsoft Azure also has strong cyber security controls with more than 90 compliance certifications in more than 50 different regions.

Azure Active Directory is a centralized authentication service that allows you to integrate your onsite Active Directory with Azure Active Directory in the cloud for secure authentication.

AWS and Google Cloud use the default Deny policy in access configuration, while Azure uses the Allow policy by default.

Azure Activity Logs and Azure Security Center provide many advantages compared to AWS, including the ability to move events between regions without manually building Lambda functions.

Key Management plays a crucial role in cloud security, and both AWS and Azure offer robust solutions to manage cryptographic keys.

AWS Key Management Service (KMS) stands out for its dual-key hierarchy, where master keys are generated for the creation of data keys, which are then used for data encryption and decryption processes.

AWS KMS ensures the highest level of security for key management, utilizing FIPS 140-2 validated hardware security modules for secure storage.

A centralized key management system allows for streamlined control over keys across the AWS ecosystem.

Seamless integration with various AWS services enhances data encryption, while automatic key rotation improves security by regularly updating keys.

Here are the key benefits of AWS KMS:

AWS IAM also offers a robust framework for managing users, groups, and permissions, but it lacks native Privileged Identity Management, which can be obtained through third-party solutions available through the AWS Marketplace.

A firewall is a crucial security measure that allows you to control access to your network and virtual machines. You can configure it to only allow traffic from trusted sources.

AWS provides a managed service called AWS Network Firewall, which can be managed in AWS Firewall Manager. It's divided into two categories: Network Firewall and Web Application Firewall.

The Network Firewall is used to filter network traffic for protocols like IP addresses, ports, and more. It includes features like packet filtering, VPN, deep packet inspection, website filtering, and DNS reputation filtering.

Azure offers multiple firewall services, including Azure Firewall Premium, Azure Application Gateway, and Azure Web Application Firewall. Each service is designed for specialized purposes.

Azure Firewall offers Network Address Translation (NAT) and filtering for IP addresses, TCP and UDP ports, and HTTPS traffic. Azure Firewall Premium also includes Intrusion Detection and Protection System (IDPS) and TLS inspection.

Google Cloud Platform allows you to configure firewall rules for ingress/egress traffic and secure network access to virtual machines. Firewall options are part of the VPC network configuration.

Here's a comparison of the firewalls offered by each cloud platform:

To ensure lower network latency, select a data center region that's geographically closest to your organization's physical location.

As you consider cloud security, it's essential to understand the features and capabilities of each platform. AWS, Azure, and Google Cloud all provide robust security features, but with some key differences.

AWS uses security isolation as the default principle when creating an account or virtual machine, protecting cloud resources against unauthorized access. This strict security policy can be adjusted as needed.

Azure Active Directory is a centralized authentication service that allows you to integrate your onsite Active Directory with Azure Active Directory in the cloud. This provides single sign-on across services and enhances security.

Google Cloud Platform is more centralized, with projects isolated from each other by default. The Cloud Security Command Center is equivalent to the Azure Security Center, offering a robust security management tool.

All three cloud platforms provide managed firewalls to configure secure network access to virtual machines and services. AWS offers the AWS Network Firewall and Web Application Firewall, while Azure provides Azure Firewall Premium, Azure Application Gateway, and Azure Web Application Firewall.

Here's a comparison of the key features of each platform's firewall:

AWS IAM offers a robust and flexible framework for managing users, groups, and permissions, with no additional charge for registered AWS users. This includes features like user groups, roles, multi-factor authentication, live access tracking, and policy management using JSON.

AWS GuardDuty is a fully managed threat detection service that employs sophisticated machine learning and anomaly detection techniques to detect unexpected and unauthorized activities that may indicate a security threat.

AWS Shield is a managed DDoS protection service that offers automatic safeguards against common and complex DDoS attacks, engineered to protect applications running on AWS. It provides two levels of service: Standard and Advanced, with Shield Advanced offering enhanced protections and 24/7 access to the AWS DDoS Response Team.

When choosing a cloud platform, databases and analytics are crucial components to consider. All three vendors - AWS, Azure, and Google Cloud - provide database as a service (DBaaS) options, allowing customers to work with databases without managing the infrastructure.

AWS provides the broadest range of database options, including relational databases like Amazon RDS and NoSQL databases like Amazon DynamoDB.

Azure offers great support for migration, flexible deployment options, and hybrid deployment, making it a good choice for those with specific security and privacy requirements. It's also cost-friendly for Microsoft SQL Server, especially when using IaaS.

Google Cloud's database services are user-friendly and provide high performance for workloads, making it a good choice for attaching databases to containers in microservice architecture.

Here's a comparison of the database services offered by each platform:

All three platforms also provide data analytics services, machine learning, and artificial intelligence. AWS offers a wide range of AI/ML services, including SageMaker, while Azure has improved its analytics and machine learning tools. Google Cloud has a special area of Big Data analytics available, with projects like Cloud Vision API and Cloud Speech API.

SQL Relational Databases are a crucial part of any database setup, and all three major cloud providers - AWS, Azure, and Google Cloud - offer top-notch relational database services.

AWS provides stellar managed services, Amazon RDS and Amazon Aurora, for running MySQL and PostgreSQL databases in the cloud. These services simplify database management, improve scalability, and automate disaster recovery.

One of the key differences between AWS RDS and Azure SQL is deployment. AWS RDS lets you allocate E2C instances to databases and provision storage capacity separately, which can lead to lower costs if you apply cloud cost optimization techniques.

Azure SQL databases, on the other hand, rely on Azure's multi-tenant architecture, which doesn't require specific virtual machines. This abstract container approach provides a platform for database management and auto-scaling.

Here's a comparison of relational database services offered by the three cloud providers:

In conclusion, each cloud provider has its strengths and weaknesses when it comes to relational databases. By understanding these differences, you can make an informed decision about which provider is best for your specific needs.

All three major cloud platforms - Amazon, Microsoft Azure, and Google Cloud - offer data analytics services, machine learning (ML), and artificial intelligence (AI). These services are widely used for data analysis, science, research work, and automation.

Amazon, Microsoft Azure, and Google Cloud provide machine learning as a service (MLaaS). Amazon's older ML service is called Amazon Machine Learning, while the newer one is SageMaker. SageMaker is preferred by data scientists.

AWS offers 12 AI/ML services, including SageMaker, Machine Learning, and Comprehend. Microsoft Azure's AI Platform has three services: Machine Learning, Azure Bot Service, and Cognitive Services. Google's AI Platform has nine services, including Cloud Machine Learning Engine and Cloud Vision API.

Here's a comparison of the ML/AI features offered by AWS, Microsoft Azure, and Google Cloud:

AWS made a breakthrough by launching Quick Sight analytics service, which includes ready-made templates and is less expensive than classical BI solutions. Microsoft Azure has improved its analytics and machine learning tools, and created the Data Lake Analytics subsystem. Google Cloud's special area of Big Data analytics is currently available, with projects like Cloud Vision API, Cloud Speech API, and Google Translate API.

When it comes to networking and regions, AWS and Azure have some key differences.

AWS has a virtual private cloud (VPC) architecture, which is a completely isolated logical network. This provides network redundancy for their cloud services. Google, on the other hand, uses the Andromeda architecture for networking, which is Google’s network virtualization stack.

Both AWS and Azure provide excellent networking solutions, offering automated server load balancing and connectivity to on-premise systems. Azure Virtual Networking (VNet) is Microsoft’s core cloud network product, which is closer to the traditional network architecture of data centers and private networks.

AWS has over 80 availability zones in 25 geographic regions, while Azure has more than 60 regions with at least 3 availability zones per region. Google Cloud Platform has 24 regions and 73 availability zones.

Here's a comparison of the regions and availability zones offered by each provider:

Azure is the clear winner in terms of the number of regions and availability zones, making it a great option for deploying virtual machines in the largest number of geographical locations over the world.

Network services are a crucial aspect of cloud computing, allowing you to create virtual networks and connect virtual machines to them. All three major cloud providers - AWS, Azure, and Google Cloud - offer similar network capabilities, including network redundancy for their cloud services.

AWS uses an internal architecture called virtual private cloud (VPC), which is a completely isolated logical network. This allows for precise configuration of your virtual network to meet specific requirements.

Azure's internal architecture is closer to traditional network architecture, with Azure Virtual Networking (VNet) being Microsoft's core cloud network product. Google, on the other hand, uses the Andromeda architecture for networking, which is Google's network virtualization stack.

Here's a comparison of network services offered by AWS, Azure, and Google Cloud:

Cloud providers also offer robust Virtual Private Network (VPN) functionalities to ensure secure data transmission. AWS and Azure both employ subnetting to divide networks, but AWS stands out with its extensive customization capabilities.

Regions and Availability Zones are the backbone of cloud computing, allowing you to deploy virtual machines in multiple geographical locations. Each cloud provider covers major areas like Europe, North America, Southeast Asia, East Asia, and China with their data centers.

Regions are the largest units of a cloud provider, containing multiple availability zones. A region is a separate geographical area where data centers physically exist. Data centers are connected via low-latency networks.

Availability zones are isolated physical locations within a region, connected via high-speed redundant networks. If one availability zone fails, other functioning zones provide services to customers.

Let's take a look at the numbers:

Azure has the largest number of regions and availability zones, making it a great choice if you need to deploy virtual machines in a large number of geographical locations.

Pricing is a crucial factor to consider when choosing between AWS and Azure. Prices can change over time, making it difficult to make a direct comparison.

AWS and Azure both offer pay-as-you-go models with charges applied per second, minute, or hour. This means you only pay for what you use.

Azure frequently emerges as the more cost-effective option, offering lower rates for similar services when considering on-demand pricing. However, AWS often outpaces Azure with more significant discounts for longer-term commitments.

AWS distinguishes itself with greater flexibility, permitting changes to instance types mid-contract, a level of adaptability not typically found with Azure. Both providers offer reserved instances, requiring a commitment to a certain level of usage over a fixed term.

Both AWS and Azure equip potential users with pricing calculators, enabling a detailed estimation of costs before committing to services. This tool is invaluable for businesses meticulously planning their cloud budgets.

GCP pricing can be complex, but it's mainly based on computing services like virtual machines.

The price depends on the region where a data center is located, which can affect your costs.

The CPU configuration of a VM, the amount of memory, disk space, and disk type (SSD or HDD) also impact the price.

Billing is provided on a per-hour and per-second basis for running a virtual machine.

You can get a discount if you pay for a 1-year commitment with one transaction, typically by selecting a reserved instance of the needed type.

Pricing can change over time, so it's essential to check the latest prices on the Google Cloud website.

Pricing is a crucial factor to consider when choosing between AWS and Azure. Both platforms offer competitive pricing, but the details can be complex.

AWS and Azure both use pay-as-you-go models, charging per second, minute, or hour for on-demand services. Azure often emerges as the more cost-effective option in this area.

Reserved instances require committing to a certain level of usage over a fixed term, available in one-year or three-year commitments from both providers. AWS offers more significant discounts for longer-term commitments.

AWS has greater flexibility when it comes to reserved instances, allowing changes to instance types mid-contract. This is not typically found with Azure.

Both AWS and Azure provide pricing calculators to help estimate costs before committing to services. This is invaluable for businesses planning their cloud budgets.

Here's a comparison of on-demand pricing and reserved instances:

Note that pricing can change over time, so it's essential to check the latest prices on the AWS, Azure, and Google Cloud websites.

AWS and Azure are both leaders in the cloud computing market, but they have distinct strengths and weaknesses. AWS has a broad range of features and services, with over 175 different tools and features, making it a one-stop-shop for many businesses.

In terms of market share, AWS has approximately one-third of the total market share, making it a significant player in the industry. Microsoft Azure, on the other hand, interfaces effectively with major Microsoft local systems, such as Windows Server and Active Directory.

Here's a comparison of the two platforms in a table format:

AWS has a clear advantage in terms of access zones, with more regions and access zones available. However, Azure's flexible pricing model and frequent discounts make it a more cost-effective option for some businesses.

All three major cloud providers - AWS, Azure, and Google Cloud - offer a range of analytical tools to help businesses make data-driven decisions.

AWS made a breakthrough with Quick Sight, a less expensive analytics service that includes ready-made templates.

Microsoft Azure has improved its analytics and machine learning tools, and created the Data Lake Analytics subsystem.

Google Cloud has a special area of Big Data analytics available, with projects like Cloud Vision API, Cloud Speech API, and Google Translate API that can be integrated into third-party resources.

AWS's partner ecosystem and overall product strategy are considered market leaders, with a wide variety of third-party software services available at AWS Marketplace.

Azure's significant advantage is its ability to interface effectively with major Microsoft local systems, such as Windows Server and Active Directory.

All three vendors offer data analytics services, machine learning (ML), and artificial intelligence (AI) as part of their cloud computing services.

Here's a comparison of the AI/ML services offered by each provider:

AWS offers SageMaker, a hosted environment for Jupyter notebooks with built-in model management, while Azure Machine Learning is a simpler service that allows developers to write, test, and deploy new algorithms.

AWS seems to have a clear advantage when it comes to access zones, with more regions and access zones available. Azure, on the other hand, is the most versatile platform when it comes to integration with open source systems and local systems, such as Microsoft tools.

AWS leads the pack in terms of the number of services offered. This is a significant advantage, especially for businesses with complex needs. However, Google Cloud's flexible pricing model and frequent discounts make it the most affordable option.

AWS has approximately one-third of the total market share, giving it a strong lead in the market. This is a significant advantage, but it's worth noting that Azure and Google Cloud still have a strong presence.

Here's a quick comparison of the three platforms:

AWS and Azure are two of the most popular cloud services, but they have some key differences that set them apart. AWS has been leading the market for over a decade, but Azure is quickly catching up.

One of the main differences between AWS and Azure is the breadth of services offered. AWS has a massive 175 different comprehensive tools and features, making it a one-stop-shop for most cloud needs. In contrast, Azure is tightly integrated with Microsoft's other offerings, including Windows, Office, and Teams.

AWS has a significant advantage in terms of market share, with 56% of the market as of 2021, compared to Azure's 33%. However, Azure is gaining ground quickly and is now more than twice as popular as Google Cloud.

Here's a brief comparison of the two:

AWS is a great choice for companies that need a wide range of cloud services, but Azure is a better option for businesses that already use Microsoft services and software. Ultimately, the choice between AWS and Azure will depend on each organization's specific needs and interests.