AWS vs Azure vs GCP Comparison – Foundational Overview

The Evolution of Cloud Giants: AWS vs Azure vs GCP

Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) have played a pivotal role in reshaping the world’s digital landscape, offering a myriad of services that cater to various needs. We’ll uncover how AWS, Azure, and GCP have not only competed but also pushed each other to new heights, driving forward the cloud revolution.

Amazon Cloud

From our perspective today, it is both amusing and hard to believe that Amazon started as a small e-commerce company grappling with technological challenges — a common pitfall for fast-evolving startups. In 2000, Amazon strategically decided to develop reusable modules for its internal development teams out of necessity. This move allowed teams to innovate faster by avoiding the need to build everything from the ground up every time.

Over time, the internal services within Amazon expanded, prompting internal stakeholders to recognize potential business prospects. Initially introduced in 2004 and later revamped in 2006 with three public pay-as-you-go services, AWS experienced a pivotal moment in 2006 with the debut of Elastic Compute Cloud (EC2) and Simple Storage Service (S3). The former enabled users to deploy virtual servers as per their requirements, while S3 delivered scalable storage solutions. These offerings both helped Amazon get its foot in the door of cloud computing and transformed the approach to IT infrastructure, offering companies flexible, scalable, and cost-effective alternatives to traditional on-premises data centers.

Since then, AWS has continuously expanded the range of services it offers, coming up with more and more options including computing, storage, databases, analytics, machine learning, etc. Its early, well-timed infrastructure investment enabled the construction of a vast global network of data centers which ensured performance and dependability for customers all over the world. What AWS did was not only grow at an unprecedented pace, but Amazon also democratized cloud computing access by introducing innovative services and consistently lowering prices.

Microsoft Azure

Simultaneously with Amazon’s cloud services debut, Microsoft was also making strides in the cloud arena, piecing together various services and creating reusable components for internal use. In 2008, Microsoft hinted at its cloud ambitions with a humorous name of Project Red Dog. When Windows Azure was launched in 2010, it fell short of expectations — it could not even run a regular Windows server.

Upon some introspection, Azure’s executives realized they needed to up their game to compete with AWS and Google. The big turnaround came in 2013 with Azure’s relaunch, which provided a more appealing alternative for businesses, particularly those that were heavily dependent on Microsoft products. Renamed Microsoft Azure in 2014, it signified Microsoft’s commitment to embracing a multi-platform cloud environment, including Linux and various programming languages.

Recognizing the importance of hybrid cloud configurations for businesses, Microsoft released Azure Stack in 2017. This breakthrough made it possible for companies to seamlessly combine the capabilities of the public and private clouds by integrating Azure services inside their own data centers. For businesses looking for the freedom to run apps in various on-premises and cloud settings, this shift was essential.

Google Cloud

Google first dabbled into cloud computing and internal infrastructure in the early 2000s to support its growing search engine and related services. Its future cloud attempts are set up by pioneering advances in data center architecture, network infrastructure, and software-defined networking.

In April 2008, Google formally entered the public cloud space with the introduction of Google App Engine preview, specially designed to speed up web application deployment and scalability. It introduced Google Storage in 2010, which was then renamed as Google Cloud Storage: to provide safe and scalable object storage options. During this period, Google also introduced Google Kubernetes Engine to capitalize on the growing popularity of microservices and containerization, as well as Google Compute Engine, which offered scalable virtual machines (VMs). The phrase “Google Cloud

Platform” was first used in 2013 to refer to a group of cloud services that included analytics, machine learning, computation, and data storage. In contrast to AWS and Microsoft, which in their early phases mostly concentrated on infrastructure as a service (IaaS), Google chose to take a platform as a service (PaaS) strategy. With the help of its well-known development staff, well-timed acquisitions, and ample resources, Google gradually turned GCP into a competitive force in the public cloud market.

Basic Service Offerings Comparison

What is crucial to recognize when making a comparison of AWS, Google Cloud and Azure is that each offers a diverse array of cloud services. These offerings are shaped by their respective origins, strategic priorities, and core technologies. Understanding the strengths of each platform enables you to tailor cloud adoption strategies to your organization’s goals and needs.

Compute Capabilities

Amazon
AWS is the industry leader thanks to its flexible serverless computing architecture, Elastic Compute Cloud (EC2), which has over 100 Availability Zones deployed globally, and its robust ecosystem of container services, which includes Amazon ECS and EKS. This wide range of computing options satisfies scalability, adaptability, and creative needs for a multitude of application requirements.

Microsoft Azure
Through Azure Kubernetes Service (AKS), which works smoothly with Microsoft’s extensive software stack, Azure offers dependable virtual machines and container services. Azure Functions broadens their serverless computing portfolio. Its primary benefit is that it provides adaptable hybrid cloud solutions, like Azure Stack, which are ideal for companies seeking a smooth transition from on-premises to cloud environments.

Google Cloud Platform
GCP sets itself apart with high-performance computing resources like Compute Engine and Google Kubernetes Engine (GKE) for containerized applications. The latter impresses with its scalability in meeting a wide range of workload requirements across industries and use cases. With Cloud Functions, its serverless computing capabilities are significantly enhanced. GCP is a compelling option for jobs requiring a large amount of processing power because it enables data-intensive applications and places a high priority on open standards.

Storage Solutions

Amazon
AWS provides a wide range of storage services, such as scalable solutions like Simple Storage Service (S3), robust substitutes like Amazon Elastic File System (EFS), and more. AWS also offers reliable and reasonably priced solutions, such as Amazon Glacier, which is perfect for long-term data backup and preservation. These systems provide flexible, secure, high-performance storage that can be tailored to meet various data storage requirements.

Microsoft Azure
Azure offers comparable storage options that are seamlessly integrated with its computing power to grant consumers even greater autonomy. File storage, Blob storage, and Disk storage are some of these choices. One notable feature of its hybrid storage solutions is the ease with which enterprises may transition between cloud and on-premises storage.

Google Cloud Platform
Persistent Disk, Filestore, and Google Cloud Storage are reliable storage choices from GCP, well-known for working with Google’s analytics and machine learning applications. The seamless integration of Google Cloud Bigtable, a fully managed, scalable NoSQL database service, with storage solutions offered by Google Cloud Platform is another feature. This integration makes it straightforward and easy to build apps that are driven by data.

Database Management

Amazon
AWS provides a wide range of database services, such as Amazon ElastiCache for in-memory data storage, Amazon DynamoDB for NoSQL solutions, and Amazon RDS for relational databases. Proven for its remarkable performance and scalability, AWS Aurora is compatible with both MySQL and PostgreSQL. AWS also offers Amazon Neptune, a fully managed graph database service that offers high availability and durability, with a track record of maintaining uptime levels exceeding 99.99%.

Microsoft Azure
Similar database services are offered by Azure, which includes Azure Cache for Redis in addition to Azure SQL Database and Cosmos DB for NoSQL databases. The latter, known for its global distribution and scalability, boasts serving millions of requests per second across billions of records for diverse workloads worldwide. With a track record of maintaining single-digit millisecond latencies, Azure Cosmos DB ensures high availability with 99.999% SLA-backed uptime. These services simplify the creation and deployment of applications by integrating easily into Azure’s ecosystem.

Google Cloud Platform
GCP is a database management powerhouse with Cloud SQL and Cloud Firestore for NoSQL and relational databases, respectively, and Cloud Bigtable for large-scale operational and analytical tasks. The database solutions offered by GCP are made to fulfill the demands of modern data environments by being scalable, performant, and easy to maintain.

Networking

What underpins all cloud services and facilitates their seamless interaction is networking. While the foundational concepts of networking have remained largely unchanged over time, their implementation strategies have significantly evolved. The speeds and reliability of modern networks have now been improved and can offer greater efficiency.

Historically, managing on-premises networks required the meticulous deployment and configuration of routers, switches, and firewalls. This often involved routine firmware updates and hardware maintenance. Though the advent of cloud computing has alleviated the need for direct hardware management, the responsibility for network configuration and security remains with the user.

In cloud environments, the cornerstone of connectivity is the virtual network, a service provided by major platforms. These virtual networks operate as cloud-based routing mechanisms which is the basis for efficient communication between services.

Let us lay out the basic terms: Azure designates this service as a Virtual Network (VNet), while AWS and GCP refer to it as a Virtual Private Cloud (VPC). Despite the different terminology, the core functionality remains the same: they segment resources into subnets in order to facilitate secure and controlled communication within the network. Now, we are going to look in more detail.

Amazon Web Services
AWS implements a regional networking approach through its Virtual Private Cloud (VPC). Each VPC is established within a specific region, which includes multiple availability zones—distinct locations designed to prevent failures from affecting the entire region. Within the availability zones, AWS defines two types of subnets:

• public subnets – have direct access to the internet;
• private subnets – do not openly access the web.

By default, all resources, or instances as AWS calls them, within a VPC can communicate with one another, regardless of their subnet type. This architecture offers a versatile and resilient networking solution, well-suited for a variety of applications and workload requirements.

Microsoft Azure
In Azure, each Virtual Network (VNet) is region-based, meaning it resides in a single region that consists of one or more data centers. Within a VNet, subnets are created to allow all assigned resources to communicate with each other and access the internet by default. However, for resources to connect to the network, they must be located in the same region as the VNet.

Organizations in need of redundancy and high availability, will benefit from deploying additional VNets across different regions that enhance workload distribution and fault tolerance.

Google Cloud Platform
Unlike AWS and Azure, which restrict their virtual networks to specific regions, GCP offers broader geographic flexibility with a VPC covering multiple regions. Once a global VPC is set up, the VPC automatically creates region-specific subnets locally. This setup means that all your instances, like virtual machines, can communicate with each other across different regions by default. GCP’s global VPC is particularly useful for businesses that need smooth, global connectivity and want to manage deployments across various locations seamlessly.

Peering and Gateways

Networks and subnets for each provider sorted out, let’s look at how we can connect them. Since communication among different VPCs and Vnets in the services is crucial, each provider supports peering between virtual networks: concept that connects two virtual networks to allow them to interact directly without routing traffic over the public internet.

In all three providers in the review, peering is non-transitive. Essentially, this means that if Network A is connected to Network B, and Network B is connected to Network C, Networks A and C cannot interact unless we set up a direct peering link between them. Alas, this approach can quickly become impractical as you scale. For each new network, one would need to establish additional peering connections to maintain communication, which can get cumbersome and difficult to manage as the number of networks increases.

There is a streamlining solution for this, namely a hub-and-spoke model, where multiple networks connect through a single central hub with the help of gateways.

Amazon
So as to seamlessly connect multiple VPCs within a region and ensure traffic flow between them, AWS uses a Transit Gateway. If connections across multiple regions are needed, inter-region peering links these Transit Gateways to extend connectivity. AWS utilizes two types of gateways:

• Virtual Private Gateway – to handle VPN access & connect to remote networks;
• AWS Direct Connect Gateway – to form a private link between the on-premises network and AWS for high-bandwidth, dedicated connections. This connection method needs a third-party provider located near AWS Data Centers to ensure a reliable connection.

Microsoft Azure
A VNet can act as a central hub in a Hub-and-Spoke model. Peering connects other VNets (known as the spokes) to this hub, which contains a gateway to manage traffic between the networks. Gateways, of which Azure has specific types to suit various needs, also facilitate connections outside of Azure.

A VPN gateway sets up a secure connection between Azure and your on-premises network, ensuring safe and encrypted data transfer. For a faster and more private link, one can go for the ExpressRoute gateway, which provides a dedicated connection to your on-premises network through a third-party network, with built-in redundancy for extra reliability.

Google Cloud Platform
In contrast to the region-specific VPCs used by AWS and Azure, Google Cloud services take a different approach to networking. GCP makes use of the following.

• Global Virtual Private Cloud (VPC) model:
  This means all subnets within the VPC can interact boundlessly.
• The “project” concept:
  When a VPC belongs to a specific project, and while subnets within the same project can communicate, they can’t interact with subnets in another project unless you establish VPC peering between them.
• Shared VPC:
  A feature which allows multiple projects to share a central VPC, making it easier to manage connectivity across projects.

For hybrid connectivity (linking cloud networks with on-premises systems), GCP provides two gateway options:

• Cloud VPN – for secure connections over the public internet, and
• Cloud Interconnect – for private, dedicated circuits.

Like Azure’s ExpressRoute and AWS’s Direct Connect, these services offer secure and reliable connections between your on-premises network and GCP.

Regardless of your provider choice and business requirements, planning your IP address spaces is crucial from the outset. Since overlapping subnets can not only create conflicts with peering or hybrid connections but also disrupt the multi-cloud strategy, supported by hybrid connections, – the proactive approach minimizes the risk of connectivity issues and helps maintain the efficiency of your network infrastructure.

Load balancing

Apart from peering and gateways, what aids in distributing connections among multiple instances of a service is load balancing. It enhances both availability and performance by distributing the workload across several service instances. Each cloud provider offers various load-balancing options tailored to different needs to guarantee optimal app performance, and we are about to discover what those options are.

Amazon
In AWS, you have several load-balancing options to address different needs:

• AWS Network Load Balancer operates at the transport layer, therefore managing connections and distributing SSL or TCP traffic.
• AWS Application Load Balancer functions at the application layer and offers advanced routing capabilities like path-based routing and SSL termination.
• AWS Route 53, acting as a DNS-based load balancer, directs traffic based on rules you configure, including health checks, geographic considerations, and latency-based routing.

Microsoft Azure
This provider has a number of load-balancing options to choose from as well:

• Azure Load Balancer operates at the protocol level, managing traffic distribution to ensure high availability.
• Azure Application Gateway operates at an application level; it also has more advanced features, such as SSL termination and URL-based routing.
• Azure Traffic Manager acts as a DNS-based load balancer in order to direct traffic based on DNS name resolution and configurable rules.
• Azure Front Door is a global load balancer that enhances performance by offloading SSL encryption and directing traffic to the closest resource according to your configuration.

Google Cloud Platform
And finally, let’s look at load balancing with GCP, which has its own distinct approach:

• Internal Load Balancer handles traffic coming from within Google Cloud. It operates regionally and can manage traffic using TCP or UDP ports, or act as a proxy for HTTP and HTTPS traffic.
• External Load Balancer is used for traffic originating from the internet. It can be either regional or global and supports passthrough or proxy modes to route traffic effectively.

There is something that one interested in achieving high availability and resilience had better keep in mind when browsing through available options. For example, if all the resources that a load balancer handles are housed in a single data center, then simply deploying the load balancer will not be enough. Should that data center fail, your entire setup is at risk. Instead, aim for a more robust solution by combining global and regional load balancers, along with internal and external ones, to create a highly available and resilient infrastructure.

Security Features

Apart from cloud computing performance and efficiency, more and more entrepreneurs are focusing on reliability and security, especially given that breaches become increasingly common. So, this section is going to overview the security offerings from the cloud providers.

An introduction to cloud security

The thing about cloud security is that it is far more complicated than switch flipping or box checking. Because security entails a combination of various controls and settings, there is often confusion, primarily about the responsibilities distributed within an organization. What makes this even more problematic is that some companies assume the cloud providers handle all aspects of security, which is not true at all.

One can more easily approach cloud security when having considered the shared responsibility model.

Shared responsibility models for Azure, AWS, and GCP

Essentially, the shared responsibility model is a framework that helps define where accountability for security tasks lies between the cloud provider and the organization. The three platforms’ implementation of the shared responsibility model are presented below.

Amazon
AWS holds a very straightforward approach to the shared responsibility model by dividing it into two key areas.

• Customers are responsible for securing their data, user accounts, applications, and anything they run in the cloud.
• AWS accounts for securing the cloud itself, including the underlying hardware in their data centers, such as physical servers, storage, and networking infrastructure.

Microsoft Azure

Azure has adopted a bit more complicated view by dividing security responsibility into three main categories:

• Customer Responsibility
  The customer is always responsible for their own data, information, devices (like mobiles and PCs), and user accounts (also known as identities).
• Shared Responsibility
  This varies depending on the cloud model—whether it is Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS). In this category, the responsibilities are shared, and it can get more complex.
• Provider Responsibility
  The cloud provider handles all security aspects for their services, whether SaaS, PaaS, or IaaS. This includes securing the physical infrastructure in their data centers.

Google Cloud Platform
Google’s approach to the shared responsibility model is quite detailed in specifying who is responsible for security in various instances. Overall, while AWS, Azure, and Google Cloud all adhere to the same fundamental principles of shared responsibility, each has its own specific approach to managing security responsibilities.

Identity and Access Management (IAM)

Generally speaking, regardless of the specificities of shared responsibility models, organizations are responsible for managing user accounts. This falls under identity and access management (IAM), which involves defining user access and managing roles, also known as role-based access control (RBAC).

Here is a quick overview of IAM options across Google Cloud Platform (GCP), AWS, and Azure:

• Multi Factor Authentication (MFA)
  All three platforms support MFA to enhance security by requiring an additional verification step beyond just a password.
• Single Sign-On (SSO)
  Each platform provides SSO capabilities that allow users to log in once and gain access to multiple services.
• Role-Based Access Control (RBAC)
  Built-in RBAC is available on all three platforms, enabling you to assign roles and permissions to users.
• Privileged Access Management (PAM) differs slightly across these platforms:
• Azure offers Privileged Identity Management, which provides just-in-time privileged access to Azure Active Directory (AD) and Azure resources.
• AWS and GCP do not have built-in PAM features but support third-party solutions available in their respective marketplaces to manage privileged accounts.

These tools and services help ensure secure and efficient management of user access and roles across cloud environments.

IaaS Security

For securing virtualized computing resources delivered via the IaaS (Infrastructure as a Service) model, cloud providers offer a range of robust security solutions designed to protect data, applications, and networks. Let us examine some of those.

DDoS Protection
Distributed denial of service protection is a feature that is handled in essentially the same way across providers, but is part of different offerings, namely:

• AWS offers Shield
• Azure provides DDoS Protection
• Google Cloud uses Google Cloud Armor

Secrets Management
The three cloud providers offer similar capabilities for storing passwords, keys, and certificates within different features.

• AWS and Google Cloud provide this functionality under the name of Secrets Manager.
• Azure operates Key Vault.

Virtual Private Networking (VPN)

• AWS: AWS VPN supports both point-to-site and site-to-site connections, with a limit of 10 site-to-site connections per VPN gateway.
• Azure: VPN Gateway supports both point-to-site and site-to-site VPNs, allowing up to 30 site-to-site connections per gateway.
• Google Cloud: Cloud VPN supports site-to-site VPN connections but does not offer point-to-site options.

Data security
Next up, we shall explore how the major cloud platforms handle security in Platform as a Service (PaaS) offerings, with a focus on safeguarding data, a prime target for hackers due to its sensitive nature.

AWS, Azure and GCP have offer similar security measures for databases, including the following:

• Identity and Access Management (IAM) policies
  These policies define who has access to what within your cloud environment.
• Firewall rules and IP whitelisting
  This allows organizations to secure their databases by only permitting connections from specific IP addresses, typically the organization’s own.
• Encryption in transit (TLS)
  The feature ensures that any data sent to or from the database is encrypted and secure during transmission.
• Encryption at rest (TDE)
  This protects data stored on the database by encrypting it on the hard drive, ensuring its security even when not in use.

Compliance

Similar to organizations, which are required to comply with security regulations, cloud workloads must also adhere to these standards. Further we will find out how the major cloud platforms support compliance with these requirements.

All three platforms offer compliance tools that support widely recognized standards like ISO 27001 and PCI DSS. More specifically, Azure Security Center from Azure, Trust and Security Center from Google Cloud, and Amazon Inspector from AWS.

All of those tools can audit your deployed resources and provide recommendations on security best practices. And it proves a significant step towards ensuring the absence of overlooked critical security or configuration issues, as well as overall environment security.

Serverless

The serverless offerings by AWS, Azure, and GCP are the object of our review in this section of Cloud Comparison, including frameworks, functions, orchestration, and debugging. Keep reading to discover the features of pay-per-use API and other serverless offerings by the major cloud providers.

What is serverless?

In the light of confusion around the term ‘serverless’, let us briefly define what the topic of our examination is.

At its core, serverless is a way to build and run applications without the need to manage the underlying servers. Unlike traditional infrastructure-as-a-service (IaaS), where you are responsible for maintaining the operating system and the applications on the server, serverless takes that burden off your hands. Servers are still involved, but you will not have to deal with managing their capacity, performance patches, or fault tolerance.

Not having to deal with servers is a major advantage for developers, though the real treat of serverless is the scalability it offers. It is serverless that empowers apps to scale up and down smoothly depending on the demand, with no manual intervention.

Traditional server-based apps lose in comparison for they require the resources to keep running continuously, just in case users need them. In a serverless pay-as-you-go model, when there is no activity, processing ceases, and consequently, there are no associated costs.

Functions as a Service

Functions as a service (FaaS), which are typically event-driven, are the basis for serverless architecture.

This means that whenever something occurs, such as a REST API request coming in or a file being uploaded to cloud storage, the function automatically triggers and performs the programmed task. While the exact process differs between providers, the general idea is to configure your container to run your code in their environment.

The kind of event-driven processing is managed with Lambda at AWS, Functions at Azure, and Cloud Functions at GCP. All these services support popular programming languages like Python, Java, Node.js, and C#. Should your preferred language not be available, you can use a custom runtime by creating a Docker container and setting it up on the function-as-a-service platform.

Frameworks

Managing and deploying a few serverless functions is fairly straightforward. If those are not enough for your application, this is where the challenge begins.

Handling an entire app with hundreds or even thousands of functions is complicated, and this complication is addressed by serverless frameworks. These frameworks offer a set of tools that streamline the deployment of numerous serverless resources, which simplifies the management of a large-scale environment.

Amazon has developed the Serverless Application Model (or SAM) to simplify the creation and deployment of serverless applications on AWS. Given that SAM is tailored for AWS resources, it operates exclusively on AWS.

A valid multi-cloud solution, with substantial documentation and customer base alike, is the Serverless Framework. It works with clouds including AWS, Azure, and GCP.

Other notable frameworks include Zappa and Up Architect. On top of those, Terraform and Pulumi can manage serverless environments, particularly if already used for infrastructure as code.

Orchestration

Whereas having a framework and a collection of functions is like having a toolbox and tools, orchestration acts as a blueprint to guide and organize how everything fits together in a complex project.

Much like middleware in traditional IT setups, orchestration tools play the role of linking and coordinating separate functions to achieve a larger goal in the cloud world. More specifically, AWS offers Step Functions as its solution; Azure has Durable Functions, which allow you to build stateful workflows using orchestration and entity functions; and Google Cloud recommends using Workflows for this purpose.

Debugging

Despite the advantages of the serverless architecture, troubleshooting serverless apps tends to be quite challenging. Typically, developers prefer to debug serverless components locally before deploying them to the cloud. Fortunately for them, major cloud platforms offer the tools that enable you to simulate most components locally and step through your serverless code within your IDE. Taking that into account, once an application is live on the cloud, debugging still becomes more complex and requires additional strategies.

• Tracing your app and identifying errors or performance issues can be taken care of with AWS X-Ray on AWS, Application Insights on Azure, and GCP’s Cloud Trace.
• If you prefer digging into logs, AWS has CloudWatch for that. Azure lets you review logs from the App Service platform, or you can also pipe them into Application Insights as well. As for GCP, it provides similar capabilities through Cloud Logging and error reporting services.

Containers

In this section of cloud provider comparison, we will dive into how different platforms handle containers, focusing on their options for deploying and running containerized applications.

What are containers?
A container bundles everything needed to run your application—dependencies, configurations, and more—into a single lightweight package. In contrast to traditional applications that run directly on the operating system, containers operate on a container runtime, abstracting away the OS details. This makes your apps highly portable and significantly accelerates development and deployment.

The most commonly used containerization technology is Docker, though other options exist in different environments.

Container registries
Building a container image – a static file containing executable code that can generate a container – is the first step in the container registration process. This image is then stored in a container registry, where one can manage access, versions, and security.

All three cloud providers support containers, their clusters and registries. On AWS, this service is called the Elastic Container Registry (ECR), while Azure refers to it as the Azure Container Registry. GCP also offers a container registry but goes a step further with its Artifact Registry, which handles not just container images but also language packages like Maven and NPM, and Debian OS packages.

Standalone container limitations
Although you can deploy container images directly on virtual machine instances on AWS, Azure or GCP, this Infrastructure as a Service (IaaS) approach comes with significant administrative overhead.

The reason is that container images are static files containing all the elements needed to create a container, but when run as standalone containers, they lack essential features like replication, auto-healing, auto-scaling, and load balancing. These functionalities are crucial for modern applications, which is why container images deployment necessitates orchestration tools like Kubernetes to automate and manage container clusters efficiently.

Kubernetes

In the same manner as it was for the previous services in this comparison, each of the three major cloud platforms offers its own managed Kubernetes service.

• On AWS, there is Elastic Kubernetes Service (EKS), the most widely adopted among its alternatives.
• On Azure, it is Azure Kubernetes Service (AKS), which has often got more cost-efficient bills than other similar services.
• And on Google Cloud, its Google Kubernetes Engine (GKE) offers the biggest number of features and automation.

Since you have the choice among the cloud providers, if your applications predominantly run on a specific cloud platform—whether it’s Azure, AWS, or GCP—it makes sense to use that platform’s Kubernetes service. Doing so ensures seamless integration with other services you are already utilizing on that cloud, for maximal compatibility.

Amazon

• Kubernetes Offering: Elastic Kubernetes Service (EKS) was released in 2018.
• Additional Container Orchestration: AWS also offers Elastic Container Service (ECS) for managing container clusters, which may be preferred for tight integrations with other AWS services.
• Government Cloud: Kubernetes on AWS can be run on the government cloud, for it meets the needs of federal governments worldwide.
• Automation: EKS requires more manual upgrades and node health repairs compared to other platforms.
• Cluster Nodes: AWS supports both virtual machine nodes with GPUs and bare metal machines for Kubernetes clusters.
• Command Line Support: AWS’s command line support for Kubernetes is more limited compared to Azure and GCP.
• Service Mesh: AWS offers an integrated service mesh called App Mesh for microservices.
• Cluster Scale: EKS can support up to 100 nodes in a Kubernetes cluster.

Microsoft Azure

• Kubernetes Offering: Azure Kubernetes Service (AKS) was also released in 2018.
• Integration: Azure integrated its service with Amazon Kubernetes Service as Kubernetes adoption grew.
• Government Cloud: Kubernetes on Azure can be run on the government cloud because it complies with federal laws.
• Automation: AKS automatically handles security patches on the control plane and upgrades cluster nodes.
• Cluster Nodes: Azure supports virtual machine nodes with GPUs enabled.
• Command Line Support: Azure provides complete command line support for working with Kubernetes.
• Service Mesh: Currently, Azure does not have an integrated service mesh for microservices.
• Cluster Scale: AKS can support up to 500 nodes in a Kubernetes cluster.

Google Cloud Platform

• Kubernetes Offering: Google Kubernetes Engine (GKE) was released in 2015, making it the oldest among the three, as Google originally invented Kubernetes.
• Government Cloud: GKE does not support running Kubernetes on the government cloud.
• Automation: GKE is highly automated in security patches on the control plane and node upgrades.
• Cluster Nodes: GCP supports virtual machine nodes with GPUs enabled.
• Command Line Support: GKE offers complete command line support for Kubernetes.
• Service Mesh: GKE provides an integrated service mesh called Istio for microservices.
• Cluster Scale: GKE can support up to 5,000 nodes in a Kubernetes cluster, making it the most scalable option.

Serverless containers

If you would like to deploy and run containerized applications without the hassle of managing infrastructure or setting up clusters, you can use serverless containers. Let us look in on what the cloud providers have to offer.

Amazon
AWS has developed Fargate, a serverless solution that eliminates the need to manage infrastructure for container clusters. The solution is unique in that it works with an orchestrated cluster, either EKS or ECS, to abstract away the overhead of scaling, patching, and managing servers.

Microsoft Azure
Azure was the first in the industry to introduce serverless containers with Azure Container Instances (ACI). This feature allows you to run containerized applications without managing infrastructure or the need for a Kubernetes cluster.

Google Cloud Platform
GCP offers Cloud Run, which enables you to run containerized workloads in a serverless manner. Like Azure Container Instances, Cloud Run does not require an underlying Kubernetes cluster as it provides a simple way to deploy and run containerized applications without managing the infrastructure.

This is how each major cloud provider approaches serverless containers in order to simplify the process and eliminate the need for manual infrastructure management, allowing you to focus on deploying your applications efficiently.

Analytics and Machine Learning

Amazon
Among the many analytics and machine learning tools that set AWS apart are AWS SageMaker, which is used to create and apply machine learning models, Amazon Redshift, which is used for data warehousing and has over 1 million active users, and Amazon EMR, which is used for big data processing. These are all the instruments that make data-driven decisions possible.

Microsoft Azure
Azure’s ecosystem features Azure Synapse Analytics (with over 85% of Fortune 500 companies leveraging it) and Azure HDInsight for big data solutions. As for AI applications, Microsoft offers Azure Machine Learning, which is actively utilized by more than 200,000 developers. These services allow Azure to easily combine analytics and machine learning capabilities, allowing enterprises to effectively exploit data insights and AI capabilities.

Google Cloud Platform
GCP is well-known for its machine learning and advanced analytics services, which make use of Google’s artificial intelligence know-how. The two most popular products offered by GCP are Google BigQuery and Google AI Platform. GCP’s commitment to analytics and machine learning is evident from its substantial user base, with over 4 million active users leveraging Google BigQuery for data analysis and more than 1.5 million developers utilizing Google AI Platform to build and deploy AI models.

What are the Key Strengths of AWS, Azure, and GCP?

Top-tier cloud service providers include AWS, Microsoft Azure, and Google Cloud Platform (GCP), each with a unique set of advantages catered to distinct business requirements. Business executives must understand the unique features of GCP vs AWS vs Azure in order to align their cloud strategy with the specific requirements of their company, whether those needs are related to cost-effectiveness, interoperability, global reach, or availability of services. Let’s examine some of each platform’s competitive advantages.

Amazon Web Services (AWS) Merits

With cloud experience seven years more extensive than its competitors, AWS is widely recognized for its expansive global network and diverse range of services, making it an option suitable for companies from start-ups to major multinational corporations. Its position in the market has been further cemented by the longer tenure, which has also led to a larger pool of personnel experienced with AWS services. We are going to dive deeper into each of the prominent benefits of Amazon.

Extensive Global Infrastructure

Among cloud providers, AWS has the largest network of data centers, which means its reliability and maximized performance. Due to the over 100 AWS Availability Zones (distinct data centers within AWS regions that are designed to be isolated from each other in terms of power, cooling, and network connectivity), the fault tolerance and stability of applications and services hosted on AWS is supreme. The extensive worldwide reach also allows services and applications to be easily deployed in a variety of locations, adhering to local laws and regulations.

Comprehensive Service Offerings
Huge experience in cloud services corresponds to an unrivaled portfolio of services, including processing, storage, databases, machine learning, and analytics. This means that any application, from basic website hosting to intricate machine learning projects, will benefit from this spectrum of services.

Deep Market Experience
Another side effect of Amazon’s extensive experience in the cloud computing space is a profound grasp of the needs of clients across industries, which are covered with an expanded system of partners, developers, and third-party tools.

Innovation Leadership
The speed at which new services and features are released by AWS demonstrates its dedication to innovation. As a market leader, AWS maintains its position by staying at the forefront of cloud technology breakthroughs and adapting constantly to suit the shifting demands of the digital ecosystem.

Microsoft Azure Powers

An indispensable part of the Microsoft ecosystem, Azure provides smooth hybrid cloud solutions and robust support for enterprises using Microsoft software. What this integration guarantees is a consistent development experience and a seamless cloud migration.

Integration with Microsoft Ecosystem
If you are a devoted Microsoft product customer, you are sure to be satisfied with a consistent user experience that Azure’s smooth integration with the company’s array of programs and services guarantees. By utilizing such well-known tools as Windows Server and Active Directory, this connection increases productivity by simplifying migration.

Hybrid Cloud Capabilities
Strong support for hybrid cloud configurations is a noteworthy feature of Microsoft: more specifically, Azure Stack offers deployment choices that are flexible and allow enterprises to extend Azure services into their own data centers. This characteristic merits further examination if you aim to combine cloud services with on-premises infrastructure.

Global Data Center Network
For the users striving for their business’s international presence, Azure developed an extensive data center network. The fact that regardless of one’s location, one can rely on both the application’s performance and the residency regulations compliance due to the data centers spread across diverse sites, means a lot for the worldwide presence of an enterprise.

Focus on Security and Compliance
Speaking of compliance, in case your business is committed to abiding by strict regulations and demanding requirements in a sphere adjacent to finance, healthcare or legislature, Azure has a wide range of tools and certifications designed particularly for such purposes.

Google Cloud Platform (GCP) Advantages

Despite its initial lack of a competitive edge or outstanding cloud expertise, Google Cloud Platform has succeeded in making up for this. The cloud provider has been adding executives to their team from well-known enterprise-focused companies like SAP and Oracle in an effort to strengthen their position. Another undeniable merit is the founding of Kubernetes, one of the most popular systems for container orchestration which has recently been on the rise.

Data Analytics and ML Expertise
GCP leverages Google’s state-of-the-art data processing capability to excel in machine learning and data analytics. Several examples of platforms that provide reliable solutions for managing large datasets and developing sophisticated ML algorithms include BigQuery and TensorFlow.

High-Performance Computing and Networking
The infrastructure powering Google’s own services accounts for the GCP’s notable networking and processing capabilities. Leveraging this ensures optimal performance for applications that have high demands on processing power and fast data transmission rates.

Support for Open Source and Multi-Cloud
GCP’s support of Kubernetes fulfills the needs of developers and companies looking for flexible deployment options by enabling flexible and transportable cloud solutions. In this way, Google exhibits its commitment to open-source technologies and multi-cloud environments.

Cost-Effective Pricing Models
GCP’s current pricing strategies make it a desirable choice for those on a tight budget. With such pricing techniques as customized machine types and discounts for extended use, Google Cloud Platform provides affordable cloud computing options.

Key Weakness of AWS, Azure, and GCP

Apart from their competitive advantages, each of the major cloud providers comes with its weaknesses. Whether the challenges originate in corporate backgrounds, market strategies, or technological approaches, businesses must comprehend the inherent cloud platforms’ limitations as they directly impact cloud computing services.

Cloud providers’ efforts to tackle these obstacles will influence the competitive environment in cloud computing. It is essential for enterprises to keep up with these advancements to make well-informed decisions when opting for the best-suiting cloud vendor that will cover their computing needs.

What recognizing the drawbacks of each platform does is prepare business executives for risk mitigation by improving security measures, resolving data transfer problems, or getting ready for unexpected service outages. Armed with the information on a provider’s limitations, companies can gain an advantage in negotiations with the vendor and make sure the platform they select meets their unique business needs.

Amazon Web Services (AWS) disadvantages

Market Perception and Competition
A first-glance benefit, which in fact undermines AWS is the broad reach of Amazon in a variety of businesses. Companies that operate in industries where Amazon is heavily represented frequently view it as a direct competitor. Following this judgement, businesses could be reluctant to use AWS lest it should unintentionally help their rivals. This barrier might even question AWS’s ability to gain market share in some industries as Amazon continues to grow.

Regulatory Scrutiny
Another outcome that originates in Amazon’s extensive involvement across industries is the scrutiny from international regulatory agencies and governments. The examination of AWS’s parent firm, Amazon, encompasses everything from data privacy and security to allegedly monopolistic behaviors. These legal issues may, though hypothetically, affect AWS’s operations, requiring adjustments to company procedures.

Amazon Web Services are frequently advised by experts in both legal and IT industries to proactively participate in policy talks and set industry norms for fair competition, privacy, and security. Along with its cross-industry leading image, AWS have the chance to establish a standard for the industry by being a leader in openness and compliance.

Pricing Opacity
One of the biggest obstacles facing AWS consumers is the platform’s complex and sometimes confusing pricing structure. Monthly bills from AWS’s billing system can be unexpected due to a number of reasons, featuring regional differences in infrastructure and tax rates. Complicating matters further is the requirement to select among various tech support packages (Developer, Business, and Enterprise), each with its own cost. This intricacy can be especially daunting for small business owners who don’t have the technical know-how to deal with these nuances.

Although AWS is respected for its dependability, the possibility of unexpected charges emphasizes how crucial it is to become familiar with AWS’s pricing structure or look for support from an AWS-certified partner who is qualified to bring more transparency into the billing system. This strategy can assist reduce the possibility of unanticipated costs while guaranteeing that customers can utilize AWS’s cloud services without sacrificing control over costs or quality.

Microsoft Azure drawbacks

Open-source Adoption
Microsoft has made significant progress in supporting open-source software. Nevertheless, the company is still seen as a dominant player in proprietary software, which comes with its benefits, such as the ecosystem’s large user base, as well as its disadvantages. Azure is still trying to strike a balance between supporting open-source groups and promoting its proprietary technologies.Engaging with the open-source community through contributions, support, and transparent policies is essential to solidify Azure’s reputation as a cloud platform supportive of open innovation.

Cloud Transition In Process
Selling software licenses has always been the major expertise and source of income for Microsoft’s business strategy. As the market moves towards cloud computing and subscription-based business models, Azure is in the process of adjusting its clientele and sources of income.

Making the most of Microsoft’s huge enterprise client base and offering them tailored migration services, hybrid cloud solutions, as well as other alluring incentives will ease the transition to the cloud. Helping the customers in their migration journey will also match Microsoft’s revenue models with the change in demand.

Hybrid Cloud Integration
Although Azure is well-known for its hybrid cloud capabilities, there may be operational and technical challenges when combining Azure services with on-premises infrastructure. These difficulties include creating safe connections, preserving data consistency, and making sure that everything works properly in hybrid contexts. Putting the fact that Azure places a strong focus on hybrid support aside, managing the challenges of reaching a smooth integration may have a big influence on cost control, operational effectiveness, and the overall success of implementing a cloud strategy. Unfortunately, Azure clientele sometimes needs to make investments in specialist knowledge and tools to successfully handle the integration challenges.

Google Cloud Platform (GCP) downsides

Market Perception and Service Stability
One obstacle that GCP encounters is challenging its settled image as a supporting player inside Google, especially given that YouTube and search advertising are the company’s primary sources of income. These facts impact customer confidence in GCP’s long-term commitment to cloud services.

Apart from this, Google’s deprecation policy, which means terminating some products and services, instills uncertainty in the GCP’s potential customers. Maintaining developer-friendly, transparent policies about any changes to any services is what GCP lacks to build confidence and dependability among its customers.

Google can enhance GCP’s reputation by proving a strong commitment through consistent infrastructure investments, open communication about service life cycles, and the development of success stories in key industries. By putting this plan into practice, Google may be able to strengthen stakeholder trust and demonstrate its commitment to GCP as a core business initiative.

Focused Growth and Investment
All the while it is a growing business for Google, GCP still has to manage expenditures wisely to compete with the likes of AWS and Azure. This entails developing new technologies in critical areas like machine learning, analytics, and containerization, as well as growing its data center infrastructure and making strategic acquisitions.

While Google is attempting to balance these expenditures with its main operations, which include YouTube and Search, investing according to its advantages and forming strategic alliances are primary tasks to secure GCP’s role in the cloud market. Capitalizing on its strengths, like AI, machine learning, and data analytics, GCP gets a chance to maintain its competitive standing among the major cloud providers.

Inadequate Documentation and Training Resources
Based on customers’ experience, documentation and training materials provided by Google Cloud Platform (GCP) provide a significant problem. User feedback, particularly ratings on sites like Gartner, frequently highlights issues with GCP’s documentation. Frequently voiced grievances include out-of-date information, excessively complex jargon, and understanding challenges, even for seasoned IT specialists.

This concern extends beyond the mere accessibility of information — it also involves the quality and relevance of the content that is offered. Due to the insufficiency of educational materials, many users have trouble finding answers to simple questions or handling technological problems. This may prevent users from fully utilizing GCP’s whole feature set.