Choosing a cloud platform is not an easy task, especially when there are giants like AWS, Google Cloud, and Azure. But how do they differ and which solution is right for you?
Please note that the choice between AWS, Google Cloud, and Azure depends on the specific needs of the project and the company’s infrastructure.
What are cloud platforms?
Cloud platforms are services that provide computing resources, data storage, databases, networking, and other IT services over the Internet. These platforms allow companies and developers to build, manage, and scale their applications and data without having to invest in their own hardware.
What’s the difference between AWS, Google Cloud, and Azure?
Each platform has its own features. AWS is famous for its reliability, GCP for its speed in data analysis, and Azure for its flexibility in hybrid solutions. Let’s take a closer look.
AWS (Amazon Web Services) is, so to speak, an old-timer among clouds. They have an incredibly wide range of services and an extensive network of data centers around the world. If you need powerful tools for big data, machine learning, or DevOps, AWS won’t disappoint you.
The platform offers virtual servers (EC2), data storage (S3), databases (RDS), and other tools for developing, deploying, and managing applications.
With EC2, you get full control over server configuration, can choose the required capacity, and scale resources as the load grows. In essence, it is renting powerful hardware without having to buy it.
S3 is an infinite data storage. Upload files of any size and type there, and they will be available anywhere in the world. S3 provides high reliability and security of storage, and also offers convenient tools for data management.
Don’t want to bother with setting up and maintaining databases? You can choose popular engines like MySQL, PostgreSQL, or Oracle and quickly deploy a managed database. AWS will take care of backups, updates, and scaling.
Lambda is another AWS service that allows you to run code without dedicated servers. Upload a function, and AWS automatically executes it in response to events. It is an ideal choice for microservices and event-driven architectures.
AWS is especially popular among startups and large enterprises due to its flexibility and wide range of services.
Google Cloud is a powerful and flexible platform that offers a wide range of tools and services for developing, deploying, and managing applications. If you want to use cutting-edge technologies and integration with Google products, this platform is a great choice.
Google was one of the founders of Kubernetes, so it is no surprise that their service works so well. GKE automates the deployment, management, and scaling of containerized applications, allowing you to focus on development.
App Engine is a platform as a service (PaaS) that allows you to develop and deploy applications without having to manage server infrastructure. For data storage, Google Cloud offers Cloud Storage. Cloud Storage supports several storage classes, allowing you to choose an option that suits your budget. BigQuery is a powerful analytics service for processing big data. It allows you to quickly run SQL queries on huge data sets and get results in seconds. BigQuery is ideal for analytics, business intelligence, and machine learning.
Why Google Cloud?
- The platform integrates well with other Google products, making it easier to work with data and analytics.
- Google Cloud services are easy to set up and manage, reducing the time it takes to deploy and operate applications.
- Many tools for ML development.
Microsoft Azure is the best friend of all Microsoft users. Here you will find Windows Server, Active Directory, and SQL Server. Azure is great for enterprise customers and hybrid clouds, offering a wide range of solutions for developers and DevOps engineers.
Azure Virtual Machines provide virtual servers with complete freedom of configuration. You can choose the operating system, configuration, and power you need. Azure VMs are easy to scale and provide high performance for any task – from web applications to complex computing projects. Azure App Service is a platform as a service (PaaS) that allows you to quickly create, deploy, and scale web applications and APIs. Various programming languages are supported, including .NET, Java, PHP, Node.js, and Python.
Azure SQL Database is a fully managed relational database with support for automatic updates, backups, and scaling. It is an ideal choice for applications that require high performance and data security. Azure SQL Database maintains compatibility with SQL Server, which makes migration and integration easier.
Azure Active Directory is a cloud-based identity and access management service that provides single sign-on (SSO), multi-factor authentication, and application access control. Azure AD provides security and ease of management for users and groups across cloud and on-premises applications.
Most popular cloud platform in 2024
The most popular cloud in 2024 is AWS. AWS entered the market in 2006, which gave it a significant advantage due to early access to cloud technologies. This allowed the company to accumulate extensive experience and build a large-scale, reliable infrastructure, guaranteeing sky-high quality and stability of its services.
Secondly, AWS offers more than 200 different services, covering all kinds of business and developer needs. Such a wide range of solutions allows customers to find everything they need in one place, making AWS a universal choice.
In addition, AWS has the world’s largest network of data centers, which covers many regions and availability zones. Global presence ensures low latency and high availability for users around the world. The presence of such data centers makes it easy to scale solutions and support business operations in different countries – and international businesses are only growing.
Another significant reason for the popularity of AWS is its constant pursuit of innovation. Amazon invests heavily in developing new technologies and improving existing services, regularly announcing hundreds of new features and services.
AWS offers flexible pricing models, including pay-as-you-go, upfront payment, and discounts for long-term use.
AWS has a large ecosystem and partner network. The platform supports a huge community of developers, partners, and independent software vendors, creating a vast network of integrations and solutions. This simplifies migration and use of cloud technologies, providing customers with a variety of ready-made solutions.
Cloud tools and technologies
AWS provides a whole set of tools to automate development and deployment processes: CodeCommit for storing code, CodeBuild for automated builds, CodeDeploy for deploying applications, and CodePipeline for organizing CI/CD processes.
CloudFormation allows you to describe your entire infrastructure as templates using JSON or YAML format. This makes setting up and managing resources on AWS predictable and repeatable.
If you prefer to program your infrastructure in a familiar language, CDK provides a more flexible and convenient way to describe your infrastructure. CDK allows you to use programming languages (TypeScript, Python, Java, C#) to define cloud resources.
Google Cloud uses GKE, or Google Kubernetes Engine, to automate updates, monitoring, and autoscaling.
Cloud Build is a fully managed CI/CD service that allows you to automate building, testing, and deploying code. It supports integration with GitHub, GitLab, and Bitbucket repositories, and allows you to create multi-stage pipelines for various tasks.
Deployment Manager allows you to describe infrastructure as code (IaC) using YAML. This tool automates the creation and management of Google Cloud resources, ensuring repeatability and version control.
Cloud Functions is a serverless platform that allows you to run code in response to events. Cloud Run allows you to deploy and manage containerized applications using a pay-as-you-go model. It supports any container – very convenient for deploying microservices.
Azure DevOps is a complete set of tools for managing the development lifecycle. Includes Azure Repos (code repository), Azure Pipelines (CI/CD), Azure Boards (project management), Azure Test Plans (testing), and Azure Artifacts (package management).
Azure Artifacts supports NuGet, npm, Maven, and Python. It enables the management of dependencies and package versions as part of your CI/CD pipelines.
Bicep is a resource deployment language for Azure that simplifies the creation and management of infrastructure as code (IaC). Bicep is an alternative to the complex and verbose Azure Resource Manager (ARM) JSON templates and provides a more readable and simplified way to describe infrastructure.
Cloud Platform Costs and Payment Models
AWS is flexible in pricing, GCP is attractive due to its transparency and competitive rates, and Azure offers favorable conditions for those who already work in the Microsoft ecosystem.
Amazon Web Services (AWS)
AWS offers a variety of pricing plans, including pay-as-you-go, capacity reservations, and long-term contract discounts.
AWS’s 12-month free trial includes a limited number of resources: EC2 (750 hours per month), S3 (5 GB of storage), and RDS (750 hours per month). After that, costs start at regular rates.
Pricing:
- EC2 (t3.micro): About $0.0116 per hour.
- S3 (standard storage): About $0.023 per GB per month.
- RDS (db.t3.micro): About $0.017 per hour.
Google Cloud Platform (GCP)
GCP offers automatic, sustained use discounts, which can reduce costs without the need for upfront contracts. A free tier offers $300 in credit for the first year and a number of free resources – 5 GB of storage in Cloud Storage and 1 GB of storage in Cloud Firestore.
Pricing:
- Compute Engine (e2-micro): About $0.0076 per hour.
- Cloud Storage (standard storage): About $0.020 per GB per month.
- Cloud SQL (db-f1-micro): About $0.018 per hour.
Microsoft Azure
Azure offers pay-as-you-go pricing, capacity reservations, and flexible discounts. Discounts are also available for using Microsoft Software Assurance.
You get 12 months of free use of core functionality, plus 1 GB of BLOB Storage and 250 GB of SQL Database storage perpetually free.
Pricing:
- Virtual Machines (B1S): About $0.008 per hour.
- Blob Storage (standard storage): About $0.0184 per GB per month.
- SQL Database: About $0.016 per hour.
Which cloud platform is the most cost-effective for small businesses?
- GCP has lower prices for virtual capacity, especially with automatic discounts for long-term usage. Azure also offers competitive rates, especially for entry-level users.
- Google Cloud and Azure offer slightly lower storage rates than AWS. If your business is data-centric, it may be worth choosing.
- All three platforms offer free tiers, but GCP provides a larger credit for the first year, which can be beneficial for startups and small businesses.
- AWS and Azure have long-term contract programs that can provide significant discounts if you plan on long-term usage.
How are platforms adapting to new trends and technologies such as AI and machine learning?
In the world of IT, change is a constant reality. New trends emerge every day, and platforms are forced to quickly adapt to stay relevant. It’s by this logic that AI and ML functionality can now be found in every cloud platform.
Amazon Web Services (AWS)
Amazon SageMaker platform allows you to create, train, and deploy machine learning models using a user-friendly interface and built-in algorithms. SageMaker supports popular TensorFlow and PyTorch frameworks, and offers tools for data preparation and model evaluation.
In addition, AWS provides Deep Learning AMIs — virtual machine images specially prepared for deep learning, simplifying the setup of an environment for training models.
Amazon Recognition offers capabilities for analyzing images and videos, including recognizing objects, faces, and text. For natural language processing, AWS provides Amazon Comprehend, which helps extract meaning from texts and analyze sentiment.
We have already mentioned AWS Lambda above. It simplifies integration with AI and ML, allowing you to respond to events and run models faster.
Google Cloud
Google Cloud is actively developing its AI and ML offerings. Google AI Platform provides a set of tools for creating, training, and deploying AI models. The platform supports integration with TensorFlow and other frameworks.
BigQuery ML allows you to create machine learning models using SQL queries. This simplifies working with data and makes model creation accessible to everyone.
AutoML from Google Cloud offers automated solutions for creating and tuning AI models. This platform allows you to create models for processing images, text, and tabular data, even if you do not have deep knowledge of machine learning.
For language and image analysis, similar to AWS, Google Cloud has Google Cloud Natural Language and Google Cloud Vision.
Microsoft Azure
Azure Cognitive Services is a set of APIs that let you add AI features to your applications. These services include image, video, speech, and text analysis. For example, the Computer Vision and Face APIs help you recognize and analyze visual content, while Text Analytics provides features for processing text.
Azure Databricks is a platform for processing big data and building AI models based on Apache Spark. It integrates with other Azure services and makes it easier to work with data. Azure Cognitive Search also adds AI features to search, while Azure Bot Services provides capabilities for creating chatbots and virtual agents.
Conclusions
Take a step towards innovation. AWS, Google Cloud and Azure — each of these platforms is ready to provide you with tools to reach your best. Assess the needs of your project, make an reasonable decision and implement new technologies. Your future is not behind the clouds!