What Are Cloud Computing and Cloud Platforms?
On-Demand availability of computer system resources, especially data storage, computing power, and high-performance computing, is known as Cloud Computing.
Cloud platforms are the platforms that provide users with the power of Cloud Computing. There are several cloud platforms available these days, which are also in demand, namely AWS (Amazon Web Services), GCP (Google Cloud Platform), Microsoft Azure, IBM Cloud (formerly IBM Bluemix), and Alibaba Cloud.
Cloud platforms aim at providing shared infrastructure. This increases resource utilization, reduces resource idle time, and thereby reduces the resource cost proportionately. Less idle time of a resource means more sharing and shared cost of the resource.
Major Cloud Platforms in the Industry
With the primary aim of providing IaaS (Infrastructure as a Service), AWS Cloud Platform was started in the year 2006, and later expanded the services to PaaS (Platform as a Service) and SaaS (Software as a Service). They were later followed by Google, which founded GCP in 2008, and Microsoft, founding Azure in 2010.
As of today, cloud platforms provide a wide range of services apart from the 3 mentioned above, such as MBaaS (Mobile Backend as a Service), Serverless computing, and Function as a Service (FaaS), all aiming to provide cost-effective, efficient, on-demand, and easy scaling experiences to users.
Availability Zones and Regions
The cloud infrastructure is available based on the geographical region of the user and several zones under those regions. They are basically data centers for data storage and network connections for accessing applications.
- AWS operates multiple Availability Zones across many geographic regions worldwide, with continued expansion into new regions and zones.
- GCP is available across multiple regions and availability zones worldwide.
- Azure is available in many regions with data centers all around the world. As promised by Microsoft, Azure aims to make it easy to choose the right data center in the right geographical region according to the targeted customer due to increasing concerns about data privacy.
Pros and Cons of the Different Cloud Platforms
AWS has dominance in cloud platforms with more than 15 years in the industry and a massive scope of operations, whereas Azure holds its position in providing strong integrated support for Microsoft and Windows-based software applications like Windows Server, Office, SQL Server, SharePoint, Dynamics, Active Directory, .NET, and others.
On the other hand, GCP proves its dominance in scenarios where strong containerization of applications is needed. Google originally created Kubernetes, which is also offered by Azure and AWS as part of their services. Kubernetes is now maintained by the Cloud Native Computing Foundation (CNCF). It is an open-source platform and a portable, extensible system for containerized applications. It helps in managing containerized workloads and services and facilitates both declarative configuration and automation.
Azure is considered an enterprise-ready cloud platform due to its ability to support large-scale Windows and Microsoft applications, whereas GCP is often chosen for cloud-native businesses as it provides strong support for analytics and big data. GCP also offers high-performance computing services that are well suited for machine learning and other domains requiring high computational power.
AWS has a great ecosystem, strong customer support, and a wide set of learning resources due to its popularity and maturity in the industry. AWS also has very mature solutions in terms of technological challenges one faces while using cloud platforms due to its long presence in the market.
Pricing Models by Cloud Platforms
All cloud platforms mainly provide services like storage, computation, and databases. But they differ in pricing and types of services.
- Cloud platforms provide a range of pricing models, such as pay-as-you-go, which ensures flexible billing and efficient resource utilization. This approach is also known as on-demand pricing and ensures that customers only pay for what they use.
- AWS instances can be purchased in models such as on-demand, reserved, and spot.
- Azure instances can be purchased as on-demand, reserved, and spot (short-term capacity).
- For Google Cloud, pricing models include on-demand usage, committed use discounts, and spot VMs. Historically, GCP also offered sustained use discounts, which have been reduced in importance over time.
AWS, Azure and GCP Services
| Cloud Platform | Storage Solutions | Database Support | Computer Engine |
| AWS | • Simple Storage Service (S3) • Elastic Block Storage (EBS) • Elastic File System (EFS) • Storage Gateway • Snowball • Snowball Edge • Snowmobile | • Aurora • RDS • DynamoDB • ElastiCache • Redshift • Neptune • AWS Database Migration Service (DMS) | • EC2 • Elastic Container Service • Amazon Elastic Kubernetes Service (EKS) • Amazon Elastic Container Registry (ECR) • Lightsail • Batch • Elastic Beanstalk • Fargate • Auto Scaling • Elastic Load Balancing • VMware Cloud on AWS |
| Azure | • Blob Storage • Queue Storage • File Storage • Disk Storage • Azure Data Lake Storage | • SQL Database • Database for MySQL • Database for PostgreSQL • Azure Synapse Analytics • Cosmos DB • Azure Table Storage • Redis Cache • Data Factory | • Virtual Machines • Virtual Machine Scale Sets • Azure Container Service (AKS) • Container Instances • Batch • Service Fabric |
| GCP | • Cloud Storage • Persistent Disk • Transfer Appliance • Transfer Service | • Cloud SQL • Cloud Bigtable • Cloud Spanner • Firestore | • Compute Engine • Google Kubernetes Engine (GKE) • Cloud Functions • Graphics Processing Unit (GPU) • App Engine |
Technical Differences Between AWS, Azure and GCP
| Technical Aspect | AWS | AZURE | GCP |
| Maximum no of processors | 128+ | 128+ | 96+ |
| Managed Data Warehouse | Amazon Redshift | Azure Synapse Analytics | Big Query |
| Kubernetes | Amazon Elastic Kubernetes Service (EKS) | Azure Kubernetes Service (AKS) | AEFS |
| File Storage | Amazon EFS | Azure Files | Filestore |
| Serverless Functions | AWS Lambda | Azure Functions | Cloud Functions |
| Analytics Service | Amazon Kinesis | Azure Stream Analytics | Dataflow |
| Search Service | Amazon OpenSearch Service | Azure AI Search | Google Cloud Search; Vertex AI Search |
Summary: AWS vs. Azure vs. GCP
On one hand, Azure is considered enterprise-ready due to its wide range of geographical regions and strong integration with Windows and Microsoft applications, while also supporting regional data privacy requirements. On the other hand, AWS has a massive global reach and a highly mature service ecosystem. AWS offers a broad portfolio of cloud services and extensive operational experience, whereas GCP is particularly strong in high-performance computing, analytics, and cloud-native technologies. AWS also has a huge community and a large amount of learning resources available due to its popularity and maturity in the market, while Azure has significantly improved its documentation and developer ecosystem over the years.
Therefore, choosing a cloud platform ultimately depends on the requirements of the application and the business needs of the organization.