In today’s world, companies and businesses cannot function without cloud computing solutions as these solutions enable virtual storage, computing resources, and much more options. Currently Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure are the top three cloud service providers globally, taking up nearly 65% of the market share.
While each platform comes with its own advantages, it is crucial to compare their features and services to make the right choice.
Overview of AWS, Azure, and Google Cloud
AWS, Google Cloud, and Azure offer similar services and solutions when it comes to virtual computing resources, storage, security, and other cloud solutions. However, their backgrounds and nuances vary, as do their pricing plans. Before we delve deeper into the services provided by these platforms, let us familiarize ourselves with the platforms.
Amazon Web Services (AWS)
AWS was founded in 2006 - it is the biggest cloud infrastructure platform in the world today. The platform offers services and solutions to build applications and manage several other entities. AWS has over 200 cloud solutions, including EC2, S3 Storage, and VPC. Currently, AWS is available in 26 regions and is a pay-as-you-go platform.
Microsoft Azure
Azure is a cloud service provider launched by Microsoft in 2010. The platform provides IaaS, SaaS, and PaaS solutions for data analytics, storage solutions, virtual computing, and a wide range of other solutions. While the platform is similar to AWS in many ways, Azure specializes in services related to Windows-based computing. Azure also utilizes the pay-as-you-go structure for pricing.
Google Cloud Platform (GCP)
Launched in 2011, Google Cloud is a public cloud computing service offering comprehensive services and solutions like storage, networking, big data, and data transfer for professionals across the world. GCP is considered more DevOps-friendly than the other two major platforms. Compared to Azure and AWS, GCP has less number of data centers.
AWS, GCP, and Azure - Comparing Services, Pricing and Advantages
It is impossible to compare the services offered by these platforms to determine which one is better. All three platforms have cloud solutions that help businesses and organizations manage and handle their data effectively. Based on requirements, users should choose their cloud service provider.
Key Services
AWS offers multiple lines of products and solutions, but some of its most popular ones are Amazon EC2 (Elastic Computer Cloud), Amazon S3 (Simple Storage Service), Amazon DynamoDB, Amazon Lambda, and Amazon VPC (Virtual Private Cloud). AWS provides solutions across multiple departments, including storage, management, computing resources, security, etc.
Azure is well known for its DevOps services, some of the best in the market. In addition to the popular DevOps solutions, Azure also offers Virtual Machines, Azure AD (Active Directory), Azure CDN (Content Delivery Network), and Azure API Management. Azure services are best suited for developers working on Windows and Microsoft as they cost less comparatively.
Compared to AWS and Azure, Google Cloud Platform offers only a few services but still covers the main areas of computing, storage, and serverless functions. Some of the GCP services and solutions utilized worldwide by organizations are Google Compute Engine, Google Kubernetes Engine, and Google Cloud Functions.
| Service Category | AWS (Amazon Web Services) | GCP (Google Cloud Platform) | Azure (Microsoft Azure) |
|---|---|---|---|
| Compute | EC2 (Elastic Compute Cloud) | Compute Engine | Virtual Machines |
| Lambda (Serverless Compute) | Cloud Functions | Functions (Serverless Compute) | |
| Elastic Beanstalk | App Engine | App Services | |
| ECS (Elastic Container Service) | Kubernetes Engine | Kubernetes Service | |
| EKS (Elastic Kubernetes Service) | Anthos | Azure Container Instances | |
| Storage | S3 (Simple Storage Service) | Cloud Storage | Blob Storage |
| EBS (Elastic Block Store) | Persistent Disk | Disk Storage | |
| Glacier | Nearline, Coldline, Archive Storage | Archive Storage | |
| Databases | RDS (Relational Database Service) | Cloud SQL | SQL Database |
| DynamoDB (NoSQL) | Firestore, Bigtable | Cosmos DB | |
| Aurora | Spanner | Azure Database for MySQL/PostgreSQL | |
| Networking | VPC (Virtual Private Cloud) | Virtual Private Cloud (VPC) | Virtual Network |
| CloudFront (CDN) | Cloud CDN | Azure CDN | |
| Route 53 (DNS) | Cloud DNS | Azure DNS | |
| API Gateway | API Gateway | API Management | |
| AI/ML | SageMaker | AI Platform | Azure Machine Learning |
| Rekognition (Image and Video Analysis) | Vision AI | Computer Vision | |
| Comprehend (NLP) | Natural Language API | Text Analytics | |
| Analytics | Redshift | BigQuery | Synapse Analytics |
| EMR (Elastic MapReduce) | Dataproc | HDInsight | |
| Kinesis | Pub/Sub | Event Hubs | |
| Security | IAM (Identity and Access Management) | IAM (Identity and Access Management) | Active Directory |
| Shield (DDoS Protection) | Cloud Armor | DDoS Protection | |
| GuardDuty (Threat Detection) | Security Command Center | Security Center | |
| Developer Tools | CodeBuild | Cloud Build | Azure DevOps |
| CodeDeploy | Cloud Deployment Manager | Azure Pipelines | |
| CodePipeline | Cloud Source Repositories | Azure Repos | |
| Monitoring | CloudWatch | Stackdriver | Monitor |
| X-Ray (Tracing) | Trace | Application Insights | |
| CloudTrail (Audit Logging) | Audit Logging | Log Analytics | |
| IoT | AWS IoT Core | IoT Core | Azure IoT Hub |
| Greengrass | Edge TPU | Azure IoT Edge | |
| Migration & Transfer | DMS (Database Migration Service) | Database Migration Service | Azure Migrate |
| Snowball (Data Transfer) | Transfer Appliance | Data Box | |
| Migration Hub | Migration Center | Migration Center |
Pricing
One of the crucial factors to consider while deciding on a cloud service provider is their pricing plans. Here we have summarized the pricing and plans offered by AWS, Azure, and GCP.
AWS
- AWS adapts a pay-as-you-go pricing, meaning you pay only for the resources you use.
- AWS offers an array of practical tools that allow you to predict workloads and corresponding costs.
- If we take the example of instances, AWS charges you around $69 monthly for the most minor instances. This price is determined for a primary instance that comprises two virtual CPUs and 8 GB RAM.
Azure
- Similar to AWS, Azure also offers a pay-as-you-go structure.
- The most minor instance in Azure, for a two CPU, 8 GB RAM setup, will cost you around $70 per month.
- Like AWS, you can find multiple tools that help you calculate the predicted cost for your cloud services usage. You can also avail of discounts for reserved instances.
GCP
- Google Cloud Platform is based on the pay-as-you-go structure as well. While AWS and Azure are pay-per-minute models, GCP is a pay-per-second model.
- For the same setup in the example, GCP only costs around $52 per month.
- You can avail yourself of discounts with GCP depending on your workload and the type of instances you use.
| Service Category | AWS | GCP | Azure |
|---|---|---|---|
| Compute | |||
| On-Demand VM | EC2: $0.0116/hour (t2.micro) | Compute Engine: $0.0100/hour (f1-micro) | Virtual Machines: $0.0098/hour (B1S) |
| Reserved VM | EC2 Reserved: Up to 75% off | Committed Use: Up to 57% off | Reserved VM: Up to 72% off |
| Spot Instances | EC2 Spot: Up to 90% off | Preemptible VMs: Up to 79% off | Spot VM: Up to 90% off |
| Storage | |||
| Object Storage | S3: $0.023/GB (Standard) | Cloud Storage: $0.020/GB (Standard) | Blob Storage: $0.0184/GB (Hot) |
| Block Storage | EBS: $0.10/GB (General Purpose SSD) | Persistent Disk: $0.04/GB (Standard) | Disk Storage: $0.05/GB (Premium SSD) |
| Archive Storage | Glacier: $0.004/GB | Coldline: $0.004/GB | Archive Storage: $0.00099/GB |
| Databases | |||
| Managed SQL DB | RDS: $0.017/hour (db.t3.micro) | Cloud SQL: $0.015/hour (db-f1-micro) | SQL Database: $0.0001/vCore/hour |
| NoSQL DB | DynamoDB: $1.25/GB | Firestore: $0.18/GB | Cosmos DB: $0.008/hour |
| Networking | |||
| CDN | CloudFront: $0.085/GB (first 10 TB) | Cloud CDN: $0.08/GB (first 10 TB) | Azure CDN: $0.073/GB (first 10 TB) |
| Load Balancer | ELB: $0.0225/hour | Load Balancing: $0.025/hour | Load Balancer: $0.0225/hour |
| AI/ML | |||
| ML Platform | SageMaker: $0.0464/hour (ml.t2.medium) | AI Platform: $0.049/hour (n1-standard-4) | Machine Learning: $0.008/hour (Basic) |
| Analytics | |||
| Data Warehouse | Redshift: $0.25/hour (dc2.large) | BigQuery: $0.02/GB (storage) + $5/TB (query) | Synapse Analytics: $0.014/hour (DW100c) |
| Monitoring | |||
| Monitoring | CloudWatch: $0.01/1000 metrics | Stackdriver: $0.30/1000 metrics | Monitor: $0.0023/metric |
| IoT | |||
| IoT Core | AWS IoT Core: $0.0084/1M messages | IoT Core: $0.0045/1M messages | IoT Hub: $0.008/1M messages |
Calculate Pricing as per your need
Real-world Scenarios Showcasing How AWS, GCP, and Azure
| Use Case | AWS (Amazon Web Services) | GCP (Google Cloud Platform) | Azure (Microsoft Azure) |
|---|---|---|---|
| E-commerce Platform | Shopify: Uses AWS for scalable infrastructure, S3 for storage, and RDS for database needs. | Etsy: Uses GCP for big data analytics with BigQuery and Compute Engine for scaling their infrastructure. | ASOS: Utilizes Azure for its cloud infrastructure, SQL Database, and AI capabilities to enhance customer experience. |
| Media Streaming | Netflix: Relies on AWS for massive scale, EC2 for computing, S3 for storage, and RDS for databases. | Spotify: Leverages GCP for its data analytics capabilities with BigQuery and global scaling with Compute Engine. | BBC: Uses Azure for its streaming services, Azure Media Services, and CDN for content delivery. |
| Financial Services | Capital One: Utilizes AWS for secure, compliant infrastructure, Lambda for serverless, and S3 for storage. | HSBC: Uses GCP for data analytics with BigQuery and secure cloud infrastructure. | Lloyds Banking Group: Uses Azure for its cloud services, including SQL Database and Machine Learning. |
| Healthcare | Philips Healthcare: Uses AWS for data storage with S3, analytics with Redshift, and machine learning with SageMaker. | Mayo Clinic: Leverages GCP’s Cloud Healthcare API and BigQuery for advanced analytics. | Novartis: Utilizes Azure for drug development analytics, using Azure Machine Learning and Data Lake. |
| Gaming | Epic Games: Uses AWS for global game server infrastructure, EC2 for compute, and S3 for storage. | Riot Games: Uses GCP’s Kubernetes Engine and Bigtable for scaling and real-time analytics. | Minecraft: Hosted on Azure, utilizing Virtual Machines, Cosmos DB, and PlayFab for backend services. |
| Retail | Amazon: Uses AWS for its e-commerce infrastructure, including EC2, S3, and DynamoDB. | Target: Uses GCP for data analytics, machine learning with AI Platform, and global scaling with Compute Engine. | Walmart: Utilizes Azure for its global retail operations, including SQL Database and Machine Learning. |
| Logistics | DHL: Uses AWS for logistics management, EC2 for compute, and S3 for data storage. | UPS: Leverages GCP for data analytics with BigQuery and AI capabilities for route optimization. | Maersk: Uses Azure for supply chain management, IoT Hub for tracking, and Data Lake for analytics. |
| Automotive | Toyota: Uses AWS for connected car services, IoT, and machine learning with SageMaker. | Renault: Uses GCP for data analytics, machine learning, and scalable infrastructure. | Volkswagen: Utilizes Azure for its Automotive Cloud, including IoT, machine learning, and AI services. |
| Smart Cities | City of Los Angeles: Uses AWS for smart city infrastructure, including IoT and data analytics. | City of Chicago: Leverages GCP for data analytics with BigQuery and smart city applications. | City of London: Uses Azure for smart city initiatives, including IoT Hub and AI capabilities. |
| Education | Coursera: Uses AWS for scalable online learning platforms, EC2 for compute, and S3 for content storage. | Khan Academy: Uses GCP for data analytics, machine learning, and scalable infrastructure. | Pearson: Utilizes Azure for its educational content delivery, machine learning, and data storage. |
| Travel & Hospitality | Expedia: Uses AWS for its travel booking infrastructure, including EC2, S3, and RDS. | Airbnb: Uses GCP for data analytics, machine learning with AI Platform, and scalable infrastructure. | Marriott: Utilizes Azure for its hospitality management, including Virtual Machines and SQL Database. |
Advantages of Different Cloud Service Providers
This section explores the advantages and benefits of AWS, Azure, and Google Cloud. Each platform comes with its nuances, thus making it perfect for a set of users.
AWS
- Offers a wide array of services, from computing to storage to AI and ML.
- Since you pay only for the services you use, AWS is comparatively affordable.
- As AWS has more data centers worldwide, data storage and access have become more accessible.
Azure
- Azure has structured instance families to adapt to different types of workloads, making it easy for users to select the one that suits their needs.
- Azure's hybrid cloud strategies are beneficial.
- Many of Azure's solutions and services are easy to access and simple to use.
GCP
- GCP is well-known for its quick scalability.
- Workload support is well-distributed, thus saving costs.
- GCP seamlessly works along with the other Google services.
Conclusion
As per market share, the top three cloud service providers are AWS, Azure, and Google Cloud. All three platforms boast their specialties and advantages, especially regarding critical services, accessible data centers, and pricing plans. Based on the workload, payment convenience, services and solutions needed, and ease of access, users can choose the best platform that fits their needs.
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