AWS vs. Azure vs. Google Cloud: A Complete Comparison

Features Comparison

All three cloud platforms provide similar features in one way or another. But let’s compare them to understand their differences in detail.

Compute services

First, let’s look at the compute services of all three cloud providers.

Amazon Web Services (AWS) 

AWS offers multiple instances of AWS Elastic Compute Cloud (EC2). With EC2, we can rent virtual servers, known as instances, to run applications. This way, we can use the computing resource without any upfront hardware cost.

Here are the different types of EC2 instances:

In addition, AWS offers auto-scaling options for EC2 instances, which automatically add or remove instances according to changes in application demands. That’s how we can scale the application up and down anytime.

Beyond EC2, AWS provides other compute services such as AWS Lambda for serverless computing, AWS Fargate for containerized applications, and Amazon ECS/EKS for managing Docker and Kubernetes workloads. These services offer flexibility for different computing needs, from event-driven applications to fully managed container orchestration.

Microsoft Azure

Microsoft Azure provides Azure Virtual Machines, which allows users to choose from multiple operating systems, including Windows, Linux, and others, depending on our workloads.

Some of its key benefits are: 

• Autoscale to as many virtual machines as needed. 
• Leverage Azure Boost custom hardware and an optimized hypervisor to improve performance. 
• Supports high-demanding computational tasks because of its GPU capabilities and high-performance computing (HPC).
• Provide rapid disaster recovery solutions to ensure we stay resilient in case of system failures.

Azure also offers Azure Kubernetes Service (AKS) to help users deploy and manage containerized applications. AKS is built on the open-source Kubernetes platform and provides the following benefits:

• Automate cluster management and network configuration.
• Simplified debugging, automated node maintenance, and CI/CD support through GitHub Actions for AKS.
• Flexibility to deploy on Linux, Windows, and IoT infrastructure with Azure Arc-enabled AKS.

Google Cloud 

Google offers a compute engine called Google Compute Engine and a container orchestration platform, Google Kubernetes Service (GKS).

Its Compute Engine allows users to create Virtual Machines for any workload and run them online on the cloud infrastructure. Conversely, GKE is perfect for users with little to no expertise, as it allows us to run containers by putting them on autopilot. 

Storage and databases

Storage and database services serve different purposes in cloud computing: 

• Storage services are designed for storing and managing raw data, files, and objects but do not provide built-in querying or transaction capabilities.
• Database services, on the other hand, are structured for data organization and allow querying, indexing, and transactional support. These include relational databases, NoSQL databases, and cloud data warehouses for large-scale analytics.

Let’s explore the storage and database offerings of each cloud platform.

Amazon Web Services (AWS)

AWS offers three main storage and database options:

• Amazon S3 (Simple Storage Service) is an object-storage service that stores objects in buckets (containers to store the objects). We can use it to build data lakes, keep backups, and restore important data.
• Amazon EBS (Elastic Block Store) is a block-storage service for Amazon EC2 that can run on SSD and HDD-based volume types. It can be used to build I/O-intensive applications and resize clusters for big data analytics engines.
• Amazon RDS (Relational Database Service) is a managed relational database service that supports PostgreSQL, MySQL, MariaDB, SQL Server, and Oracle.
• Amazon DynamoDB is a serverless NoSQL database designed for low-latency, high-scale applications like gaming, IoT, and real-time analytics.
• Amazon Redshift is a cloud data warehouse optimized for large-scale analytics and business intelligence (BI) workloads.

Microsoft Azure

Microsoft Azure also provides three primary services:

• Azure Blob Storage can store and access unstructured data like images and videos. It helps create data lakes and scales flexibly for high-performance computing and ML workloads. We can use it for mobile, web, and cloud-native applications as it supports popular development frameworks like .NET, Python, Java, and Node.js. 
• Azure Disk Storage is a high-performance block storage service designed for Azure Virtual Machines (VMs). It can be used for input/output-intensive applications such as SAP HANA and Enterprise production workloads like SQL Server and NoSQL.  
• Azure SQL Database allows storing structured data that requires relational operations. It features a data API builder that turns the database objects into GraphQL APIs and Dev Container templates that start coding with preconfigured environments.
• Azure Cosmos DB is a globally distributed NoSQL database optimized for real-time applications and AI-driven workloads.
• Azure Synapse Analytics is a cloud data warehouse designed for big data analytics and business intelligence (BI). It offers seamless integration with Power BI and machine learning models.

Google Cloud

Google Cloud also provides three main options:

• Google Cloud storage is a fully managed object storage service that automatically optimizes costs using features like Object Lifecycle Management and Autoclass.
• Google Persistent Disk is a block storage solution. Like Azure Disk storage, it provides block storage for virtual machine instances. It also automatically encrypts data during transfer. We can use SSD and HDD and scale up or down per the application demands. 
• Google BigQuery is a serverless cloud data warehouse designed for high-performance analytics and real-time querying. It is often used for AI, ML, and business intelligence.
• Cloud Spanner is a fully managed relational database that combines SQL consistency with NoSQL scalability, which is ideal for global applications with high availability requirements.
• Firestore is a serverless NoSQL document database optimized for mobile, web, and real-time applications.

Network and content delivery

Cloud providers offer network and content delivery services to improve scalability, reliability, security, and performance.

Amazon Web Services (AWS)

Amazon provides the following networking and content delivery services: 

• Virtual Private Cloud (VPC) allows the launching of AWS resources in an isolated virtual network. It provides more control over your environment, allowing you to customize virtual networks, such as choosing your IP address range and configuring route tables.
• Direct Connect establishes a dedicated network connected to AWS that provides the shortest path to AWS resources. It improves application performance and secures in-transit data. The data in transit never touches the public internet and only remains on the AWS global network. This ensures we can transfer large amounts of data smoothly and reliably.
• CloudFront securely delivers content with low latency and high transfer speeds. Globally, Amazon has over 600 Points of Presence (PoPs) with automated network mapping, which reduces latency. This way, it delivers the data to viewers across the globe within milliseconds with built-in data compression and field-level encryption.

Microsoft Azure

Azure also offers three networking and CDN services:

• Virtual Network allows us to build a private network in the cloud. It facilitates communication of Azure resources with the internet, communication between Azure resources such as VMs, and communication with on-premises resources such as VPNs. It also filters network traffic using security groups or virtual appliances.
• ExpressRoute creates private connections between Azure data centers and infrastructure on-premises or in a colocation environment. This allows you to securely connect to Azure without using the public internet and perform faster and more reliable data transfers.
• Azure CDN brings the content closer to users and sends less traffic to the origin point. This reduces latency and offers superior online experiences.

Google Cloud

Like AWS and Azure, Google also provides three networking and CDN services, which are:

• Virtual Private Cloud (VPC) allows automatic and manual configuration of virtual topologies, including subnet ranges and network policies. It can also expand CIDR ranges without downtime. 
• Cloud Interconnect transfers data between Google VPC and other networks with low-latency and high-availability connections. It provides internal IP addresses that are accessible from both networks. 
• Cloud Content Delivery Network (Cloud CDN) accelerates web applications using Google’s global network and supports any origin, including Compute Engine, Cloud Storage, and GKE backends.

Machine learning and artificial intelligence

AI and machine learning are revolutionizing industries by enabling predictive analytics, automation, and smarter decision-making. As these technologies become increasingly important, let’s explore how AWS, Azure, and Google Cloud provide AI/ML solutions.

Amazon Web Services (AWS)

AWS offers Amazon SageMaker, which unifies access to all data stored in data lakes, warehouses, or other data sources. It has a generative AI assistant that you can use to build, train, and deploy ML models.

AWS also offers pre-built AI services, such as:

• Amazon Bedrock – A fully managed service for deploying foundation models (FMs) from AI leaders like Anthropic, Meta, and Stability AI.
• Amazon Rekognition – A powerful image and video analysis tool.
• Amazon Polly and Amazon Lex – AI-powered text-to-speech and conversational AI services.

Microsoft Azure

Azure offers Azure AI Services to build AI applications with customized APIs and models. These services give you access to industry-leading AI models, including those from Microsoft, OpenAI, and Meta.

Other key AI/ML services include:

• Azure AI Foundry – A unified toolkit for accessing AI models and services.
• Azure Machine Learning – A platform for building, training, and managing ML models at scale.
• Azure OpenAI Service – Provides API access to OpenAI’s GPT models for natural language processing and generative AI.

Google Cloud

Google Cloud is a leader in AI and ML. Google AI provides TensorFlow, an open-source framework that makes creating ML models that can run in any environment easy. It also offers Vertex AI, an end-to-end platform for training, deploying, and scaling AI models. 

Other AI services include:

• Cloud AI APIs – Ready-to-use AI models for vision, speech, text, and translation tasks.
• Deep learning VMs and TPUs – Optimized infrastructure for AI workloads, leveraging Google’s custom-built Tensor Processing Units (TPUs).               

Developer Tools

Developer tools help programmers write and debug code efficiently to streamline software development. So, let’s see how each platform supports this.

Amazon Web Services (AWS)

AWS offers three main developer tools:

• AWS CodePipeline automates continuous delivery pipelines, mitigates the need to set up or manage servers, releases new features, and removes bugs to free us from manual tasks.
• AWS CodeBuild builds and tests code with automatic scaling. We don’t have to manage or scale our servers — rather, we just specify the location of our source code and choose suitable build settings. CodeBuild then does the rest for us. 
• Serverless computing, or AWS Lambda, runs the code without thinking about servers or clusters. We can write Lambda functions in several programming languages, including Node.js, Python, Go, and Java.

Microsoft Azure

Microsoft Azure provides the following developer tools:

• Azure DevOps facilitates smarter planning and faster shipping, along with modern development services. We can choose between a complete DevOps solution or only a product that fits our workflow. Overall, it includes Azure Boards for agile planning, Azure Pipelines for CI/CD, Azure Test Plan for testing, GitHub Advanced Security for DevOps, Azure Repos and Azure Artifacts for better collaboration, and Managed DevOps Pools to empower teams. 
• Azure Functions execute event-driven serverless code in the language of your choice. They process data in real time and allow workflow orchestration.
• Github Actions automates software workflows with CI/CD, deploys code from GitHub, and simplifies code reviews and branch management.

Google Cloud

Google Cloud provides the following developer tools:

• Cloud Functions simplifies the developer experience and increases developer velocity. As a developer, you only write code, and Google Cloud handles the operational infrastructure. 
• Cloud Run is a managed platform for building apps and websites. It runs frontend and backend services, batch jobs, host LLMs, and queue processing workloads without any infrastructure management.
• Cloud Build is a serverless CI/CD platform for building, testing, and deploying software. It supports various programming languages, including Java, Go, and Node.js, and can be used to build software and deploy it across multiple environments, such as VMs, Kubernetes, or Firebase.

Pricing and Cost Structure

Now that we have explored the main features of all three cloud platforms, let’s examine their pricing models and cost structures. 

AWS pricing

AWS follows a pay-as-you-go pricing model, which means you only pay for the services you use. It also offers additional cost-saving options:

• Reserved Instances (RIs) – Discounted pricing for instances purchased in advance for 1 or 3 years, assigned to a specific Availability Zone.
• Spot Instances – Offers significant discounts (up to 90% off on-demand prices) for unused capacity, but instances can be interrupted.
• Savings Plans – Flexible pricing model that provides cost savings similar to RIs but allows for more compute flexibility.
• AWS Free Tier – Offers 12 months of free access to 20+ services and an always-free tier for certain low-usage services.

Pricing calculator: You can use AWS’s pricing calculator to calculate pricing per instance type and service.

Microsoft Azure pricing

Microsoft Azure also offers a pay-as-you-go model and offers various cost-saving options:

• Azure Reserved Virtual Machine Instances (Azure RIs) – Prepaid VMs for 1 or 3 years, with a monthly payment option at no extra cost.
• Spot Virtual Machines (Spot VMs) – Discounts are available for spare compute capacity, but instances can be reclaimed when demand increases.
• Azure Hybrid Benefit – Cost savings for Windows Server and SQL Server customers who bring their on-prem licenses to Azure.
• Azure Free Tier – Provides 12 months of free access to over 20 popular services and an always-free tier with 65+ services.

Pricing calculator: Azure provides a unified calculator, allowing you to estimate costs for multiple services in one place.

Google Cloud pricing

Google Cloud also uses a pay-as-you-go model and provides unique cost-saving mechanisms:

• Sustained Use Discounts – Automatic discounts of up to 30% for consistent use of virtual machines.
• Preemptible VMs – Short-lived, discounted VMs (similar to AWS Spot Instances and Azure Spot VMs) terminated when capacity is needed.
• Committed Use Contracts – Discounts of up to 57% for committing to 1 or 3 years of use.
• Google Cloud Free Tier – Includes $300 in free credits for new customers and an always-free tier for various services.

Pricing calculator: Google Cloud provides a detailed pricing calculator with built-in cost optimization tools.

Comparative pricing table

Here is a comparison of the pricing and cost structure of all three providers:

Global Reach and Availability

Global reach refers to the network of resources and data centers that are accessible around the globe, and availability means that the system remains operational even during disruptions or peak traffic. So, let’s examine the global reach and availability of all three providers.

AWS global infrastructure

AWS has an extensive global infrastructure. It has 36 launched regions, 114 Availability Zones, and over 700 CloudFront Points of Presence (PoP) with 13 regional edge caches. It is also planning to build more Availability zones in 4 more regions.

Microsoft Azure global infrastructure

Microsoft Azure has over 60 Azure regions and over 300 data centers in Azure’s global infrastructure. It allows users to store data in their nearest region to reduce latency.

Google Cloud global infrastructure

Google Cloud infrastructure services are available in over 200 countries and territories across 41 regions, 124 zones, and 187 network edge locations. Its global network connects its infrastructure to more than 3.2 million kilometers of terrestrial and subsea fiber. You can use its Google Cloud Region Picker tool to pick a region that considers carbon footprint, price, and latency.

Security and Compliance

Security and compliance are the steps a provider takes to protect user data and systems from unauthorized access and compliance with industry standards.

AWS security

AWS provides a comprehensive security framework with built-in tools for access management, threat detection, and compliance. It offers two primary security services:

• AWS Identity and Access Management (IAM) enables fine-grained access control by allowing administrators to define who can access what resources based on roles and policies.
• AWS Shield protects against Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks to ensure maximum application availability and responsiveness.

In addition, AWS supports 143 security standards and compliance certifications, including:

• HIPAA (Health Insurance Portability and Accountability Act)– For handling healthcare data.
• GDPR (General Data Protection Regulation) – Ensuring data protection and privacy in the EU.
• SOC 1, SOC 2, and SOC 3 reports – Validating internal security controls.

These compliance certifications provide third-party validation for thousands of global security requirements, reducing your operational burden.

Microsoft Azure security

Azure offers robust security tools for identity management, threat protection, and compliance.

• Microsoft Entra ID safeguards organizations with cloud identity and access management solutions. It provides:
• A central location to manage all identities.
• Risk-based conditional access policies.
• Strong authentication to protect data and resources.
• Microsoft Defender for Cloud – A cloud security posture management (CSPM) tool that:
• Provides real-time security insights into hybrid and multi-cloud environments.
• Detects threats and vulnerabilities to reduce exposure to attacks.

Azure also has over 100 compliance certifications, including region-specific certifications, and 35 industry-specific compliance certifications, including health and finance.

Google Cloud security

Google Cloud offers a trusted cloud infrastructure that builds security through several layers. Their team is available 24/7 to respond to infrastructure threats, providing continuous monitoring and rapid response. This secure infrastructure covers:

• Secure deployment of services.
• Safe storage of data.
• Encrypted communication between services and over the Internet.

To strengthen security further, Google Cloud provides Identity and Access Management (IAM) and encryption options, including:

• Encryption at rest: Google encrypts all customer content automatically without requiring the user to take any action. The Cloud Key Management Service also enables users to create and manage their own encryption keys for additional security.
• Encryption in transit: Google encrypts data in transit when it moves outside physical boundaries that Google does not control.

Google Cloud also complies with major security certifications, including ISO 27001, HIPAA, and PCI DSS, ensuring data protection and regulatory compliance.

Security and compliance comparison table

Support and Ecosystem

Now, let’s look at the support and ecosystem of all three cloud platforms.

AWS support

AWS offers extensive documentation to support users, covering everything from user guides and code samples to SDKs, toolkits, API, and CLI references. It also provides hands-on tutorials, expert-written blog posts, reviewed answers, articles, and videos to ensure we have access to the best resources.

AWS offers premium support plans for developers, businesses, and enterprises for more personalized support. These plans vary based on case severity, architectural guidance, proactive programs, self-service options, technical account management, billing assistance, and third-party software support, among other factors.

Beyond technical resources, AWS builds a strong Community that offers opportunities to connect regionally and participate in events like AWS Hackday. These community activities help us network with like-minded professionals, share ideas, and collaborate on innovative cloud solutions.

Microsoft Azure support

Azure provides Developer, Standard, and Enterprise Service support plans, which differ based on scope, ICP support, billing, and technical assistance. These plans include:

• Documentation and online self-help
• A community forum on MSDN
• Best practice recommendations
• Access to a personalized service health dashboard

In addition to support services, Microsoft Azure offers integration services to build solutions that connect applications and services both on-premises and in the cloud.

Google Cloud support

Google Cloud Customer Care is a part of Google Cloud Services that streamlines cloud support by providing access to documentation, community support, billing support, and Active Assist recommendations.

It offers multiple support packages, including Standard Support, Enhanced Support, and Premium Support, which differ based on:

• Pricing and response times
• Service availability and support language
• Technical and third-party technology support

In addition, support offerings include 24/7 coverage, phone support, and access to technical account services for better customer experience. We can even use its Integration Connectors to connect to various data sources and applications without requiring protocol-specific knowledge.

Strengths and Weaknesses of AWS, Azure, and Google Cloud

Each cloud provider has unique strengths that make it ideal for different use cases. However, they also come with challenges that may not suit every business. Let's break down their key advantages and potential drawbacks.

Strengths of AWS

AWS has been in the market the longest, and some of its key strengths are the following: 

• They've built up an impressive collection of tools and services. 
• It has a huge community of developers using AWS, so you can easily find help when needed. 
• Their flexibility is great, too—whether you're a small startup or a huge corporation, AWS can scale to meet your needs.

Strengths of Azure

Azure is designed with enterprises in mind, making it the preferred choice for businesses that already use Microsoft products:

• It has a strong enterprise focus, which makes it a preferred choice for large organizations. 
• It offers hybrid cloud support to allow integration with on-premises resources.
• It offers native support for Windows, .NET, SQL Server, and Microsoft 365, benefiting organizations already using Microsoft technologies.

Strengths of Google Cloud

Google Cloud is a leader in AI, machine learning, and high-performance computing, making it ideal for data-driven use cases:

• It provides cutting-edge AI/ML tools like Vertex AI, TensorFlow, and Gemini.
• It also provides high-performance computing capabilities through Google Compute Engine.
• It provides access to a superior network infrastructure with a global network and content delivery capabilities.

Weaknesses of AWS, Azure, and Google Cloud

Each cloud provider has its own weaknesses:

• AWS: Pricing complexity is a common criticism, as understanding different pricing models and calculating costs can be challenging.
• Azure: The platform has a steep learning curve, making it difficult for new users to adapt quickly.
• Google Cloud: Compared to AWS and Azure, Google Cloud has fewer enterprise customers, which may raise concerns about community support and industry adoption.

Choosing the Right Cloud Provider for Your Needs

Choosing the right cloud provider depends on your application’s needs, such as the size, budget constraints, and the resources required. Here’s a quick comparison to help you decide:

Conclusion

The best cloud provider depends on your specific project requirements, existing technology stack, and expertise. If you're looking to deepen your knowledge of cloud computing and gain hands-on experience, check out these beginner-friendly learning tracks:

• AWS Cloud Practitioner (CLF-C02) – Ideal for understanding AWS fundamentals and preparing for certification.
• Microsoft Azure Fundamentals (AZ-900) – A great starting point for learning Azure and its cloud solutions.
• Introduction to Google Cloud (GCP) – Perfect for exploring Google Cloud's core services, including data analytics and AI.

No matter which cloud platform you choose, understanding its strengths, pricing structure, and use cases will help you make an informed decision that aligns with your business and technical needs!