How AWS, Azure, and Google Cloud Platforms Are Different 

Every year, the world generates more data than the year before –  and businesses that cannot store, process, and act on that data at speed fall behind those that can. According to Gartner, worldwide end-user spending on public cloud computing services reached $595.7 billion in 2024 and is forecast to grow 21.5% to $723.4 billion in 2025, driven by AI adoption and enterprise modernisation. This is not a trend –  it is a structural shift in how software is built, deployed, and scaled.

At the centre of this shift are three platforms that together account for roughly 63% of the world’s cloud infrastructure market (Synergy Research Group, Q3 2025): Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Each is mature, enterprise-grade, and capable of supporting everything from a startup MVP to a Fortune 500 core banking system –  but they are not interchangeable. Choosing the wrong platform can mean higher costs, integration headaches, and a cloud application development process that is far more painful than it needs to be.

This guide compares AWS, Azure, and GCP across every dimension that matters –  pricing, global infrastructure, AI tooling, security, and the specific workloads each platform handles best.

What is Cloud Computing?

Cloud computing is the use of various computing-hosted services like servers, storage, databases, networking, and software to access data in real-time over the internet. The cloud technology is used to store limitless data on a physical server and is maintained by a cloud service provider.

What Is Cloud Computing?

• Cloud computing is the delivery of computing resources –  including servers, storage, databases, networking, analytics, and software –  over the internet on a pay-as-you-go basis. Instead of owning and maintaining physical data centres, businesses rent access to technology infrastructure from a cloud provider and pay only for what they use.

  There are three primary service models:

  • IaaS (Infrastructure as a Service) –  raw compute, storage, and networking; you manage the OS and software. Examples: AWS EC2, Azure Virtual Machines, GCP Compute Engine.
  • PaaS (Platform as a Service) –  a managed environment for building and deploying applications. This is the foundation of modern cloud application development. Examples: AWS Elastic Beanstalk, Azure App Service, GCP App Engine.
  • SaaS (Software as a Service) –  fully managed software delivered over the web. Examples: Google Workspace, Microsoft 365, Salesforce.

Key Benefits of Cloud Computing for Businesses

• Scalability on demand –  infrastructure scales up or down in minutes to match actual usage. A retail app can handle Black Friday spikes without pre-provisioning year-round capacity.
• Reduced capital expenditure –  shifting from CapEx (owned hardware) to OpEx (rented cloud services) frees capital for product development. Businesses typically report 30–40% infrastructure cost reductions after a well-planned cloud migration.
• Faster time to market –  with pre-built services for databases, authentication, AI, and more, development teams ship faster. Cloud application development cycles that once took months can compress to weeks.
• Global reach –  all three platforms operate data centres across the globe, enabling businesses to serve users in any region with low latency.
• Enterprise-grade security –  AWS, Azure, and GCP each offer compliance with SOC 2, ISO 27001, GDPR, HIPAA, and PCI DSS –  capabilities most organisations could not replicate on-premise.
• Business continuity –  built-in redundancy, automated backups, and multi-region failover enable 99.99% uptime SLAs that on-premise infrastructure rarely matches.

AWS: Overview, Strengths & Best Use Cases

What is AWS?

Amazon Web Services launched in 2006 as the world’s first major public cloud and remains the global market leader with approximately 29% of cloud infrastructure revenue as of Q3 2025 (Synergy Research Group). It now offers more than 200 distinct services and operates 39 launched regions with 123+ Availability Zones globally –  the largest physical footprint of any cloud provider.

Notable AWS customers: Netflix, Airbnb, NASA, Pfizer, Unilever, Airtel, HDFC Life, SonyLIV.

Strengths of AWS

• Unmatched service breadth –  200+ services covering compute, storage, ML, IoT, security, media, and developer tools.
• Mature AI and ML tooling –  Amazon SageMaker provides an end-to-end environment for building, training, and deploying machine learning models. Amazon Bedrock gives access to leading foundation models via a single API.
• Largest marketplace and partner ecosystem –  AWS Marketplace lists over 30,000 software and service offerings. The AWS Partner Network (APN) includes 130,000+ partners across 200+ countries.
• Proven at every scale –  auto-scaling, load balancing, and CloudFront CDN are battle-tested at a scale matched by no competitor.
• Flexible pricing –  EC2 billed per second (60-second minimum). Reserved Instances and Savings Plans can reduce costs by up to 72% vs. on-demand rates.

Best Use Cases for AWS

• Scalable web and mobile application hosting
• Big data processing and analytics (EMR, Redshift, Athena)
• Machine learning model training and deployment (SageMaker, Bedrock)
• Serverless architectures (Lambda, API Gateway, DynamoDB)
• IoT application connectivity (AWS IoT Core)
• Disaster recovery and backup (AWS Backup, S3)

Microsoft Azure: Overview, Strengths & Best Use Cases

What is Azure?

Microsoft Azure launched in 2010 and is the second-largest cloud platform globally, holding approximately 20% market share as of Q3 2025 (Synergy Research Group). Azure differentiates through deep integration with Microsoft’s enterprise software ecosystem and now operates across more than 70 regions worldwide –  more announced regions than any other cloud provider, with particularly strong coverage in Europe, government sectors, and regulated industries.

Notable Azure customers: Boeing, Bosch, Audi, HSBC, Starbucks, Walgreens, FedEx, Walmart, 3M, Mitsubishi Electric.

Strengths of Azure

• Unrivalled Microsoft ecosystem integration –  Azure Active Directory (Entra ID), Azure SQL Managed Instance, and Azure Arc make it the path of least resistance for organisations running Active Directory, Office 365, Teams, or Dynamics 365.
• Hybrid cloud leadership –  Azure Stack Hub and Azure Arc allow businesses to run Azure services in their own data centres. For enterprises with on-premise requirements, Azure’s hybrid posture is often decisive.
• Strong compliance posture –  Azure Government and Azure for Sovereignty address the strictest public-sector requirements. Azure holds more compliance certifications across more regions than any other provider.
• AI and Cognitive Services –  Azure OpenAI Service provides enterprise-grade access to GPT-4o, DALL·E, and other OpenAI models within Azure’s security perimeter.
• DevOps-first tooling –  Azure DevOps (Boards, Repos, Pipelines, Artifacts) combined with GitHub (owned by Microsoft) forms the most integrated developer workflow of the three platforms.

Best Use Cases for Azure

• Enterprise applications with Microsoft stack dependencies (ERP, CRM, SAP)
• Hybrid cloud and edge computing deployments
• Government, healthcare, and financial services with strict compliance requirements
• Windows-based workload migrations (lift-and-shift)
• AI integration via Azure OpenAI (GPT-4o, DALL·E)
• CI/CD pipelines (Azure DevOps + GitHub Actions)

Google Cloud Platform: Overview, Strengths & Best Use Cases

What is Google Cloud Platform?

Google Cloud Platform launched in 2011 and has grown to capture approximately 13% of cloud infrastructure revenue as of Q3 2025 (Synergy Research Group). GCP is built on the same infrastructure that powers Google Search, YouTube, and Gmail. It currently operates across 43 regions and 130 zones globally and is widely regarded as the technically strongest platform for data analytics, Kubernetes, and AI/ML workloads.

Notable GCP customers: Spotify, PayPal, Twitter/X, Snap, Etsy, Home Depot.

Strengths of GCP

• Best-in-class data analytics –  BigQuery, GCP’s serverless data warehouse, can query petabytes of data in seconds with no infrastructure provisioning required.
• AI and ML at the cutting edge –  Google invented TensorFlow and the Transformer architecture. Vertex AI provides a unified ML platform from data prep through to production deployment, backed by Tensor Processing Units (TPUs).
• Live VM migration –  GCP can migrate running virtual machines between physical hosts during hardware maintenance with zero downtime. Neither AWS nor Azure offers this natively.
• Kubernetes leadership –  Kubernetes was created by Google and open-sourced in 2014. Google Kubernetes Engine (GKE) remains the gold standard managed Kubernetes offering.
• Superior network performance –  Google’s private global fibre network spans over 10 million kilometres. GCP workloads communicate over this private backbone rather than the public internet.
• Cost efficiency –  GCP bills compute per second with a 1-second minimum. Sustained-use discounts of up to 30% apply automatically when an instance runs for more than 25% of a billing month –  no commitment required.

Best Use Cases for GCP

• Large-scale data analytics and BI (BigQuery, Looker)
• Custom AI and ML model development (Vertex AI, TPUs, TensorFlow)
• Containerised applications and microservices (GKE, Cloud Run)
• Real-time data pipelines (Pub/Sub, Dataflow)
• Open-source-heavy and DevOps-centric architectures
• Media, gaming, and streaming infrastructure

AWS vs. Azure vs. GCP: Head-to-Head Comparison

Feature	AWS	Microsoft Azure	Google Cloud
Market share (2025 Q3)	~29%	~20%	~13%
Launch year	2006	2010	2011
Global regions	39 regions, 123+ AZs	70+ regions	43 regions, 130 zones
Total services	200+	200+	150+
Compute	EC2	Azure Virtual Machines	Compute Engine
Object storage	S3	Azure Blob Storage	Cloud Storage
Managed Kubernetes	EKS	AKS	GKE
Serverless	Lambda	Azure Functions	Cloud Run / Cloud Functions
ML platform	SageMaker	Azure ML / Azure OpenAI	Vertex AI
Data warehouse	Redshift	Azure Synapse	BigQuery
Hybrid cloud	AWS Outposts	Azure Arc / Azure Stack	Anthos
Billing granularity	Per second (60s min)	Per minute	Per second (1s min)
Security hub	AWS Security Hub	Microsoft Defender for Cloud	Security Command Center
Free tier	12 months + always free	12 months + always free	90 days ($300 credit) + always free
Best for	Breadth, scale, startups	Enterprise Microsoft stack	Data, AI/ML, Kubernetes

Pricing Comparison

Pricing across all three platforms is complex –  compute, storage, egress, API calls, and support tiers each contribute to the monthly bill. These patterns hold consistently:

• Compute –  for equivalent VM configurations, GCP is often 10–20% cheaper on list pricing due to per-second billing (1-second minimum) and automatic sustained-use discounts. AWS and Azure require Reserved Instance or Reserved VM commitments to achieve similar savings. GCP’s 3-year Committed Use Discounts reach up to 57% for compute; AWS 3-year All-Upfront Reserved Instances reach up to 72%.
• Storage –  AWS S3 standard is approximately $0.023/GB/month. Azure Blob Storage and GCP Cloud Storage are comparable, with minor differences in retrieval and operation costs.
• Data egress –  all three providers charge for data leaving their networks. GCP historically offers lower egress pricing and free egress to Google services. Egress costs are an important consideration for data-intensive cloud application development architectures.
• Free tiers –  AWS and Azure provide 12 months of free access to core services. GCP offers a $300 credit for 90 days plus always-free usage including BigQuery (1 TB queries/month), Cloud Functions, and Cloud Storage.

Which Cloud Platform Is Best for Your Business?

There is no universal ‘best’ –  the right choice depends on your workloads, team expertise, technology stack, and budget.

Choose AWS if…

You are building a greenfield cloud application and want the broadest service catalog. Your team values the largest community and documentation ecosystem. You are building IoT, ML, or media streaming applications at scale.

Choose Azure if…

Your organisation already uses Microsoft 365, Active Directory, SQL Server, or Dynamics. You are in a regulated industry requiring extensive compliance certifications. You need hybrid cloud that bridges on-premise and public cloud infrastructure.

Choose GCP if…

Data analytics and machine learning are core to your product. You are building containerised microservices and want the best managed Kubernetes experience. You want competitive pricing with no upfront commitment via automatic sustained-use discounts.

Multi-cloud is also a valid strategy. Many enterprises use AWS for primary application workloads, Azure for enterprise integrations and Active Directory federation, and GCP for data analytics and AI. The key is avoiding accidental multi-cloud sprawl –  using multiple clouds without a coherent governance strategy leads to cost inefficiency and operational complexity.

10.  How Cloud Application Development Works on Each Platform

Cloud application development is the practice of designing, building, and deploying software that runs natively on cloud infrastructure. Cloud-native applications exploit elasticity, distributed architecture, managed services, and CI/CD pipelines –  and each platform provides a distinct environment for this work.

Cloud Application Development on AWS

AWS supports cloud application development across the full spectrum of architectural patterns. Developers commonly use Elastic Beanstalk or App Runner for containerised web apps, Lambda for event-driven serverless functions, and ECS/EKS for microservices. The AWS CDK (Cloud Development Kit) enables infrastructure-as-code using TypeScript, Python, or Java.

Cloud Application Development on Azure

Azure App Service provides a fully managed PaaS environment for web, mobile, and API applications. Azure DevOps and GitHub Actions create seamless CI/CD pipelines from commit to production. For enterprise cloud application development connecting to on-premise data sources, Azure Logic Apps and Azure API Management provide powerful integration capabilities.

Cloud Application Development on GCP

GCP’s Cloud Run –  which runs any containerised workload serverlessly –  is particularly popular for cloud application development teams that want serverless simplicity without function-level constraints. Firebase provides real-time database, authentication, and hosting optimised for mobile and web applications.

Choosing the Right Partner for Cloud Application Development

The platform is only part of the equation. Translating a product idea into a production-grade cloud application requires architecture decisions –  which services to use, how to handle state, how to secure data, how to design for failure –  that have long-term consequences. Working with an experienced cloud application development partner accelerates delivery and reduces the risk of expensive architectural rework.

At Ailoitte, our engineering team has built and scaled cloud applications across AWS, Azure, and GCP for clients in healthcare, fintech, retail, and education. Whether you are migrating a legacy monolith or building a cloud-native platform from scratch, our full-stack development and cloud services are designed to move fast without cutting corners on security, performance, or maintainability.

Conclusion

Cloud computing is no longer optional for businesses that want to remain competitive –  it is the foundation on which modern software is built, deployed, and scaled. AWS, Azure, and Google Cloud Platform each represent a genuinely excellent choice, and the decision between them comes down to your existing technology stack, team expertise, workload characteristics, and industry compliance requirements.

AWS gives you the broadest service catalog and the largest ecosystem, making it the safest default for greenfield cloud application development. Azure is the strongest choice for enterprises running Microsoft infrastructure and for organisations in regulated industries. GCP leads in data analytics, AI/ML, and Kubernetes, and frequently offers the best price-to-performance ratio for compute-intensive workloads.

The most expensive mistake businesses make is choosing a cloud platform based on marketing alone, without first mapping their workloads, team capabilities, and cost projections against each provider’s strengths.

If your team is evaluating a cloud migration or planning a new cloud-native product, Ailoitte’s cloud and technology services can help. Our engineers have hands-on production experience across AWS, Azure, and GCP, and we bring the same rigour to every engagement –  from architecture review to full-scale cloud application development and launch.

→  Talk to our cloud team at Ailoitte

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