Updated September 2026 · Written and maintained by the Progression Agency strategy team
Amazon Web Services, Microsoft Azure and Google Cloud offer the same categories of service under different names, with different strengths, pricing habits and ecosystems. This comparison is written for businesses choosing where a web application, SaaS product or company infrastructure should run, not for certification study: it covers compute, databases, serverless, managed Kubernetes, AI, pricing, compliance and the practical question of which one your team can operate.
On this page · 10 sections
The short answerChoose AWS when you want the widest service catalog, the largest talent pool and the most third-party integrations; it is the default for startups and most SaaS companies. Choose Azure when the organization runs on Microsoft (Microsoft 365, Active Directory, .NET, SQL Server) or has enterprise agreements that make it cheaper. Choose Google Cloud for data analytics (BigQuery), Kubernetes (GKE) and AI tooling, or when your team prefers its cleaner developer experience. For a small web app, none of the three is required: Vercel, Cloudflare or a VPS is simpler and cheaper until scale or compliance demands a hyperscaler.
Comparisons are based on each project’s official documentation as linked in the text. Progression Agency builds with all of the technologies compared and has no affiliate relationship with any of them.
AWS vs Azure vs Google Cloud at a glance
AWS launched in 2006 and remains the largest cloud provider by revenue, with Azure second and Google Cloud third, according to Synergy Research Group‘s market tracking. Service breadth follows the same order; developer experience is often ranked in the reverse order. Documentation: AWS, Azure, Google Cloud.
| Category | AWS | Azure | Google Cloud |
|---|---|---|---|
| Virtual machines | EC2 | Virtual Machines | Compute Engine |
| Containers (managed Kubernetes) | EKS (and ECS, Fargate) | AKS (and Container Apps) | GKE (and Cloud Run) |
| Serverless functions | Lambda | Azure Functions | Cloud Functions / Cloud Run functions |
| App hosting (PaaS) | Elastic Beanstalk, App Runner, Amplify | App Service, Static Web Apps | App Engine, Cloud Run |
| Object storage | S3 | Blob Storage | Cloud Storage |
| Relational database | RDS, Aurora | Azure SQL, Database for PostgreSQL/MySQL | Cloud SQL, AlloyDB, Spanner |
| NoSQL | DynamoDB | Cosmos DB | Firestore, Bigtable |
| Data warehouse | Redshift | Synapse, Fabric | BigQuery |
| CDN | CloudFront | Azure Front Door / CDN | Cloud CDN |
| Identity | IAM, Cognito | Entra ID (Active Directory) | IAM, Identity Platform |
| AI platform | SageMaker, Bedrock | Azure AI, Azure OpenAI Service | Vertex AI, Gemini |
Compute and hosting for web apps
All three run a containerized Next.js, Django, Laravel or .NET app well. Google Cloud Run is the simplest container platform of the three for a small team; AWS App Runner and Azure Container Apps are the equivalents. For virtual machines the products are interchangeable; for Kubernetes, GKE is generally considered the most polished, EKS the most widely used, AKS the natural choice for Microsoft shops. See backend hosting options.
Databases and data
AWS RDS and Aurora, Azure Database for PostgreSQL and Google Cloud SQL all offer managed PostgreSQL and MySQL. Azure SQL is the home of SQL Server workloads. Google’s BigQuery is the standout analytics warehouse for teams without data engineers; AWS Redshift and Azure Synapse or Fabric serve the same purpose inside their ecosystems.
Serverless and edge
Lambda has the largest ecosystem and integrations; Azure Functions integrate with Microsoft services; Google Cloud Run (containers) and Cloud Functions are simple and scale to zero. None matches Cloudflare Workers for edge latency, which is why Cloudflare often sits in front of all three; see Cloudflare vs AWS.
AI and machine learning
Azure OpenAI Service gives enterprises OpenAI models under Microsoft’s compliance umbrella; AWS Bedrock offers a menu of models from several vendors plus SageMaker for training; Google’s Vertex AI and Gemini models pair with BigQuery for data-heavy work. For most product teams adding AI features, the choice follows where the data already lives. See AI integration services.
Pricing and cost control
List prices for comparable virtual machines and storage are close; the bill differs through data transfer out, support plans, reserved or committed-use discounts and enterprise agreements. Azure credits and Microsoft enterprise agreements can make it cheapest for Microsoft customers; Google’s sustained-use discounts apply automatically; AWS has the deepest savings-plan options. All three need budgets, alerts and tagging from day one. Pricing calculators: AWS, Azure, Google Cloud.
Compliance and regions
All three hold the major attestations (SOC 2, ISO 27001, HIPAA eligibility with a BAA, PCI DSS, FedRAMP for government regions). Region coverage is broadest on AWS and Azure. For healthcare, fintech and public-sector products the deciding factor is usually the organization’s existing compliance program rather than the provider. See healthcare software development and fintech software development.
Talent and ecosystem
AWS skills are the most common on the US market, Azure skills are concentrated in enterprises and consultancies, Google Cloud skills are rarer but strong in data teams. Third-party SaaS tools, marketplaces and tutorials follow the same order. For a company that will hire DevOps engineers, this matters more than any feature difference.
Which should you choose?
| Situation | Recommendation | Why |
|---|---|---|
| Startup or SaaS with no existing platform | AWS | Ecosystem, talent, integrations |
| Company running Microsoft 365, AD, .NET, SQL Server | Azure | Identity, licensing, enterprise agreements |
| Data-heavy product, analytics, ML pipelines | Google Cloud | BigQuery, Vertex AI, GKE |
| Small web app or marketing site | None of the three yet | Vercel, Cloudflare or a VPS is simpler |
| Regulated enterprise with an existing cloud program | Whichever the program covers | Compliance inheritance |
| Team already skilled in one | That one | Operating skill beats features |
How Progression Agency works with the clouds
We build cloud-agnostic where practical (containers, Postgres, object storage, infrastructure as code) and deploy to the provider the client already uses or, for new products, to AWS by default with Cloudflare in front. We set up accounts, budgets and access in the client’s name. See cloud migration services, DevOps services and SaaS hosting.
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Frequently asked questions
Which is best: AWS, Azure or Google Cloud?
Which cloud is cheapest?
Which cloud is easiest to use?
Which cloud has the most services?
Is Azure better for .NET applications?
Is Google Cloud good for startups?
Do I need a hyperscaler for a web app?
Which cloud is best for AI?
Which cloud is best for Kubernetes?
Can I use more than one cloud?
Are all three HIPAA compliant?
How do I control cloud costs?
Which does Progression Agency recommend?
What is a landing zone?
Can you migrate us between clouds?
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