Azure vs AWS vs GCP: Cloud Platform Comparison 2025
Detailed analysis of Microsoft Azure, Amazon Web Services, and Google Cloud Platform. Comparing pricing, services, Kubernetes, compliance, and strategies for choosing the right cloud platform for your project.
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Picking a cloud platform is a decision that sticks with you for years. Azure, AWS, or GCP? Each one has hundreds of services, different pricing models, and real trade-offs. Get it right and you save money and move faster. Get it wrong and you're migrating in two years.
Over the past decade, I've worked on dozens of projects across all three platforms. Startups building MVPs, mid-sized companies migrating from on-premise, enterprises going multi-cloud. This article shares what I've learned to help you pick the right cloud for your project.
Table of Contents
Quick summary: which cloud for whom?
Microsoft Azure
Ideal for: Enterprise with Microsoft stack (.NET, Windows, SQL Server), companies requiring GDPR compliance (Azure Poland region), hybrid cloud (Azure Arc).
Amazon Web Services (AWS)
Ideal for: Startups, largest service ecosystem (200+), global expansion (32 regions), widest community and tooling support.
Google Cloud Platform (GCP)
Ideal for: AI/ML projects, Kubernetes-native apps, Big Data analytics, most competitive pricing (sustained use discounts), open source enthusiasts.
Market share and trends 2025
The cloud computing market is worth over $600 billion annually and growing at 20% year over year. Here's where things stand:
AWS -- market leader
The cloud pioneer (since 2006). Still holds the largest share, especially popular with startups and tech companies.
Azure -- fastest growth
Growing 30% year over year. Dominates enterprise, especially companies already on Microsoft 365 and Active Directory.
GCP -- the innovator
Leads in AI/ML and Big Data. Smallest share, but moves fastest on new features and often undercuts on price.
Key trends 2025
- •Multi-cloud dominates: 87% of enterprises use more than one cloud (best-of-breed approach, vendor lock-in avoidance).
- •Kubernetes as standard: All three platforms invest heavily in managed Kubernetes (AKS, EKS, GKE) – abstraction over cloud.
- •FinOps critical: Cost optimization priority #1 – companies overspend 30% on average due to lack of governance.
- •Edge computing growing: Azure Arc, AWS Outposts, Google Anthos – hybrid and edge solutions for IoT, manufacturing, retail.
Pricing comparison: who's cheapest?
Short answer: it's complicated. Each platform has different pricing models, discounts, and hidden costs. Here's a detailed analysis:
Example: Standard web application (benchmark)
Stack: 3x VM (4 vCPU, 16GB RAM), 500GB SSD storage, 1TB transfer, PostgreSQL managed DB, load balancer, backup. Location: Western Europe. Monthly pricing (USD):
| Component | AWS | Azure | GCP |
|---|---|---|---|
| Compute (VMs) | $450 | $425 | $380 |
| Storage (SSD) | $80 | $75 | $68 |
| Database (PostgreSQL) | $250 | $240 | $220 |
| Network (LB + transfer) | $60 | $50 | $45 |
| Backup & monitoring | $40 | $35 | $30 |
| TOTAL monthly | $880 | $825 | $743 |
* Approximate pricing as of October 2025. Pay-as-you-go pricing without commitments.
GCP – Cheapest (up to 15-20%)
- Sustained Use Discounts: Automatic discounts up to 30% for continuous usage (no commitments!)
- Committed Use Discounts: 1-year commitment = 37%, 3-year = 55% cheaper
- Preemptible VMs: Up to 80% cheaper for batch jobs, dev/test (similar to AWS Spot)
- Aggressive pricing: Google deliberately undercuts prices to gain market share
- Free tier: More generous than AWS/Azure – Always Free tier on some services
Azure – Middle ground (but advantages for enterprise)
- Azure Hybrid Benefit: Use existing Windows/SQL Server licenses = 40% savings
- Reserved Instances: 1-year = 40%, 3-year = 62% cheaper (similar to AWS)
- Dev/Test pricing: Special discounts for dev/test environments (up to 50% cheaper)
- Azure Spot VMs: Up to 90% cheaper for tolerant workloads
- Enterprise Agreements: Negotiated discounts for large companies (often 10-20% extra)
AWS – Most expensive (but flexible)
- Reserved Instances: 1-year = 40%, 3-year = 60% cheaper (requires commitment)
- Savings Plans: More flexible than RI – discounts up to 72% for $/hour spend commit
- Spot Instances: Up to 90% cheaper, but can be interrupted (great for batch, ML training)
- Pay-as-you-go: Most expensive without optimization, but simplest to start
- Data transfer: Expensive egress fees (transfer out) – one of the main hidden costs
Pricing calculators (official links)
Always test your own scenarios – the benchmark above is a reference point, your needs will differ:
- Azure Pricing Calculator – Intuitive, visual service selection
- AWS Pricing Calculator – Most detailed, hundreds of options
- GCP Pricing Calculator – Simple, automatically adds sustained use discounts
Services comparison: compute, storage, databases, networking
All three platforms have hundreds of services. Here's how the main categories stack up:
| Category | Azure | AWS | GCP |
|---|---|---|---|
| Virtual Machines | Azure Virtual Machines Broad choice, Windows optimized | EC2 (Elastic Compute Cloud) Largest instance type selection | Compute Engine Custom machine types flexibility |
| Serverless Functions | Azure Functions Good ecosystem integration | AWS Lambda Market leader, largest ecosystem | Cloud Functions / Cloud Run Cloud Run = containers as functions |
| Kubernetes | AKS (Azure Kubernetes Service) Control plane FREE, easy setup | EKS (Elastic Kubernetes Service) $0.10/hour control plane (~$73/mth) | GKE (Google Kubernetes Engine) Best-in-class, Autopilot mode |
| Object Storage | Azure Blob Storage Hot/Cool/Archive tiers | S3 (Simple Storage Service) Industry standard, biggest ecosystem | Cloud Storage Unified API, auto-tiering |
| Block Storage | Azure Managed Disks SSD/HDD, good performance | EBS (Elastic Block Store) Many types, io2 Block Express fastest | Persistent Disk Auto-resize, snapshots cheaper |
| Relational DB | Azure SQL / PostgreSQL / MySQL SQL Server best integration | RDS (Aurora, PostgreSQL, MySQL...) Aurora = outstanding performance | Cloud SQL / AlloyDB AlloyDB = PostgreSQL-compatible, fast |
| NoSQL DB | Cosmos DB Multi-model, global distribution | DynamoDB Proven scale, serverless pricing | Firestore / Bigtable Firestore = realtime, Bigtable = analytics |
| Load Balancer | Azure Load Balancer / App Gateway L4/L7, good features | ELB / ALB / NLB Most mature, many options | Cloud Load Balancing Unified, global, auto-scaling |
| CDN | Azure CDN / Front Door Front Door = premium, WAF included | CloudFront Largest edge network, deep S3 integration | Cloud CDN Good performance, competitive pricing |
| AI/ML Platform | Azure Machine Learning Good integration, AutoML | SageMaker Complete ML lifecycle, popular | Vertex AI Best-in-class, TensorFlow native |
Where each platform stands out
Azure: enterprise and the Microsoft ecosystem
Tightest integration with Windows Server, SQL Server, Active Directory, Microsoft 365, Power Platform. If your company runs on Microsoft, Azure is the path of least resistance (and hybrid licenses save real money).
- Azure AD (Entra ID) – best identity platform
- Azure DevOps – complete CI/CD, born in Microsoft
- Hybrid cloud (Azure Arc, Azure Stack) – leader in hybrid scenarios
AWS: breadth and depth
Most services (200+), longest track record (since 2006), most edge locations (400+). If you need something niche, AWS probably has it.
- AWS Lambda – serverless pioneer, largest ecosystem
- Amazon Aurora – outstanding database performance
- Marketplace – largest selection of third-party solutions
GCP: data and developer experience
Leads in AI/ML (TensorFlow, TPUs), Big Data (BigQuery), and Kubernetes (invented by Google). Most developer-friendly of the three, and the most open-source oriented.
- BigQuery – unmatched analytics performance & price
- GKE Autopilot – most managed Kubernetes
- Vertex AI – best ML platform for production
Kubernetes: AKS vs EKS vs GKE
Kubernetes is the standard for container orchestration. All three platforms offer managed Kubernetes, but the differences matter:
If you're planning to deploy Kubernetes on Azure, check out our detailed guide: Azure AKS - Production Deployment Guide.
| Feature | AKS (Azure) | EKS (AWS) | GKE (GCP) |
|---|---|---|---|
| Control Plane Cost | FREE | $0.10/hour (~$73/mth) | FREE (standard), $0.10/hr (Autopilot) |
| Setup Time | 5-10 min (az aks create) | 15-20 min (eksctl slower) | 3-5 min (fastest) |
| Auto-upgrade | Yes (maintenance windows) | Manual (more control) | Yes (release channels: rapid/regular/stable) |
| Node Auto-scaling | Cluster Autoscaler (good) | Cluster Autoscaler / Karpenter (best) | Autopilot = fully managed (excellent) |
| Windows Containers | Excellent (native Windows support) | Good (supported) | Limited (experimental) |
| Networking | Azure CNI, Kubenet (good) | AWS VPC CNI (mature, battle-tested) | GKE native (best performance) |
| Multi-cluster | Azure Arc (hybrid/multi-cloud) | EKS Anywhere (on-premise K8s) | Anthos (best multi-cloud K8s) |
| Monitoring | Azure Monitor / Container Insights | CloudWatch Container Insights | Cloud Monitoring (native Prometheus) |
| Security | Azure AD integration, Pod Identity | IAM Roles for Service Accounts (IRSA) | Workload Identity (best security model) |
| Best for | Enterprise, Windows workloads, Azure ecosystem | AWS-native apps, largest ecosystem, control | Kubernetes purists, GitOps, least operational overhead |
AKS – Azure Kubernetes Service
Good balance between managed and control. The free control plane is a real cost advantage.
EKS – Elastic Kubernetes Service
Most popular managed K8s. Mature, stable, biggest community.
GKE – Google Kubernetes Engine
The most polished managed Kubernetes. Google invented K8s, and it shows.
My experience with production K8s
After deploying 40+ production Kubernetes clusters across all three platforms, here's what I've found:
- GKE Autopilot -- least operational work. Set it up and move on. Great for small teams without a dedicated SRE.
- EKS + Karpenter -- best for large-scale (100+ nodes). Karpenter's auto-scaling is impressive, but takes more setup.
- AKS -- a solid middle ground. Free control plane saves real money in multi-cluster setups (dev/staging/prod = $220/mth saved). Azure DevOps integration works well out of the box.
Bottom line: Want least maintenance → GKE Autopilot. Need control + scale → EKS + Karpenter. Azure ecosystem → AKS (especially with FREE control plane).
DevOps and CI/CD: tools and integrations
Each platform has native CI/CD tools, and all of them work with popular third-party solutions too:
Azure DevOps Ecosystem
- Azure DevOps: Complete ALM platform (repos, pipelines, boards, artifacts)
- GitHub Actions: Microsoft-owned, deep Azure integration
- Azure Pipelines: Native CI/CD, free for open source
- Azure Artifacts: Package management (npm, NuGet, Maven)
- Azure Container Registry: Private Docker registry
Advantage: If using GitHub/Azure DevOps – native Azure integration is zero friction.
AWS DevOps Ecosystem
- CodePipeline: Managed CI/CD orchestration
- CodeBuild: Managed build service
- CodeDeploy: Automated deployment to EC2/Lambda/ECS
- ECR: Elastic Container Registry (Docker images)
- CodeArtifact: Artifact repository
Advantage: Largest number of third-party integrations (Jenkins, GitLab, CircleCI, etc.)
GCP DevOps Ecosystem
- Cloud Build: Serverless CI/CD (pay per minute)
- Artifact Registry: Universal artifact repository
- Cloud Deploy: Managed continuous delivery to GKE
- Cloud Source Repositories: Private Git repos
- Binary Authorization: Deploy-time security policy enforcement
Advantage: Most GitOps-friendly, excellent Kubernetes-native CI/CD
Platform-agnostic CI/CD (what I use for multi-cloud)
If planning multi-cloud or want to avoid vendor lock-in, use tools that work everywhere:
GitHub Actions
Most popular CI/CD for modern apps. Works identically with Azure, AWS, GCP.
- Biggest marketplace (10,000+ actions)
- Free for public repos, cheap for private
- Native secrets management
GitLab CI/CD
Complete DevOps platform. Self-hosted or cloud.
- Auto DevOps (zero config for standard apps)
- Built-in Container Registry
- Security scanning included
ArgoCD (GitOps)
Declarative GitOps for Kubernetes. CNCF graduated project.
- Git as single source of truth
- Automatic sync, rollback
- Multi-cluster management
Terraform Cloud
Infrastructure as Code CI/CD. Supports Azure, AWS, GCP, 3000+ providers.
- Remote state management
- Policy as Code (Sentinel)
- Cost estimation pre-apply
Geographic availability and compliance
For companies operating in Poland and the EU, GDPR compliance is non-negotiable. EU data residency is a legal requirement in many industries like finance, healthcare, and public administration.
| Aspect | Azure | AWS | GCP |
|---|---|---|---|
| Global Regions | 60+ regions | 32 regions (largest) | 40+ regions |
| EU Regions | 10+ (incl. Poland!) | 8 regions | 6 regions |
| Poland Region | YES – Warsaw region (2024) | NO – closest: Frankfurt | NO – closest: Warsaw (planned 2026) |
| GDPR Compliance | Excellent – EU Data Boundary commitment | Excellent – AWS GDPR compliance tools | Excellent – strong privacy stance |
| Certifications | ISO 27001, SOC 1/2/3, PCI DSS, HIPAA | 90+ compliance programs (most) | ISO 27001, SOC 1/2/3, PCI DSS |
| Data Residency | Guaranteed EU data residency | Configurable (stay in EU regions) | Configurable (stay in EU regions) |
| Gov/Public Sector | Azure Government Cloud | AWS GovCloud (US only) | Limited gov offerings |
Azure Poland region: a big deal for Polish companies
In 2024, Microsoft opened an Azure region in Warsaw. This matters a lot for Polish businesses:
- ✓GDPR compliance out-of-the-box: Data never leaves Poland. Perfect for banks, insurance, healthcare, public administration.
- ✓Low latency: 2-5ms latency from Warsaw vs 15-25ms Frankfurt. Significant difference for real-time applications.
- ✓Disaster recovery in-country: Paired region in Poland – geo-redundancy without legal risk of cross-border data transfer.
- ✓Local support: Microsoft Partner Network in Poland, Polish language documentation, local support engineers.
Compliance resources (official links)
- Azure Compliance Documentation – Complete compliance offerings, trust center
- AWS Compliance Center – 90+ compliance programs, risk management tools
- GCP Compliance Resource Center – Security, privacy, and compliance information
When to choose which platform
There's no universal "best cloud." It comes down to your business context, tech stack, team, and budget. Here's how I think about it:
Choose Azure if:
Tech Stack & Ecosystem
- • Using Microsoft stack (.NET, C#, Windows Server, SQL Server)
- • Microsoft 365 / Active Directory in company (seamless SSO)
- • Power Platform (Power BI, Power Apps, Power Automate)
- • GitHub or Azure DevOps as CI/CD
Business Requirements
- • Enterprise with long-term Microsoft strategy
- • GDPR compliance critical (Azure Poland region)
- • Hybrid cloud (on-premise + cloud) – Azure Arc
- • Already have Windows/SQL licenses (Azure Hybrid Benefit = 40% cheaper)
Sweet spot: Polish enterprise with Microsoft 365, Active Directory, .NET applications, GDPR compliance requirement. Azure is the obvious choice – fastest ROI, integration with least friction.
Choose AWS if:
Tech Stack & Ecosystem
- • Open source stack (Linux, PostgreSQL, Redis, Kafka)
- • Need wide service selection (200+ services)
- • Team already has AWS experience (largest talent pool)
- • Using popular tools (Terraform, Ansible, Datadog)
Business Requirements
- • Startup with dynamic growth (AWS Activate program)
- • Global expansion (32 regions, 400+ edge locations)
- • Need exotic services (IoT, robotics, satellite)
- • Largest marketplace of third-party solutions
Sweet spot: Tech startup with Node.js/Python, PostgreSQL, Redis – building SaaS, planning global expansion. AWS has the biggest startup ecosystem, easy to find engineers, most battle-tested services.
Choose GCP if:
Tech Stack & Ecosystem
- • AI/ML projects (TensorFlow, Vertex AI, TPUs)
- • Big Data analytics (BigQuery unmatched)
- • Kubernetes-native architecture (GKE best-in-class)
- • Open source first mentality (Cloud Run, Anthos)
Business Requirements
- • Budget critical (sustained use discounts auto)
- • DevOps/SRE team experience (less hand-holding needed)
- • Data-driven company (analytics, ML core business)
- • GitOps workflows (Cloud Build, Binary Authorization)
Sweet spot: Startup/scale-up focused on data & ML, Kubernetes-native apps, DevOps-savvy team. GCP offers best price, best K8s, best ML tools. If you know what you're doing – GCP shines.
Real-world use cases
Azure Success Stories
- Volkswagen:
Automotive Cloud platform – Azure IoT, AI for connected cars, 40M vehicles
- Walmart:
Hybrid cloud with Azure Stack – on-premise stores + cloud analytics
- NHS (UK Healthcare):
Azure for healthcare compliance, Teams for 1.2M healthcare workers
- Boeing:
Azure HPC for aerospace simulations, AI analytics
AWS Success Stories
- Netflix:
200M users, 100% AWS – EC2, S3, Lambda. Cloud-native architecture pioneer
- Airbnb:
150M users, complete AWS stack – RDS, ElastiCache, EMR for analytics
- Spotify:
500M users, AWS data lake – S3, EMR, ML for recommendations
- Slack:
AWS global infrastructure, auto-scaling for 10M+ daily users
GCP Success Stories
- Spotify:
BigQuery for analytics – 1.5PB data, real-time insights for 500M users
- Twitter (X):
GCP for ML/AI – tweet recommendations, content moderation
- PayPal:
GKE Autopilot – 400M users, transaction processing, fraud detection
- Snapchat:
GCP core infrastructure – 750M users, BigQuery for analytics
Multi-cloud: the common approach
87% of enterprises use more than one cloud. Typical multi-cloud scenarios:
- •Best-of-breed: AWS for core workloads, GCP for BigQuery analytics, Azure for Microsoft 365 integration. Pick best service from each cloud.
- •Geo-distribution: AWS in USA (biggest presence), Azure in Europe (GDPR), GCP in Asia (strong APAC regions). Optimize latency globally.
- •Disaster recovery: Primary on AWS, DR failover on Azure. Different cloud = different failure domains (true HA).
- •Acquisitions: Company acquires another on different cloud. Sometimes easier to maintain multi-cloud than migrate everything.
Need help choosing a cloud?
Choosing a cloud platform is a decision for years. I'll help you analyze requirements, compare options, and select the optimal solution for your business.
What I can help with:
- Cloud readiness assessment and requirements analysis
- Platform comparison with TCO analysis (Total Cost of Ownership)
- Reference architecture design for your use case
- Migration strategy, lift-and-shift or re-architecture
- PoC/Pilot test deployment on chosen platform
- Cost optimization with FinOps practices, reserved instances
- Multi-cloud strategy when one cloud is not enough
- Training and knowledge transfer for your team
Background:
- 10+ years cloud architecture (Azure, AWS, GCP)
- 60+ successful cloud migrations
- Azure, AWS, and GCP certified
- Average savings post-migration: 35-50%
- All projects: 99.9%+ uptime SLA
- Specialization: Azure Poland region, GDPR compliance
- Reference clients: fintech, e-commerce, SaaS, manufacturing
- Long-term partnerships, not just deploy-and-disappear
📧 Email: hello@wojciechowski.app · Response within 24h
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Frequently Asked Questions
Which cloud platform is cheapest for typical workloads?
There is no single winner - it depends on workload mix. AWS is generally cheapest for compute-heavy scenarios (large instances, databases). Azure is cheapest for Windows/SQL Server (license inclusions). GCP is cheapest for data analytics and ML. Real advice: get quotes from each provider and expect 15-30% variation based on your exact workload mix.
Can I easily migrate between Azure, AWS, and GCP later?
Managed services are hardest to migrate (RDS to Azure Database, Redshift to BigQuery). Containers (Docker/Kubernetes) are most portable. Use managed services for competitive advantage, but expect 6-12 months of migration effort. Hybrid/multi-cloud is now standard practice for enterprise risk mitigation. Build abstraction layers early using Terraform and Kubernetes.
Which cloud has the best Kubernetes (AKS vs EKS vs GKE)?
GKE: Most advanced, best integration with Google services. EKS: Most mature, largest community, best for pure-AWS environments. AKS: Tightest Azure integration, best for teams already on Azure, strong for .NET workloads. All three are production-grade. Choose based on existing cloud commitment, not Kubernetes features alone.
Which provider complies with GDPR and has EU data centers?
All three have EU regions (Ireland, Germany, France). All have GDPR-compliant data residency options. Azure strongest in EU presence. Requirement: Sign Data Processing Agreement (DPA) with your chosen provider. Key fact: GDPR compliance is about your controls and agreements, not the cloud provider's general features.
What are the hidden costs I should know?
Data transfer costs can reach 30-50% of bills for high-volume apps (AWS has the most expensive egress). Reserved instances on AWS/Azure require 1-3 year commitments while GCP is more flexible. Enterprise support tiers run $10k-50k/month. The average organization wastes 30-40% on unused services. Use cost management tools (Kubecost, CloudHealth) from day one.
References
- [1] Microsoft Azure - Official Documentation -https://learn.microsoft.com/en-us/azure/
- [2] Microsoft Learn - Azure Training Center -https://learn.microsoft.com/en-us/training/azure/
- [3] Kubernetes - Official Documentation -https://kubernetes.io/docs/
- [4] CNCF Annual Survey 2023 - State of Kubernetes Adoption -https://www.cncf.io/reports/cncf-annual-survey-2023/
- [5] .NET - Official Microsoft Documentation -https://learn.microsoft.com/en-us/dotnet/
- [6] .NET Blog - Latest updates and best practices -https://devblogs.microsoft.com/dotnet/
- [7] Flexera State of the Cloud Report 2024 -https://www.flexera.com/blog/cloud/cloud-computing-trends-2024-state-of-the-cloud-report/
- [8] FinOps Foundation - Best Practices -https://www.finops.org/
- [9] Gartner - Cloud Computing Research -https://www.gartner.com/en/information-technology/insights/cloud-computing
- [10] AWS - Official Documentation -https://docs.aws.amazon.com/
- [11] Google Cloud - Official Documentation -https://cloud.google.com/docs