AWS vs GCP vs Azure: Cloud Provider Comparison for CTOs

TL;DR: Decision Matrix

| Factor | AWS | GCP | Azure | Winner | |--------|-----|-----|-------|--------| | Market Share | ⭐⭐⭐⭐⭐ 32% | ⭐⭐ 10% | ⭐⭐⭐⭐ 23% | AWS | | Service Breadth | ⭐⭐⭐⭐⭐ 200+ | ⭐⭐⭐ 100+ | ⭐⭐⭐⭐ 150+ | AWS | | Hiring Pool | ⭐⭐⭐⭐⭐ Largest | ⭐⭐ Smallest | ⭐⭐⭐⭐ Large | AWS | | Cost (Baseline) | ⭐⭐⭐ $$$ | ⭐⭐⭐⭐ $$ | ⭐⭐⭐ $$$ | GCP | | Enterprise | ⭐⭐⭐⭐ Strong | ⭐⭐ Limited | ⭐⭐⭐⭐⭐ Best | Azure | | AI/ML | ⭐⭐⭐⭐ Good | ⭐⭐⭐⭐⭐ Best | ⭐⭐⭐ Catching up | GCP | | Kubernetes | ⭐⭐⭐⭐ EKS | ⭐⭐⭐⭐⭐ GKE (best) | ⭐⭐⭐⭐ AKS | GCP | | Windows/.NET | ⭐⭐ Supported | ⭐ Weak | ⭐⭐⭐⭐⭐ Native | Azure | | Networking | ⭐⭐⭐⭐⭐ Best | ⭐⭐⭐⭐ Good | ⭐⭐⭐⭐ Good | AWS | | Global Reach | ⭐⭐⭐⭐⭐ 33 regions | ⭐⭐⭐⭐ 40 regions | ⭐⭐⭐⭐⭐ 60+ regions | Azure |

Quick Recommendation:

Startup (seed/Series A): AWS (services, hiring)
Startup (AI/ML focus): GCP (BigQuery, TensorFlow)
Enterprise (Microsoft shop): Azure (100% Azure)
Enterprise (open-source): AWS or GCP
Cost-conscious: GCP (sustained use discounts)

The Real Question: Vendor Lock-in vs Best-of-Breed

Every cloud decision involves these trade-offs:

AWS = Most mature, most services, most expensive, most lock-in GCP = Best AI/ML, Kubernetes, cheapest, good DX Azure = Best for Microsoft shops, enterprise integration

There's no "best" cloud. Only the best cloud for your situation.

AWS: The Market Leader

Why AWS Dominates

Service Breadth:

200+ services (vs GCP's 100, Azure's 150)
First-mover advantage (launched 2006)
Every niche has a service (IoT, blockchain, satellites)

Maturity:

Battle-tested at scale (Netflix, Airbnb, Slack)
Most stable APIs
Best documentation (usually)

Hiring:

Largest talent pool (8.5M AWS-certified professionals)
62% of job postings mention AWS
Enterprise-proven skill set

Ecosystem:

Terraform, Pulumi support AWS first
Every SaaS integrates with AWS
Most tutorials/courses use AWS

AWS's Drawbacks

Cost:

Most expensive at list price
Complex pricing (1000+ pricing pages)
Easy to overspend (misconfigured resources)
Data egress charges (expensive to leave)

Complexity:

Overwhelming number of services
Naming is inconsistent (EC2, RDS, ECS, EKS, Fargate, Lambda...)
IAM is powerful but confusing

Developer Experience:

Console UI is dated
CLI is verbose
CloudFormation YAML is painful (use Terraform)

When to Choose AWS

✅ Startup scaling fast - Need services, hire easily, proven at scale ✅ Broad service needs - IoT, ML, analytics, databases, everything ✅ Open-source stack - Linux, containers, Kubernetes ✅ Global presence - Need multi-region from day one

❌ Don't choose AWS if:

Cost is the primary concern (GCP is cheaper)
You're all-in on Microsoft (Azure is better)
Team is small and values simplicity (GCP is easier)
AI/ML is your core competency (GCP is better)

GCP: The Technical Favorite

Why Engineers Love GCP

Best Services:

BigQuery - Best data warehouse (serverless, fast, cheap)
GKE - Best Kubernetes (Google invented it)
Vertex AI - Best AI/ML platform
Firestore - Best real-time database

Cost:

Sustained use discounts (automatic 30% off if you run VMs 24/7)
Committed use discounts (up to 70% off with 1-3 year commit)
No data transfer charges within regions
Generally 20-30% cheaper than AWS

Developer Experience:

Best console UI (modern, clean)
Cloud Shell (free VM in browser)
gcloud CLI is simpler than AWS CLI
Better defaults (security, networking)

Performance:

Google's network (fastest globally)
Live migration (VM moves without downtime)
Custom machine types (choose exact vCPU/RAM)

GCP's Drawbacks

Service Gaps:

Fewer managed services than AWS
Some services are "beta" for years
Google kills products (Cloud IoT Core, others)

Hiring:

Smallest talent pool (2.3M GCP-certified)
Only 18% of job postings mention GCP
Harder to find experienced GCP engineers

Enterprise Support:

Perceived as less "enterprise" than Azure/AWS
Sales org is smaller
Less hand-holding than AWS/Azure

Product Stability:

Google's history of killing products
Breaking API changes (rare but painful)
Less mature than AWS in many areas

When to Choose GCP

✅ Data/Analytics workload - BigQuery is the best data warehouse ✅ Kubernetes-native - GKE is the gold standard ✅ AI/ML product - TensorFlow, Vertex AI, TPUs ✅ Cost optimization - Sustained use discounts save 20-30% ✅ Modern tech stack - Greenfield, cloud-native, containers

❌ Don't choose GCP if:

Need every possible service (AWS has more)
Enterprise credibility is critical (Azure/AWS safer)
Hiring speed is bottleneck (GCP pool is smallest)
Risk-averse stakeholders (Google's history concerns them)

Azure: The Enterprise Choice

Why Enterprises Choose Azure

Microsoft Integration:

Azure AD (now Entra ID) - Best identity platform
Office 365 integration (SharePoint, Teams, OneDrive)
Hybrid cloud (on-prem + cloud) is seamless
Windows Server / SQL Server licensing (pay once, use anywhere)

Enterprise Features:

Best compliance (100+ certifications)
Best support (SLAs, dedicated TAMs)
Best for regulated industries (healthcare, finance, gov)
Hybrid cloud story (Azure Arc, Azure Stack)

Hiring:

Large talent pool (5.2M Azure-certified)
41% of job postings mention Azure
Overlaps with existing Windows admins

.NET Ecosystem:

Best place to run .NET (obviously)
Visual Studio integration
App Service for .NET is excellent

Azure's Drawbacks

Complexity:

Most complex console UI
Terminology is Microsoft-specific (confusing)
Too many ways to do the same thing

Cost:

Expensive at list price (similar to AWS)
Licensing can be confusing (bring-your-own-license, etc.)
Reserved instances aren't as flexible as AWS/GCP

Service Quality:

Some services feel "bolted on"
Open-source support is improving but lagged
Documentation quality varies

Linux Support:

Better than it was, but still Microsoft-first
Kubernetes (AKS) is good but not GKE-level

When to Choose Azure

✅ Microsoft shop - Windows, .NET, SQL Server, Office 365 ✅ Enterprise product - Compliance, support, hybrid cloud ✅ Existing EA - Already have Microsoft Enterprise Agreement ✅ Regulated industry - Finance, healthcare, government

❌ Don't choose Azure if:

Pure Linux/open-source stack (AWS or GCP better)
Cost is primary concern (GCP is cheaper)
Team is small and modern (Azure is complex)
Not using Microsoft products already

Cost Comparison (Real Numbers)

Baseline Workload

Setup: 4 VMs (2 vCPU, 8GB RAM), 500GB storage, 1TB egress/month

| Provider | List Price | Optimized | 3-Year Commit | |----------|-----------|-----------|---------------| | AWS | $450/mo | $320/mo | $210/mo | | GCP | $380/mo | $270/mo | $180/mo | | Azure | $440/mo | $310/mo | $220/mo |

Winner: GCP (20-30% cheaper baseline)

Data Warehouse Workload

Setup: 10TB data, 1TB queries/month, BigQuery vs Redshift vs Synapse

| Provider | Storage | Queries | Total | |----------|---------|---------|-------| | AWS (Redshift) | $255/mo | $500/mo | $755/mo | | GCP (BigQuery) | $200/mo | $250/mo | $450/mo | | Azure (Synapse) | $245/mo | $480/mo | $725/mo |

Winner: GCP (40% cheaper for data warehouse)

AI/ML Workload

Setup: Training models, GPU instances, managed ML platform

| Provider | Compute | Platform | Total | |----------|---------|----------|-------| | AWS (SageMaker) | $800/mo | $200/mo | $1000/mo | | GCP (Vertex AI) | $700/mo | $150/mo | $850/mo | | Azure (ML Studio) | $780/mo | $180/mo | $960/mo |

Winner: GCP (15% cheaper for ML)

Enterprise Workload

Setup: Windows VMs, SQL Server, Active Directory, compliance

| Provider | Compute | Licensing | Support | Total | |----------|---------|-----------|---------|-------| | AWS | $600/mo | $400/mo | $200/mo | $1200/mo | | GCP | $550/mo | $380/mo | $150/mo | $1080/mo | | Azure | $580/mo | $0 (EA) | $150/mo | $730/mo |

Winner: Azure (40% cheaper with EA licensing)

Key Insight: Azure wins on Microsoft workloads, GCP wins on everything else.

Service-by-Service Comparison

Compute

| Service | AWS | GCP | Azure | Best | |---------|-----|-----|-------|------| | VMs | EC2 | Compute Engine | Virtual Machines | Tie | | Containers | ECS/EKS | GKE | AKS | GCP (GKE is best K8s) | | Serverless | Lambda | Cloud Functions | Functions | AWS (most mature) | | Batch | Batch | Cloud Run Jobs | Batch | AWS |

Storage

| Service | AWS | GCP | Azure | Best | |---------|-----|-----|-------|------| | Object | S3 | Cloud Storage | Blob Storage | AWS (S3 is standard) | | Block | EBS | Persistent Disk | Managed Disks | Tie | | File | EFS | Filestore | Files | Tie |

Databases

| Service | AWS | GCP | Azure | Best | |---------|-----|-----|-------|------| | Relational | RDS | Cloud SQL | SQL Database | Azure (.NET integration) | | NoSQL | DynamoDB | Firestore | Cosmos DB | GCP (Firestore best DX) | | Data Warehouse | Redshift | BigQuery | Synapse | GCP (BigQuery wins) | | Cache | ElastiCache | Memorystore | Cache for Redis | Tie |

Networking

| Service | AWS | GCP | Azure | Best | |---------|-----|-----|-------|------| | CDN | CloudFront | Cloud CDN | Front Door | AWS (most PoPs) | | Load Balancer | ALB/NLB | Cloud Load Balancing | Load Balancer | Tie | | VPN | Site-to-Site VPN | Cloud VPN | VPN Gateway | Tie | | Global Network | AWS Global Accelerator | Premium Tier | ExpressRoute | GCP (Google's network) |

AI/ML

| Service | AWS | GCP | Azure | Best | |---------|-----|-----|-------|------| | ML Platform | SageMaker | Vertex AI | ML Studio | GCP (ease of use) | | Vision AI | Rekognition | Vision API | Computer Vision | GCP | | NLP | Comprehend | Natural Language API | Text Analytics | GCP | | Custom Models | SageMaker | Vertex AI | ML Studio | GCP (TPUs) |

Multi-Cloud Strategy

Should You Go Multi-Cloud?

Reasons to use multiple clouds:

Regulatory - Data residency requires specific regions
Best-of-breed - BigQuery on GCP, Lambda on AWS, AD on Azure
Vendor negotiation - Credible threat to switch
Acquisition - You bought a company on different cloud

Reasons NOT to:

Complexity - 3x the learning curve, 3x the tooling
Cost - Teams, tools, training multiply
Integration - Cross-cloud networking is expensive and slow
Talent - Hard to find engineers who know all three

Recommendation:

Pick one primary cloud (80% of workloads)
Use others tactically (BigQuery on GCP even if you're AWS)
Avoid active-active multi-cloud (operational nightmare)

Migration Considerations

Moving TO AWS

From on-prem:

Use AWS Migration Hub
Lift-and-shift to EC2 first, optimize later
Typical timeline: 6-18 months

From Azure:

Hard (different primitives)
Consider if: Cost savings justify effort
Typical timeline: 12-24 months

From GCP:

Moderate (Kubernetes makes it easier)
Common reason: Need more services, easier hiring
Typical timeline: 9-15 months

Moving TO GCP

From AWS:

Moderate (containers help)
Common reason: Cost, Kubernetes, BigQuery
Typical timeline: 9-15 months

From Azure:

Hard (especially if .NET)
Rare (usually only for cost)
Typical timeline: 12-24 months

Moving TO Azure

From on-prem Microsoft:

Easiest migration path
Use Azure Migrate
Typical timeline: 6-12 months

From AWS/GCP:

Hard, rare (usually acquisition)
Only if: Existing Microsoft EA, .NET stack
Typical timeline: 12-24 months

Migration Truth: Cloud-to-cloud migrations are expensive ($500K-$5M+) and risky. Only migrate if:

Cost savings > $200K/year (2-year payback)
Technical blocker (need BigQuery, need AD integration)
Compliance requirement

The CTO's Checklist

1. Workload Type

[ ] What's our primary workload? (Web app, data, ML, Windows)
[ ] Is it cloud-native or lift-and-shift?
[ ] Do we need specialized services (BigQuery, SageMaker)?

2. Team

[ ] What cloud does our team know?
[ ] How fast do we need to hire?
[ ] Do we have Microsoft skills (.NET, Windows, AD)?

3. Cost

[ ] What's our estimated monthly spend?
[ ] Do we have Microsoft EA or AWS credits?
[ ] Is cost optimization a priority?

4. Compliance

[ ] What certifications do we need? (SOC2, HIPAA, FedRAMP)
[ ] Any data residency requirements?
[ ] Hybrid cloud or cloud-only?

5. Future

[ ] Will we need AI/ML in 2 years?
[ ] Will we need global presence?
[ ] Will we acquire companies on different clouds?

Real-World Case Studies

Case 1: SaaS Startup (AWS)

Choice: AWS Reason: Hiring, services, proven at scale Outcome: Scaled to 200 engineers, $500K/mo AWS bill, exploring GCP for BigQuery

Lesson: AWS is safe bet for rapid scaling

Case 2: Data Company (GCP)

Choice: GCP Reason: BigQuery, Kubernetes, cost Outcome: Saved $150K/year vs AWS, hired 20 engineers (harder but doable)

Lesson: GCP wins for data-heavy workloads

Case 3: Enterprise SaaS (Azure)

Choice: Azure Reason: Microsoft shop, Office 365 integration, existing EA Outcome: Seamless AD integration, 40% cost savings with EA licensing

Lesson: Azure is no-brainer for Microsoft shops

The Honest Recommendation

If you're choosing today (2025):

Default to AWS - Safest choice, most services, easiest hiring
Choose GCP - If cost matters, data/ML workload, Kubernetes-native
Choose Azure - If Microsoft shop, enterprise compliance, hybrid cloud

Decision Tree:

Are you a Microsoft shop (.NET, Windows, Office 365)?
├─ Yes → Azure (100% Azure)
└─ No → Is cost your top priority?
    ├─ Yes → GCP (20-30% cheaper)
    └─ No → Is hiring your bottleneck?
        ├─ Yes → AWS (largest talent pool)
        └─ No → Is data/ML your core product?
            ├─ Yes → GCP (BigQuery, Vertex AI)
            └─ No → AWS (safest, most complete)

Red Flags:

"Let's go multi-cloud for vendor negotiation" (complexity > savings)
"GCP is too risky, Google kills products" (BigQuery isn't going anywhere)
"We'll use AWS because everyone does" (might overpay)

Green Lights:

"AWS because we need to hire fast and ship" (valid)
"GCP because BigQuery saves us $200K/year" (ROI-driven)
"Azure because we're all-in on Microsoft" (strategic alignment)

Conclusion: All Three Are Great

You can't go wrong with AWS, GCP, or Azure. Poor decisions look like:

❌ Multi-cloud for the sake of it ❌ Migrating clouds for small cost savings ❌ Ignoring your team's existing skills ❌ Choosing based on hype

Good decisions look like:

✅ Picking based on workload type ✅ Considering hiring and team skills ✅ Calculating real TCO (not just list price) ✅ Aligning with existing technology investments

The best cloud is the one that fits your workload, team, and business model.

TL;DR: Decision Matrix

The Real Question: Vendor Lock-in vs Best-of-Breed

AWS: The Market Leader

Why AWS Dominates

AWS's Drawbacks

When to Choose AWS

GCP: The Technical Favorite

Why Engineers Love GCP

GCP's Drawbacks

When to Choose GCP

Azure: The Enterprise Choice

Why Enterprises Choose Azure

Azure's Drawbacks

When to Choose Azure

Cost Comparison (Real Numbers)

Baseline Workload

Data Warehouse Workload

AI/ML Workload

Enterprise Workload

Service-by-Service Comparison

Compute

Storage

Databases

Networking

AI/ML

Multi-Cloud Strategy

Should You Go Multi-Cloud?

Migration Considerations

Moving TO AWS

Moving TO GCP

Moving TO Azure

The CTO's Checklist

1. Workload Type

2. Team

3. Cost

4. Compliance

5. Future

Real-World Case Studies

Case 1: SaaS Startup (AWS)

Case 2: Data Company (GCP)

Case 3: Enterprise SaaS (Azure)

The Honest Recommendation

Conclusion: All Three Are Great

Further Reading

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Microservices vs Monolith: Architecture Decision Guide for CTOs