AWS vs Azure vs GCP: Which Cloud Platform Should You Learn in 2026?
Learn AWS first. It has the largest market share (~32%), the most job postings, the broadest service catalogue, and the most learning resources available. Once you understand AWS deeply, Azure and GCP concepts map directly cloud fundamentals are universal across platforms.
That said, the "best" platform depends on your career goals, target employers, and the kind of infrastructure work you want to do. This guide compares all three in detail so you can make an informed decision.
The big picture: AWS vs Azure vs GCP at a glance
Before diving into specifics, here is the full comparison across every metric that matters for your career decision.
| Metric | AWS | Azure | GCP |
|---|---|---|---|
| Market share (2026) | ~32% | ~23% | ~11% |
| Job postings (% mentioning) | ~58% | ~42% | ~22% |
| Total services | 200+ | 200+ | 150+ |
| Free tier | 12-month free tier + always-free services | 12-month free tier + always-free services | 12-month free tier + always-free services + $300 credit |
| Data centre regions | 33+ | 60+ | 40+ |
| Top certifications | Solutions Architect Associate, DevOps Engineer Professional | Azure Administrator (AZ-104), Azure DevOps Engineer (AZ-400) | Cloud Engineer, Professional Cloud Architect |
| Key strength | Broadest services, largest ecosystem | Enterprise/Microsoft integration, hybrid cloud | Kubernetes-native, data/ML, developer experience |
| Key weakness | Complex pricing, console UX | Naming conventions, documentation gaps | Smaller market share, fewer enterprise features |
| Best for | Default choice, most jobs, startups to enterprise | Microsoft-heavy organisations, hybrid cloud | Data engineering, ML/AI, Kubernetes-first teams |
Now let's break down each area in depth.
Market share and job demand
Market share determines how many companies use a platform, which directly determines how many jobs exist for engineers who know it.
Current market share (2026)
| Provider | Global cloud market share | Year-over-year growth |
|---|---|---|
| AWS | ~32% | ~13% |
| Azure | ~23% | ~17% |
| GCP | ~11% | ~22% |
| Others | ~34% | varies |
AWS holds the largest share and has done so since cloud computing began. Azure is growing faster in percentage terms, driven by enterprise Microsoft contracts and AI partnerships. GCP is growing fastest but from a much smaller base.
What the job market looks like
Job postings tell a clearer story than market share alone. When we look at cloud and DevOps job listings across the UK and US:
- AWS appears in approximately 58% of cloud-related job postings
- Azure appears in approximately 42% of cloud-related job postings
- GCP appears in approximately 22% of cloud-related job postings
- Multi-cloud (two or more platforms) appears in approximately 35% of postings
Many postings mention multiple platforms, so these figures overlap. But the pattern is clear: AWS is mentioned most often, Azure is second, and GCP is a distant third by raw job volume.
Where each platform dominates
AWS dominates in: startups, scale-ups, SaaS companies, tech-forward enterprises, media and entertainment, gaming, and the broader startup ecosystem. If a company was founded in the last 15 years, it is likely on AWS.
Azure dominates in: large enterprises with existing Microsoft licensing (Office 365, Active Directory, SQL Server), government and public sector, financial services with legacy Windows infrastructure, and healthcare organisations.
GCP dominates in: data-heavy companies, ML/AI teams, Kubernetes-native organisations, and companies that heavily use the Google ecosystem (BigQuery, Looker, Vertex AI). It is also popular among developer-tooling companies.
The career implication: Learning AWS gives you access to the largest pool of job opportunities. But if you are targeting a specific industry (e.g. UK public sector or enterprise banking), Azure may be more relevant. If you want to specialise in data engineering or ML infrastructure, GCP is a strong choice.
Related reading: Cloud Computing Career Guide 2026
Service comparison: AWS vs Azure vs GCP
All three platforms offer equivalent core services. The names differ, but the concepts are the same. Here is the service mapping across key categories.
Compute
| Function | AWS | Azure | GCP |
|---|---|---|---|
| Virtual machines | EC2 | Virtual Machines | Compute Engine |
| Managed containers | ECS, Fargate | Container Apps | Cloud Run |
| Managed Kubernetes | EKS | AKS | GKE |
| Serverless functions | Lambda | Azure Functions | Cloud Functions |
| Batch processing | AWS Batch | Azure Batch | Batch on GKE |
Key difference: GKE (Google Kubernetes Engine) is widely considered the best managed Kubernetes service. Google created Kubernetes, and GKE consistently leads in features, reliability, and developer experience. AKS is a strong second. EKS is functional but has more operational overhead.
Storage
| Function | AWS | Azure | GCP |
|---|---|---|---|
| Object storage | S3 | Blob Storage | Cloud Storage |
| Block storage | EBS | Managed Disks | Persistent Disk |
| File storage | EFS | Azure Files | Filestore |
| Archive storage | S3 Glacier | Archive Storage | Archive Storage |
| Content delivery | CloudFront | Azure CDN / Front Door | Cloud CDN |
Key difference: S3 is the industry standard for object storage. Many third-party tools are "S3-compatible" by default. This means learning S3 gives you transferable knowledge to any S3-compatible storage system.
Networking
| Function | AWS | Azure | GCP |
|---|---|---|---|
| Virtual network | VPC | Virtual Network (VNet) | VPC |
| Load balancer | ALB / NLB | Azure Load Balancer / App Gateway | Cloud Load Balancing |
| DNS | Route 53 | Azure DNS | Cloud DNS |
| VPN / interconnect | Direct Connect | ExpressRoute | Cloud Interconnect |
| Firewall | Security Groups + NACLs | NSGs + Azure Firewall | Firewall Rules + Cloud Armor |
Key difference: GCP networking is globally distributed by default. A GCP VPC spans all regions automatically. AWS and Azure VPCs are regional. For globally distributed applications, GCP's networking model is simpler.
Databases
| Function | AWS | Azure | GCP |
|---|---|---|---|
| Managed relational DB | RDS (MySQL, PostgreSQL, etc.) | Azure SQL, Azure DB for PostgreSQL | Cloud SQL, AlloyDB |
| NoSQL document store | DynamoDB | Cosmos DB | Firestore |
| In-memory cache | ElastiCache | Azure Cache for Redis | Memorystore |
| Data warehouse | Redshift | Synapse Analytics | BigQuery |
| Globally distributed DB | DynamoDB Global Tables | Cosmos DB | Cloud Spanner |
Key difference: BigQuery is GCP's standout database service. It is a serverless, petabyte-scale data warehouse that is genuinely best-in-class. Cosmos DB is Azure's standout a multi-model, globally distributed database with flexible consistency levels. DynamoDB leads in simplicity and scale for key-value workloads.
AI and Machine Learning
| Function | AWS | Azure | GCP |
|---|---|---|---|
| ML platform | SageMaker | Azure Machine Learning | Vertex AI |
| Pre-built AI APIs | Rekognition, Comprehend, Polly | Cognitive Services | Vision AI, Natural Language AI |
| LLM-as-a-service | Bedrock | Azure OpenAI Service | Vertex AI (Gemini, PaLM) |
| GPU instances | P5 (H100), P4d (A100) | ND H100 v5, NC A100 v4 | A3 (H100), A2 (A100) |
| Custom silicon | Trainium, Inferentia | Maia 100 | TPUs (v5e, v5p) |
Key difference: Azure has an exclusive partnership with OpenAI, giving it unique access to GPT models through Azure OpenAI Service. GCP offers TPUs custom silicon designed for ML workloads which are unavailable anywhere else. AWS Bedrock provides model choice, with access to models from Anthropic, Meta, Mistral, and others.
Serverless
| Function | AWS | Azure | GCP |
|---|---|---|---|
| Functions | Lambda | Azure Functions | Cloud Functions |
| Container serverless | Fargate | Container Apps | Cloud Run |
| API gateway | API Gateway | API Management | API Gateway / Apigee |
| Event bus | EventBridge | Event Grid | Eventarc |
| Workflow orchestration | Step Functions | Logic Apps / Durable Functions | Workflows |
Key difference: Cloud Run is arguably the best serverless container platform. It runs any Docker container as a serverless service with zero infrastructure management. AWS Fargate achieves a similar result but with more configuration overhead.
Pricing comparison
Let's be honest: all three platforms are similarly priced for comparable workloads. The differences are in the details, not the fundamentals.
General pricing reality
- Compute costs are within 5-15% of each other for equivalent instance types
- Storage costs are nearly identical across providers
- Egress (data transfer out) is expensive on all three this is the hidden cost that catches teams off guard
- All three offer reserved/committed use discounts of 30-60%
Where pricing differs
| Pricing factor | AWS | Azure | GCP |
|---|---|---|---|
| Discount model | Reserved Instances, Savings Plans | Reserved Instances, Azure Hybrid Benefit | Committed Use Discounts, Sustained Use Discounts |
| Automatic discounts | None (you must commit) | None (you must commit) | Sustained Use Discounts apply automatically |
| Spot/preemptible pricing | Spot Instances (up to 90% off) | Spot VMs (up to 90% off) | Preemptible / Spot VMs (60-91% off) |
| Free tier | 12 months + always free | 12 months + always free | 12 months + always free + $300 credit |
| Egress (first 10TB/month) | $0.09/GB | $0.087/GB | $0.12/GB |
| Billing increment | Per-second (minimum 60s) | Per-minute | Per-second (minimum 60s) |
| Enterprise licensing | Pay-as-you-go or EDP | Azure Hybrid Benefit (use existing Microsoft licences) | Pay-as-you-go or CUD |
The real pricing takeaway
GCP's Sustained Use Discounts are genuinely useful you get automatic discounts for running instances more than 25% of the month, no commitment required. Azure Hybrid Benefit is a significant cost saver if your organisation already pays for Microsoft licences (Windows Server, SQL Server). AWS Savings Plans offer the most flexibility for committed spend.
For learning purposes, all three free tiers are generous enough to practise without spending money. GCP's additional $300 credit makes it the most generous for experimentation.
Certification paths
Certifications validate your cloud skills to employers. Here is how the certification paths compare across providers.
| Level | AWS | Azure | GCP |
|---|---|---|---|
| Entry-level | Cloud Practitioner | Azure Fundamentals (AZ-900) | Cloud Digital Leader |
| Associate (most valuable) | Solutions Architect Associate, SysOps Administrator | Azure Administrator (AZ-104), Azure Developer (AZ-204) | Associate Cloud Engineer |
| Professional | Solutions Architect Professional, DevOps Engineer Professional | Azure Solutions Architect Expert (AZ-305), Azure DevOps Engineer (AZ-400) | Professional Cloud Architect, Professional Cloud DevOps Engineer |
| Specialty | Security, Machine Learning, Networking, Database | Security Engineer, AI Engineer, Data Engineer | Professional Cloud Security Engineer, Professional Machine Learning Engineer |
| Exam cost | $100-$300 | $99-$330 | $99-$200 |
| Validity | 3 years | 1 year (renewal free via online assessment) | 2 years |
Which certification to get first
For most people: AWS Solutions Architect Associate. It is the most widely recognised cloud certification, appears in the most job postings, and covers the broadest range of cloud concepts. It signals to employers that you understand cloud architecture fundamentals.
If your target employer is a Microsoft shop: start with AZ-104 (Azure Administrator).
If you're targeting data/ML roles: consider the GCP Professional Cloud Architect or go directly for a specialty certification.
Important note: Certifications alone do not get you hired. They are most effective when combined with hands-on project experience. An AWS certification plus a portfolio of real projects is far more valuable than three certifications with no practical experience.
Related reading: Cloud Computing Career Guide 2026
When to choose each platform
Choose AWS when...
- You want the maximum number of job opportunities AWS appears in more job postings than any other cloud platform
- You're just starting out and want the most learning resources (tutorials, courses, community content)
- You're targeting startups, scale-ups, or tech companies the majority use AWS
- You want the broadest service catalogue to explore
- You plan to work in a multi-cloud environment and need the most transferable starting point
- You're interested in DevOps or platform engineering AWS is the default platform in most DevOps toolchains
Choose Azure when...
- Your target employer is a Microsoft-heavy enterprise (Office 365, Active Directory, SQL Server)
- You're targeting UK government or public sector roles many use Azure due to Microsoft licensing agreements
- You want to leverage Azure OpenAI Service for AI/ML projects
- Your organisation already has Microsoft Enterprise Agreements that include Azure credits
- You're working with .NET applications or Windows-based workloads
- You want strong hybrid cloud capabilities (Azure Arc, Azure Stack)
Choose GCP when...
- You want to specialise in Kubernetes GKE is the best managed Kubernetes service
- You're targeting data engineering roles BigQuery is best-in-class
- You want to work with TPUs for ML model training
- You're interested in a developer-first experience with cleaner APIs and documentation
- You're targeting companies that are Google ecosystem-heavy (BigQuery, Looker, Google Workspace)
- You want automatic cost optimisation (Sustained Use Discounts)
The multi-cloud reality
Here is something the certification vendors won't emphasise: most companies use more than one cloud platform.
A 2025 Flexera survey found that 87% of enterprises have a multi-cloud strategy. The most common combination is AWS + Azure, followed by AWS + GCP.
Why multi-cloud happens
- Acquisitions Company A uses AWS, acquires Company B on Azure. Now the combined entity runs both.
- Best-of-breed An organisation might use AWS for core infrastructure, BigQuery for analytics, and Azure OpenAI for AI features.
- Vendor negotiation Running workloads on multiple platforms gives leverage in pricing negotiations.
- Compliance Some data residency requirements push specific workloads to specific providers.
- Team preference Different teams within an organisation may choose different platforms for different projects.
What this means for your career
You don't need to be an expert in all three platforms. The practical reality is:
- Deep expertise in one (the one you build and operate in daily)
- Working familiarity with a second (enough to navigate, troubleshoot, and deploy)
- Awareness of the third (you know what it offers and when to use it)
This is why learning fundamentals matters more than memorising service names. If you understand VPC networking on AWS, you can learn Azure VNet or GCP VPC in days, not months. The concepts are identical; only the interfaces differ.
Related reading: DevOps Tools Guide 2026
AI infrastructure comparison
AI workloads are the fastest-growing category of cloud spend. Every major cloud provider is racing to offer the best GPU instances, ML platforms, and AI services. Here is how they compare.
GPU instances
| GPU | AWS Instance | Azure Instance | GCP Instance |
|---|---|---|---|
| NVIDIA H100 (80GB) | p5.48xlarge (8x H100) | ND H100 v5 (8x H100) | a3-highgpu-8g (8x H100) |
| NVIDIA A100 (80GB) | p4d.24xlarge (8x A100) | NC A100 v4 (4x A100) | a2-ultragpu-8g (8x A100) |
| NVIDIA L4 | g6.xlarge | NC T4 v3* | g2-standard-4 |
| NVIDIA T4 | g4dn.xlarge | NC T4 v3 | n1-standard-4 + T4 |
*Availability and naming may vary by region.
Custom AI silicon
- AWS: Trainium (training) and Inferentia (inference) custom chips optimised for deep learning. Cost-effective for supported model architectures. AWS Neuron SDK required.
- Azure: Maia 100 (announced, rolling out). Azure also benefits from the deepest partnership with NVIDIA for GPU supply.
- GCP: TPU v5e and v5p Tensor Processing Units designed for ML workloads. Best for JAX/TensorFlow workloads. Unique to GCP. Competitive pricing for large-scale training.
ML platform comparison
| Capability | AWS SageMaker | Azure ML | GCP Vertex AI |
|---|---|---|---|
| Notebook environment | SageMaker Studio | Azure ML Studio | Vertex AI Workbench |
| AutoML | SageMaker Autopilot | Automated ML | Vertex AI AutoML |
| Model registry | SageMaker Model Registry | Azure ML Model Registry | Vertex AI Model Registry |
| Pipeline orchestration | SageMaker Pipelines | Azure ML Pipelines | Vertex AI Pipelines |
| Feature store | SageMaker Feature Store | Azure ML Feature Store (preview) | Vertex AI Feature Store |
| Model monitoring | SageMaker Model Monitor | Azure ML monitoring | Vertex AI Model Monitoring |
| LLM hosting | SageMaker JumpStart, Bedrock | Azure OpenAI Service | Vertex AI Model Garden |
The AI infrastructure takeaway
Azure wins on LLM access thanks to the exclusive OpenAI partnership. If your team needs GPT-4 or similar models via API with enterprise security, Azure OpenAI Service is the clear choice.
GCP wins on ML tooling and custom silicon. TPUs offer a genuinely differentiated compute option, and Vertex AI is well-regarded for end-to-end ML workflows. BigQuery ML lets data analysts run ML models directly in SQL.
AWS wins on breadth and model choice. Bedrock offers access to models from multiple providers (Anthropic, Meta, Mistral, Cohere, and more). SageMaker is the most mature end-to-end ML platform. Trainium and Inferentia offer cost-effective alternatives for supported workloads.
For DevOps and infrastructure engineers entering the AI space, the platform matters less than the fundamentals: GPU scheduling on Kubernetes, model serving, monitoring inference latency, and managing GPU costs. These skills transfer across all three platforms.
Related reading: AI Infrastructure Explained
Our recommendation
Here is our clear recommendation, based on job demand, learning resources, and career versatility:
1. Start with AWS
AWS gives you the strongest foundation. It has the most jobs, the most community content, and the broadest service catalogue. The AWS Solutions Architect Associate certification is the most recognised cloud credential in the industry.
When you learn AWS, you learn cloud fundamentals that transfer everywhere: VPCs, IAM, load balancers, auto-scaling, object storage, managed databases, serverless, containers, Kubernetes. These concepts are identical on Azure and GCP the service names change, but the architecture patterns are the same.
2. Add a second platform based on your target employer
Once you're comfortable with AWS (3-6 months of hands-on work), add a second platform:
- Add Azure if you're targeting enterprise roles, UK public sector, Microsoft-heavy organisations, or roles that mention Azure OpenAI
- Add GCP if you're targeting data engineering, ML/AI roles, Kubernetes-heavy organisations, or developer-tooling companies
You don't need deep expertise in the second platform. Aim for working familiarity: you can navigate the console, deploy resources, and translate concepts from your primary platform.
3. Don't learn all three simultaneously
This is the most common mistake. Trying to learn AWS, Azure, and GCP at the same time means you learn none of them well. Cloud platforms are vast AWS alone has 200+ services. Depth beats breadth, especially when you're starting out.
Learn one deeply. Add others as your career requires. The fundamentals transfer.
Why CloudPros teaches on AWS
We chose AWS as our teaching platform because it provides the most job opportunities for our graduates. Our curriculum covers the full DevOps toolchain with AWS as the cloud foundation. The cloud concepts you learn networking, identity, compute, storage, orchestration apply to any platform you encounter in your career.
Related reading
- Cloud Computing Career Guide 2026 roles, salaries, and how to break into cloud computing
- DevOps Tools Guide 2026 the complete toolchain from Git to Kubernetes to MLOps
- AI Infrastructure Explained what powers every AI product and the careers it creates
- AWS vs Azure vs GCP: A Quick Comparison shorter blog version of this guide
Frequently Asked Questions
Ola
Founder, CloudPros
Building the most hands-on DevOps bootcamp for the AI era. 16 weeks of real infrastructure, real projects, real career outcomes.