AWS vs Azure vs GCP: Cloud Platform Comparison for Developers

Every developer eventually faces the cloud platform question. Your company picks one, or you're starting fresh and have to choose. The internet is full of vendor-sponsored comparisons that conveniently conclude their cloud is best. Here's an honest take from someone who's deployed production workloads on all three.

Market Share — Context Matters

As of early 2026, the split looks roughly like this:

Provider	Market Share	Strength
AWS	~31%	Most services, largest ecosystem
Azure	~25%	Enterprise/.NET integration
GCP	~11%	Developer experience, AI/ML

AWS has the first-mover advantage and the broadest service catalog. Azure rides the Microsoft enterprise wave. GCP is smaller but punches above its weight in specific areas. The remaining ~33% is a mix of smaller providers, private cloud, and multi-cloud setups.

Market share matters because it determines the size of the community, the number of Stack Overflow answers, and the pool of experienced engineers you can hire.

Compute — Running Your Code

The bread and butter. All three offer virtual machines, but the naming and defaults differ.

AWS EC2 has the most instance types — literally hundreds of combinations of CPU, memory, GPU, and networking. Powerful, but the sheer number of choices can be paralyzing. Graviton (ARM) instances offer excellent price-performance. Azure Virtual Machines integrate tightly with Active Directory and Windows Server. If your org runs Windows workloads, Azure VMs are the path of least resistance. Linux support is solid too — Azure actually runs more Linux VMs than Windows ones. Google Compute Engine has fewer instance types but lets you create custom machine types — pick your exact vCPU and memory ratio instead of choosing from a menu. Live migration during maintenance events means fewer interruptions.

For most developers, compute is a commodity. The real differences show up in the higher-level services.

Serverless — Functions as a Service

Here's the same HTTP-triggered function on all three platforms:

// AWS Lambda
export const handler = async (event) => {
  const name = event.queryStringParameters?.name || 'World';
  return {
    statusCode: 200,
    body: JSON.stringify({ message: Hello, ${name}! }),
  };
};

// Azure Functions
export default async function (request, context) {
  const name = request.query.get('name') || 'World';
  return {
    status: 200,
    jsonBody: { message: Hello, ${name}! },
  };
}

// Google Cloud Functions
import functions from '@google-cloud/functions-framework';

functions.http('helloWorld', (req, res) => {
  const name = req.query.name || 'World';
  res.json({ message: Hello, ${name}! });
});

All three do the same thing. The differences:

• Lambda has the largest ecosystem of triggers (S3, DynamoDB, SQS, EventBridge, basically everything in AWS). Cold starts have improved dramatically. 15-minute max execution.
• Azure Functions have the best local development story — the Functions Core Tools emulator is genuinely good. Durable Functions for orchestration workflows are a standout feature.
• Cloud Functions are the simplest to deploy. gcloud functions deploy and you're done. But fewer trigger options than Lambda.

Databases — Where It Gets Interesting

Managed relational:

• AWS RDS (Postgres, MySQL, etc.) + Aurora (AWS's custom engine, genuinely faster)
• Azure SQL Database (SQL Server managed) + Azure Database for PostgreSQL
• Cloud SQL (Postgres, MySQL) + AlloyDB (Google's PostgreSQL-compatible, competitive with Aurora)

NoSQL / document:

• DynamoDB (AWS) — single-digit millisecond latency, but you need to understand partition keys and access patterns upfront. Get this wrong and you're in pain.
• CosmosDB (Azure) — multi-model (document, graph, column, key-value), global distribution built in. More flexible than DynamoDB but more expensive.
• Firestore (GCP) — easiest to get started with, great for mobile/web apps. Less suited for complex backend queries.

In my experience, DynamoDB is the most battle-tested at scale, CosmosDB is the most flexible, and Firestore is the friendliest for smaller projects.

Containers and Kubernetes

This is where GCP shines, and it's not close.

GKE (Google Kubernetes Engine) is considered the gold standard. Google literally invented Kubernetes, and GKE gets features first. Autopilot mode handles node management. The integration with Google's networking is seamless. EKS (AWS) works fine but feels bolted on. Cluster setup is more manual, networking with VPCs requires more configuration, and you'll likely need additional tooling. That said, AWS's massive ecosystem means your other services integrate well. AKS (Azure) has improved significantly and is solid for teams already in the Azure ecosystem. Good integration with Azure DevOps and Active Directory.

If Kubernetes is central to your architecture, GCP has a real advantage here.

Storage

Object storage is effectively a commodity:

Feature	AWS S3	Azure Blob Storage	Google Cloud Storage
Durability	99.999999999%	99.999999999%	99.999999999%
Free tier	5 GB / 12 months	5 GB / 12 months	5 GB / always free
Pricing (per GB/mo)	$0.023	$0.018	$0.020
Ecosystem	Largest (S3 is the de facto standard)	Good	Good

S3's API has become the unofficial standard — many third-party tools speak "S3-compatible." That ecosystem advantage is real.

AI and Machine Learning

Google leads here. Vertex AI, pre-trained models, TPU access, and the fact that Google publishes many of the foundational ML research papers gives GCP a genuine edge for ML workloads. BigQuery ML lets you run models directly in your data warehouse with SQL.

AWS SageMaker is comprehensive and has more enterprise deployments. It's more complex to set up but covers the full ML lifecycle. Bedrock provides access to foundation models from multiple providers.

Azure ML integrates with the Microsoft ecosystem and offers Azure OpenAI Service — if you want GPT models with enterprise compliance, Azure is the only option through Microsoft's partnership with OpenAI.

For hobbyist ML: GCP. For enterprise ML pipelines: any of them work. For OpenAI models specifically: Azure.

Pricing — The Honest Truth

All three use pay-as-you-go pricing, and all three are complicated enough that your actual bill will surprise you.

Free tiers compared:

Service	AWS	Azure	GCP
Compute	750 hrs t2.micro / 12 mo	750 hrs B1s / 12 mo	e2-micro always free
Functions	1M requests/mo free	1M requests/mo free	2M requests/mo free
Storage	5 GB S3 / 12 mo	5 GB Blob / 12 mo	5 GB always free
Database	750 hrs RDS / 12 mo	250 GB SQL / 12 mo	1 GB Firestore always free

GCP's "always free" tier is genuinely more generous than the 12-month trials from AWS and Azure. For personal projects and learning, GCP costs less.

At scale, pricing depends entirely on your usage patterns. AWS and Azure offer aggressive enterprise discounts. GCP's sustained-use discounts apply automatically without commitments. Get quotes for your specific workload — generic comparisons are meaningless at enterprise scale.

Developer Experience

AWS has the most services (200+), which means you can build anything but finding the right service takes research. The console is functional but overwhelming. Documentation is thorough but verbose. CloudFormation YAML will test your patience — most teams use CDK or Terraform instead. Azure is the best choice if your team is already in the Microsoft ecosystem. Visual Studio integration, .NET optimization, Active Directory, Teams, Office 365 — it all connects. For non-Microsoft stacks, Azure feels more friction-heavy. GCP has the best developer experience, in my opinion. The console is cleaner, gcloud CLI is intuitive, and the documentation is more developer-friendly. Firebase for web/mobile apps is excellent. The tradeoff is fewer services and a smaller community.

Certifications

All three certifications have value, but AWS certifications are the most recognized simply because of market share. If you're unsure, AWS Solutions Architect Associate is the safest bet for your resume. Azure certifications are valuable in enterprise/Microsoft shops. GCP certifications are less common but signal deeper technical knowledge.

The Verdict

Here's the thing — the "best" cloud platform is the one your team already knows, or the one your employer uses. Switching costs are real, and the differences between platforms matter far less than your ability to use one well.

If you're starting fresh:

• Choose AWS if you want the most services, the largest community, and the most job opportunities


• Choose Azure if you're in a Microsoft/.NET shop or enterprise environment


• Choose GCP if you prioritize developer experience, run Kubernetes-heavy workloads, or focus on ML/AI

All three platforms have interactive tutorials and exercises on CodeUp — you can practice deploying serverless functions, configuring databases, and setting up container orchestration without racking up a cloud bill.

Don't overthink the choice. Pick one, learn it deeply, and pick up the others as needed. The concepts transfer — once you understand load balancers, IAM, and managed databases on one platform, the others are just different UIs for the same ideas.