AWS vs GCP vs Azure 2026: Honest Dev Comparison

The surprise cloud bill

The classic way to learn cloud pricing is a shock bill on a side project. A NAT Gateway left running, an RDS instance provisioned "just to test something," a couple of Lambda functions stuck in a retry loop over a weekend, and no budget alert configured. Tens of thousands of rupees later, the credit card statement is the first warning.

It's not unique to AWS. Every major provider has its traps: a BigQuery scan run against the wrong huge table on GCP, or a Cosmos DB instance on Azure that turns out not to be on the free tier. Cloud billing is confusing, and the providers don't seem to mind.

So picking a cloud provider comes down to features, performance and understanding the pricing model well enough that it doesn't bite you. This is an opinionated comparison of what matters for developers and startups in India. All three are good, and all three work. Which one is "best" depends far more on your situation than on any technical superiority one might claim over the others.

Compute

Virtual machines

Every cloud starts with VMs: spin up a server, SSH in, deploy your stuff.

AWS has the widest variety of instance types, which matters when your workload has very specific requirements; I once needed a memory-optimized instance with exactly 512 GB RAM for an in-memory cache, and only AWS had that spec. GCP's e2-micro is permanently free. It doesn't expire after 12 months, so it's ideal for small always-on services. Azure has three India regions. That helps with latency-sensitive deployments.

For general workloads, I find the performance differences negligible. Pick on ecosystem, pricing, or whatever your team already knows. If you're just learning, GCP's permanent free tier VM is hard to beat, and it's probably the best place to start without worrying about a surprise bill.

Managed containers

If you're running Docker containers (and in 2026 most applications probably should be), all three have managed container services. New to containerization? Our Docker and Kubernetes beginner's guide walks through the fundamentals before you start choosing providers.

AWS ECS/Fargate is the default choice on AWS. Fargate gives you serverless containers, where you define CPU and memory and AWS handles the infrastructure, and it works well, although the configuration is verbose and you'll write a lot of YAML. Fargate task definitions can end up longer than the application code they deploy.

GCP Cloud Run is the simplest of the lot. You push a Docker image and get a URL. It scales to zero when there's no traffic (you don't pay when nobody's hitting it), which makes it perfect for side projects and APIs with variable traffic.

Azure Container Apps is Azure's answer to Cloud Run. It's quite good. It runs on Kubernetes under the hood but hides the complexity, and it supports Dapr for microservices communication out of the box, which is a nice touch.

For most developers, GCP Cloud Run wins on simplicity. It's the fastest path from "I have a Docker image" to "it's running in production with a public URL," and a simple container can be live in a couple of minutes.

Serverless functions

With serverless, you write a function and the cloud provider handles everything else: scaling, infrastructure, patching. You pay only when the function runs. It sounds great until you get a retry loop on a weekend, but that's my trauma, not yours.

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

AWS Lambda is the most mature option. It has the deepest integration with other AWS services (S3, DynamoDB, SQS, SNS), so if you're already on AWS, Lambda is the natural fit and you'll rarely have a reason to look elsewhere.

GCP Cloud Run functions (Google renamed Cloud Functions in 2024) run on Cloud Run under the hood, so you get container benefits with function simplicity, and the 60-minute execution limit helps with data processing jobs that would time out on Lambda.

Azure Functions has the roughest cold starts, and I'm not sure that's improved much recently. It does give you the most hosting flexibility: the same function code can run on a Consumption plan (serverless), a Premium plan (pre-warmed instances), or your own App Service plan. For .NET developers it's the most natural fit by far.

Databases

This is where the differences start to matter. Your database choice often locks you into an ecosystem. Think carefully.

Relational databases

GCP Cloud SQL is cheapest. The db-f1-micro instance with 0.6 GB RAM is enough for development and small production workloads, at around Rs 600/month. AWS Aurora Serverless v2 is the best option for variable workloads because it scales compute up and down automatically with demand, though its minimum cost is higher.

If you're using PostgreSQL, all three support it well. I think you should seriously consider it as your default database in 2026, and my personal preference is GCP Cloud SQL for PostgreSQL when cost efficiency matters and AWS Aurora PostgreSQL for performance-critical applications.

NoSQL and document databases

AWS DynamoDB is a beast: single-digit millisecond latency at any scale, pay-per-request pricing, and zero operational overhead. The learning curve is steep, because you have to design access patterns upfront instead of querying however you like as you would with SQL. Once it clicks, DynamoDB is impressive. It's probably my favorite AWS service.

GCP Firestore is easier to learn than DynamoDB, with a more intuitive query model and real-time sync built in, and it's the default choice for Firebase applications, though it can't match DynamoDB's raw throughput for write-heavy workloads.

Azure Cosmos DB supports multiple APIs (SQL, MongoDB, Cassandra, Gremlin, Table), which neither of the other two offers. If you need global distribution with multi-region writes, Cosmos DB does it better than the competition. It's also the most expensive. The pricing model seems designed to confuse, and I suspect that's intentional.

Storage

Object storage is practically a commodity. All three offer the same core functionality, and for most use cases the choice between them doesn't matter much.

At small scale the differences are minimal. At petabyte scale, Azure's slightly lower per-GB cost adds up. GCP's always-free 5 GB is nice for personal projects that will run indefinitely.

S3's API has become a de facto standard that most tools and libraries support natively, and because GCP offers S3 compatibility, you can often switch without code changes. Azure uses its own API. That means more migration effort if you ever need to move, so keep it in mind.

AI/ML services

GCP leads here, clearly.

GCP Vertex AI is the most developer-friendly ML platform on any provider, with AutoML for no-code model training, the Gemini API for generative AI, and tight integration with BigQuery for data analysis. If AI is central to your product, GCP is the strongest choice, and Google's research heritage shows in the quality and breadth of its ML services more than anywhere else, I think.

AWS SageMaker is more mature. It has a larger ecosystem of pre-built models and algorithms, and the recent SageMaker Studio overhaul made it more accessible, but it's still more complex than Vertex AI. Bedrock gives you multiple foundation models (Claude, Llama, Titan) through a unified API, which is nice if you don't want to commit to one.

Azure OpenAI Service gives you OpenAI's current models, including the GPT-5 family, the gpt-image models and Whisper, on Azure's enterprise infrastructure. If you need OpenAI's models with Azure's compliance and security guarantees, it's the only option, and many large Indian enterprises pick Azure specifically for this. I can see why.

Kubernetes: EKS vs GKE vs AKS

This might be controversial, but you probably shouldn't run Kubernetes unless you have a team of at least 3-4 engineers to manage it. If you're doing it anyway, Google has a clear advantage. They invented Kubernetes. GKE reflects that pedigree.

GKE (Google Kubernetes Engine) has an Autopilot mode that manages node pools automatically, and it's the smoothest Kubernetes experience of the three, because node auto-upgrades, built-in monitoring with Google Cloud Operations, and the fastest cluster provisioning times mean less fiddling.

EKS (Elastic Kubernetes Service) is solid but expensive. The control plane costs $0.10/hour ($73/month) on top of your node costs, and it needs more manual setup, especially around networking and IAM, which isn't terrible but you notice it next to GKE.

AKS (Azure Kubernetes Service) has a Free tier with no cluster management fee, which is a significant cost advantage for dev and test clusters, although the Standard tier you'd want for production (it adds a financially backed uptime SLA) is paid. Its integration with Azure Active Directory for RBAC is excellent for enterprise environments. Performance is comparable to EKS.

If Kubernetes is non-negotiable, go with GKE. If you can avoid it entirely, use Cloud Run (GCP), Fargate (AWS), or Container Apps (Azure) instead, because Kubernetes is powerful but costs a lot of engineering time to operate, arguably more than the infrastructure itself. For a broader look at how cloud architecture fits into larger systems, see our system design interview preparation guide.

Pricing for common workloads

Typical workloads, priced in India regions:

Small web app (1 server, database, storage)

Serverless API (1M requests/month)

GCP wins on serverless pricing because Cloud Run includes an HTTP endpoint without a separate API Gateway charge, while AWS bills $3.50/month for API Gateway even at low volumes. Across projects, that adds up.

Free tier comparison

The free tier matters enormously for students and developers learning cloud skills, and if you get it wrong, you end up paying for an education that should have been free.

For ongoing use, GCP's always-free tier wins. AWS dropped its 12-month free tier for accounts created from July 15, 2025, so new accounts get up to $200 in credits that last six months at most, and t2.micro is no longer the free-tier instance to look for; on the Free plan, the account closes when the credits or the six months run out unless you upgrade to the Paid plan. Azure's 12-month free services are more powerful but expire. When they do, charges start immediately, with no grace period and no reminder email that gets your attention. The $300 GCP credit and $200 Azure credit are great for experimenting with expensive services like GPUs and managed databases. Spend them wisely. Set a budget alert on day one.

Which to learn for jobs in India

This is probably why many of you are reading, so I'll be blunt: learn AWS first.

AWS held about 28% of the global cloud infrastructure market in Q2 2026, according to Synergy Research Group, and an even larger share in India. Most Indian startups, mid-size companies, and even large enterprises run on it. Pull up job listings on Naukri, LinkedIn, or Indeed for cloud roles in Bangalore, Hyderabad, or Pune and AWS skills show up 2-3x more often than GCP or Azure, which is a big gap.

Azure dominates in large Indian enterprises that run Microsoft products: banks, insurance companies, government agencies, and IT services companies like TCS, Infosys, and Wipro. If you're targeting enterprise IT roles at those firms, Azure expertise is arguably worth more than AWS.

GCP tends to show up at data-centric companies and startups with modern tech stacks. Aiming for Flipkart, PhonePe or data engineering roles? GCP skills will stand out on your resume.

Certification paths

Azure has the cheapest entry-level exam, and Microsoft often hands out free vouchers through events and learning paths, so watch for those. AWS certifications carry the most market recognition in India. GCP's are gaining ground in data and ML. If I had to pick one certification for maximum job market impact in India right now, it would be the AWS Solutions Architect Associate, by a wide margin.

Startup credit programs

Founding something? Read this part. All three providers offer generous credits for startups, and these can save you lakhs of rupees in the early stages.

AWS Activate gives up to $100,000 in credits for startups in accelerator programs and $10,000 for self-serve applications, plus credits for business support plans and training.

The Google for Startups Cloud Program offers up to $200,000 in GCP credits over two years, along with technical support and access to Google's startup network. It's generally the most generous of the three.

Microsoft for Startups (Founders Hub) gives up to $150,000 in Azure credits and free access to GitHub Enterprise, Visual Studio Enterprise, and Microsoft 365. People tend to overlook those extra software credits. They're a nice bonus.

My advice for Indian startups is to apply to all three and see which programs accept you, then spend the credits strategically, on expensive services first and on things you'd pay for regardless. You can always migrate later. Experience with multiple providers doesn't hurt.

My picks, by situation

After running production workloads on all three, this is how I'd rank them:

For startups and side projects, GCP. It has the most generous free tier, Cloud Run makes deployment trivial, the pricing is the most predictable, and BigQuery is unmatched for ad hoc data analysis, to the point where nothing else comes close.

For career advancement in India, AWS. The job market demands it. Get the Solutions Architect Associate certification, which is probably the single most impactful cloud certification for Indian tech careers; plenty of hiring managers filter for it specifically.

For enterprise environments, Azure. Its integration with Active Directory, Office 365, Teams, and Power BI runs deep, and Indian enterprises are already heavily invested in Microsoft. If you work at or sell to a large Indian company, Azure knowledge pays off.

For AI/ML projects, it depends on the models: GCP if you want Google's models and Vertex AI, Azure if you need OpenAI's GPT models, and AWS if you want the flexibility of Bedrock's multi-model approach.

Comparison articles usually dodge this, but the core services across all three are converging. A load balancer works the same way everywhere. Object storage is object storage. The differences are in developer experience, pricing surprises, and ecosystem integrations. Networking, compute, storage, databases, and IAM are universal concepts, so if you pick one provider and learn it deeply, most of that knowledge transfers when you need the others.

If you're in India, wondering where to start, and you want to maximize your employability, the answer is AWS. Branch out once that foundation is solid.