In the previous tutorials, we explored how running software in the cloud isn’t just about where it lives, it’s also about how much of it you want to manage. Now that you’ve seen the different levels of control cloud services offer, the next logical step is figuring out who provides that control, and how.

That’s where the three major cloud providers, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), come into the picture. Each of these platforms delivers the core building blocks of modern infrastructure: virtual machines, managed databases, scalable storage, and serverless tools.

Although their offerings are often similar, differences emerge in how these services are packaged, priced, and integrated. Some platforms stand out for enterprise-ready features; others for developer-friendly interfaces or advanced data tools. In reality, AWS, Azure, and GCP are interchangeable for most workloads, especially when it comes to foundational services like compute and storage.

Rather than declaring a single platform as best, many teams mix and match based on their needs—using AWS for compute-heavy workloads, Azure for internal business apps, and GCP for analytics. Often, price, convenience, and team familiarity are bigger deciding factors than individual service capabilities.

In this tutorial, we’ll explore:

• The deployment models (public, private, hybrid) supported by AWS, Azure, and GCP, and how each platform approaches global reach, on-prem integration, and multi-cloud flexibility
• The core cloud services each provider offers — from virtual machines and object storage to API platforms, serverless compute, and managed Kubernetes — and how these shape modern development workflows
• The tools, tradeoffs, and pricing models that influence each platform’s experience — including autoscaling strategies, usage-based billing, and support for legacy systems
• Real-world examples of how organizations like Netflix, Spotify, HP, Dataquest, and PayPal are using these platforms to launch, scale, and optimize their systems

By the end, you’ll have a grounded understanding of how each provider builds its ecosystem — and a practical sense of which services and strategies are best suited to your next cloud project, whether you’re deploying your first web app or scaling a multi-region system.

Let’s dive in.

Understanding Cloud Providers and Why They Matter

Cloud computing isn’t just about hosting; it’s about balancing control, convenience, and scalability. Once you determine the type of cloud infrastructure you need, the next key consideration is this: who provides the cloud is just as important as the model you choose.

Just like choosing a place to live, it’s not only about whether you want a full house, a furnished apartment, or a hotel room; it’s also about who’s managing the property, what services they offer, how quickly they respond, and how well they fit your daily needs.

That’s what makes understanding cloud providers so important.

Whether you’re building a small app, managing enterprise data, or launching AI-powered tools, your provider shapes how fast you can move, what tools are at your fingertips, and how much you’ll need to worry about behind the scenes.

Why Cloud Providers Matter

Let’s say your team is preparing to launch a containerized application with real-time analytics.

• Do you want the broadest possible service offerings with global scalability and strong third-party integrations?
• Are you working in a Windows-heavy environment that requires deep Microsoft compatibility?
• Or do you need powerful tools for machine learning and data pipelines, with a strong focus on automation and cost efficiency?

These kinds of questions point not just to how you deploy your stack, but where.

Each of the big three providers brings something different to the table:

• Amazon Web Services (AWS): Known for its extensive service catalog and global reach. It’s highly customizable, but that flexibility can come with added complexity. AWS excels when you need fine-grained control over infrastructure and access to a wide range of integrated services — though many of these features overlap with GCP.
• Microsoft Azure: A go-to for organizations already embedded in the Microsoft ecosystem. It supports seamless integration with Windows Server, Active Directory, and Office tools, making it especially attractive for hybrid cloud and enterprise IT environments.
• Google Cloud Platform (GCP): Favored by teams focused on data engineering, machine learning, and containerized apps. GCP stands out for its intuitive developer experience and strength in analytics, but its core infrastructure offerings are largely on par with AWS for most modern workloads.

Here’s the key: it’s not always about picking one. Many teams use a mix: AWS for compute-heavy workloads, Azure for internal systems, GCP for analytics, and often the choice comes down to price, team experience, or just what your developers are most comfortable with.

In the sections that follow, we’ll walk through how each of the dominant cloud providers ,AWS, Azure, and GCP, delivers services in practice.

Amazon Web Services (AWS)

When people talk about cloud computing, Amazon Web Services (AWS) is often one of the first names to come up — and for good reason. As one of the earliest players in the public cloud space, AWS helped shape how infrastructure is delivered and how organizations approach scaling modern applications.

Since its launch in 2006 with foundational services like S3 and EC2, AWS has grown into a massive cloud ecosystem. It now offers over 250 services, spanning compute, storage, databases, analytics, AI/ML, networking, developer tools, and even robotics and quantum computing. This wide surface area gives AWS a flexible platform, but it also introduces complexity — especially for new teams navigating its sheer depth.

Global Reach and Deployment Flexibility

One of AWS’s longstanding advantages is its global infrastructure. With 36 regions and 100+ availability zones, plus edge locations and specialized government clouds, AWS offers broad geographic coverage. This is particularly useful for companies operating across borders or those with strict data sovereignty requirements.

Notable users span industries and use cases, from streaming platforms like Netflix and Airbnb, to enterprises like Intuit and Coca-Cola. Dataquest, for example, uses AWS for core backend services, including code execution via API Gateway, scalable storage through S3, and data pipeline management via Redshift, ensuring fast and reliable access to learning content worldwide.

Many developer platforms: such as Heroku, Vercel, and Railway, also run on AWS behind the scenes, meaning developers often benefit from AWS performance and scale without managing AWS accounts directly.

AWS supports all major cloud deployment models, public, private, and hybrid:

• The public cloud is AWS’s standard model, with services like EC2, S3, and Lambda available globally.
• Hybrid workloads are supported via AWS Outposts, which bring AWS infrastructure to on-prem environments.
• For private cloud use cases, Virtual Private Cloud (VPC) allows users to create isolated environments with full network control.

While Azure has historically led the hybrid narrative, AWS now offers robust options across the board — making it equally capable for cloud-native builds, hybrid migrations, and enterprise-scale deployments.

Core AWS Services

Below, we’ll explore a curated set of AWS services, from compute and storage to API management and virtual desktops, to understand how they work and where they fit into modern cloud architectures.

i. Simple Storage Service(S3)

Amazon S3 (Simple Storage Service) is AWS’s infinitely scalable, highly durable object storage system — built to store anything from images and PDFs to backups and large datasets. You don’t manage servers, disks, or file systems. Instead, you interact with a simple API: upload files, retrieve them later, and let S3 handle the rest. It’s like a bottomless, ultra-secure cabinet in the cloud, designed to grow with your application without ever needing your attention.

Working with S3 starts by creating a bucket, AWS’s version of a cloud folder. You upload files using unique keys (like filenames), and from that point on, AWS takes over. It handles infrastructure, redundancy, encryption, scaling, and availability behind the scenes. You control the structure, access policies, and storage behavior — from lifecycle rules that clean up old data to versioning that preserves changes over time. Whether you’re enforcing security, saving storage costs, or serving files on demand, S3 does the heavy lifting so you can focus on building.

ii. Elastic Compute Cloud(EC2)

Amazon EC2 (Elastic Compute Cloud) gives you full control over virtual servers in the cloud — the kind of control you’d expect from running your own hardware, minus the physical maintenance. With EC2, you choose your operating system, configure your environment, install dependencies, and deploy applications exactly the way you want. Whether you’re hosting a full-stack web app, spinning up a database server, or running machine learning workloads, EC2 is the raw compute foundation that lets you build and scale on your terms.

You begin by launching an EC2 instance: a virtual machine configured to your specs. From there, you manage everything above the hardware layer: the OS, security patches, web server setup, runtime environments, and any services your application needs. Meanwhile, AWS handles the infrastructure behind the scenes, provisioning the physical servers, managing the network, distributing workloads across regions, and ensuring high availability. EC2 shines when you need flexibility, performance tuning, and the freedom to architect your stack without platform constraints, it’s IaaS at its most powerful.

iii. AWS Elastic Beanstalk

AWS Elastic Beanstalk is a platform-as-a-service (PaaS) offering that lets you deploy web applications without worrying about the infrastructure beneath them. You write your code — in Python, Node.js, Java, or another supported language, and Beanstalk handles the rest: provisioning servers, setting up load balancers, configuring the runtime, and scaling the environment automatically. It’s built for developers who want deployment to “just work” so they can focus on shipping features, not managing servers.

Deploying with Elastic Beanstalk is as simple as uploading your code bundle or connecting it to a Git repo. Beanstalk spins up the underlying EC2 instances, sets environment variables, installs dependencies, and even monitors performance. You still have control when you need it, you can customize the environment, tweak instance types, or hook into the deployment process, but you’re no longer managing the core infrastructure. Beanstalk strikes a powerful balance: full application-level control, with infrastructure abstracted just enough to help you move faster.

iv. Amazon API Gateway

Amazon API Gateway is a fully managed service that makes it easy to create, publish, and secure APIs at any scale. Acting as the front door to your cloud-based applications, it handles all the hard parts of API management, routing requests, enforcing access controls, throttling traffic, and transforming responses, without requiring you to manage servers or networking infrastructure. Whether you’re building a REST API for a mobile app, connecting a frontend to serverless functions, or exposing internal microservices, API Gateway provides the bridge between clients and the services they depend on.

You define your API endpoints and configure how they connect to backend services, like AWS Lambda, EC2, or even third-party URLs. From there, API Gateway takes over: it handles request validation, usage metering, logging, authentication via IAM, and even real-time WebSocket communication if needed. You stay focused on designing and scaling your application, while API Gateway ensures your APIs remain secure, performant, and reliable. It’s the essence of PaaS: delivering full functionality and control without exposing you to the infrastructure underneath.

v. Amazon WorkSpaces

Amazon WorkSpaces is a fully managed Desktop-as-a-Service (DaaS) solution that lets you provision virtual desktops for users in just a few clicks. Instead of issuing physical laptops or managing on-prem desktops, you provide secure, cloud-based work environments that users can access from anywhere, complete with persistent storage, access policies, and customizable compute power. Whether you’re onboarding remote employees, setting up temporary student labs, or enabling secure contractor access, WorkSpaces delivers Windows or Linux desktops at scale without managing hardware.

You choose a desktop bundle (OS, CPU, memory, storage), define user access through AWS Directory Service, and WorkSpaces handles the provisioning, patching, and maintenance behind the scenes. Users simply log in via a web browser or native client, and they’re in — with consistent performance and seamless integration with existing systems. It’s infrastructure, but purpose-built for desktops: AWS manages the virtualization, networking, and availability, while you retain control over desktop images, security policies, and user experience. WorkSpaces combines the flexibility of IaaS with the simplicity of SaaS, making it a powerful solution for scalable, secure desktop delivery.

Pricing and Cost Considerations

AWS uses a pay-as-you-go model with extensive pricing granularity across compute, storage, data transfer, and more. While this allows fine-tuned control, it can also result in unexpected costs if not monitored closely.

• AWS tends to sit in the middle of the pack pricing-wise.
• Azure may offer better baseline pricing in Microsoft-centric organizations.
• GCP’s commitment-based pricing can be more straightforward in some cases.

AWS does offer tools like Cost Explorer, Budgets, and Savings Plans, but support plans come at an additional cost and are tiered separately.

When Does AWS Make Sense?

AWS is a solid choice when:

• You need global reach with a wide array of services
• Your architecture requires fine-tuned infrastructure control
• You have experienced DevOps engineers or access to cloud architects
• You’re migrating enterprise workloads and want flexibility across systems

That said, AWS’s sheer breadth can be overwhelming for small teams or new developers. In those cases, higher-level platforms like Heroku, Railway, or Vercel, which build on AWS—offer a more streamlined experience with less overhead.

Strengths and Limitations of AWS

Strengths	Limitations
Extensive service catalog (250+), covering a wide range of use cases	Steep learning curve for new users
Strong global infrastructure and regional availability	Costs can escalate without careful monitoring
Integration with countless third-party tools and APIs	Tiered support adds cost overhead
Highly customizable and scalable environments	Less hybrid-native than Azure

Microsoft Azure

If AWS leads with breadth, Microsoft Azure competes with a deep ecosystem integration — particularly appealing to enterprises already running Windows Server, SQL Server, Active Directory, or Microsoft 365. As the second-largest public cloud provider globally, Azure has built strong momentum with governments, regulated industries, and hybrid adopters.

Launched in 2010, Azure now offers 200+ services covering everything from virtual machines and serverless compute to IoT, AI, and enterprise-grade analytics. It supports a broad range of tools and languages — including Linux, Python, JavaScript, and open-source databases — but its strengths are especially clear when integrated with existing Microsoft environments.

Global Reach and Deployment Flexibility

Microsoft Azure operates in more regions than any other cloud provider, with 64 global regions and 126 availability zones, plus specialized environments for government, education, and regulated industries. This global coverage, paired with Azure’s deep compliance framework, makes it a strong option for organizations with complex geographic or regulatory requirements.

Notable customers span industries such as retail, healthcare, and public sector — including HP, Asos, Mitsubishi Electric, the CDC, and the UK’s NHS. Many of these rely on Azure for both cloud-native applications and hybrid scenarios that connect on-prem infrastructure to the cloud. Azure’s ecosystem also powers widely adopted SaaS products like Microsoft 365, Teams, Dynamics 365, and Power BI — meaning many organizations are using Azure even if indirectly.

Azure supports all three major cloud deployment models — public, private, and hybrid:

• The public cloud is the standard, offering managed services like Azure VMs, SQL Database, and Blob Storage through Microsoft-owned data centers.
• For private cloud, Azure Stack brings Azure services on-premises with the same APIs and management tools.
• Hybrid and multi-cloud capabilities are supported through Azure Arc, which lets you manage resources across on-prem, Azure, and even other

This makes Azure particularly useful for enterprises transitioning from traditional IT systems — especially when a full migration to the public cloud isn’t possible or practical.

Core Azure Services

In this section, we’ll look at a selection of Azure services: including compute, object storage, web app hosting, serverless functions, and analytics — to understand how they’re used to power scalable, secure, and enterprise-ready cloud solutions.

i. Azure Virtual Machines

Azure Virtual Machines (VMs) let you run full operating systems in the cloud with the same flexibility you’d expect from physical hardware. Whether you’re deploying a production-grade web app, running enterprise software, or building a test environment, Azure VMs give you total control — from OS selection to resource allocation and network configuration. It’s IaaS in its rawest form: you design the machine, Azure supplies the rack.

You begin by choosing a VM image, maybe Windows Server, Ubuntu, or Red Hat, and customizing specs like CPU, memory, and disk size. From there, you install software, configure security, and deploy workloads just like you would on a physical server. Meanwhile, Azure handles the hypervisor layer, high availability, networking backbone, and physical hardware maintenance. With VMs, you get full-stack flexibility and the ability to scale horizontally across global regions — without worrying about the data center underneath.

ii. Azure Blob Storage

Azure Blob Storage is Microsoft’s object storage solution for massive-scale unstructured data — images, video, documents, backups, or any other binary files. You don’t deal with disks or filesystems: just create a container, upload your blobs, and access them through HTTP or SDKs. Whether you’re hosting a media library, backing up databases, or serving static web content, Blob Storage acts like an infinite cloud locker you’ll never outgrow.

Once you define a storage account and container, your app interacts with blobs using keys, similar to file paths. Azure takes care of redundancy, high availability, and performance tiers, from hot access to cold archive. You manage access with role-based access control (RBAC), shared access signatures, or private endpoints, while Azure quietly handles replication, durability, and scaling behind the scenes. It’s pure IaaS — powerful, flexible, and invisible when it’s working well.

iii. Azure App Service

Azure App Service is a fully managed PaaS platform that lets you deploy web apps and APIs directly from your codebase — no server setup, patching, or scaling configuration required. You focus on your app, and Azure takes care of the platform: load balancing, SSL, auto-scaling, and deployment pipelines. Whether you’re building in .NET, Node.js, Python, or PHP, it’s one of the easiest ways to ship production-grade apps in the Microsoft cloud.

You push code from GitHub, Azure DevOps, or your local machine, and App Service provisions the underlying infrastructure, assigns a custom domain, and gets your app online. You can set environment variables, define deployment slots (for blue/green testing), and scale based on traffic with just a few clicks. It’s platform power without the overhead — ideal for startups, internal tools, and scalable APIs alike.

iv. Azure Functions

Azure Functions brings serverless computing to the forefront — allowing you to run small blocks of code in response to events, without provisioning any servers. It’s PaaS with even more abstraction: you don’t deploy an app or run a server — you write a function, define its trigger (HTTP, timer, queue, blob, etc.), and Azure takes care of everything else.

When an event occurs — like an uploaded file, a new message in a queue, or a scheduled time — your function fires and executes your logic. Billing is based on execution time and resources used, making it incredibly cost-efficient for microservices, automation scripts, and lightweight integrations. You can chain functions together, integrate with APIs, or run background tasks — all without managing the runtime environment.

v. Microsoft Power BI

Power BI is Microsoft’s flagship SaaS product for business analytics — delivering dashboards, reports, and visual insights from your data, no code required. You connect it to Excel files, databases, or cloud data sources like Azure SQL or SharePoint, and Power BI transforms your raw data into interactive visuals your team can explore and share across web and mobile devices.

As a SaaS platform, Power BI handles the entire analytics stack — hosting, processing, collaboration, and sharing — so you don’t manage infrastructure or worry about deployments. You build your reports in Power BI Desktop, publish to the Power BI service, and set up refresh schedules, access controls, and dashboards. It’s perfect for decision-makers, analysts, and teams who need powerful insights fast, with no setup complexity.

Pricing and Cost Considerations

Azure offers competitive pricing, particularly for companies already licensing Microsoft products. If you use Windows Server, SQL Server, or Visual Studio, you can bring those licenses to Azure and unlock significant discounts, through models like Azure Hybrid Benefit.

Azure is generally considered the most cost-effective option for:

• Organizations that are already tied to Microsoft
• Teams looking for flexible hybrid solutions
• Companies that need enterprise support but want to control spending

It also provides pricing calculators, budgets, and cost optimization tools — similar to AWS and GCP — and offers pay-as-you-go, reserved instances, and spot pricing for flexible billing.

When Does Azure Make Sense?

Azure works especially well when:

• You’re already invested in Microsoft technologies and want seamless migration
• You need hybrid capabilities or want to modernize on-premise systems gradually
• You’re in a regulated industry (e.g. healthcare, government, finance) and need strict compliance
• Your team values integration, consistency, and centralized governance

Azure is often seen as the bridge between traditional enterprise IT and the cloud, offering powerful tools for modernization without requiring a complete overhaul.

Strengths and Limitations of Azure

Pros	Cons
Seamless with existing Microsoft environments (Windows, Office, SQL)	Slightly fewer total services than AWS
Best hybrid cloud strategy (Azure Stack, Arc)	Heavily enterprise-focused — solo developers may find it overkill
Competitive pricing and licensing advantages	Learning curve for non-Microsoft users
Robust global infrastructure and compliance options	Some services (like Function Apps) can have quirks in setup and scaling

Google Cloud Platform (GCP)

When people talk about modern cloud platforms built for speed, scale, and innovation, Google Cloud Platform (GCP) is increasingly part of the conversation. As the newest of the three major cloud providers, GCP has focused its efforts on performance, developer experience, and data-driven workloads from the very beginning.

Launched after AWS and Azure, GCP has steadily grown into a comprehensive cloud platform, now offering over 200 services that span compute, storage, networking, AI/ML, containers, and developer tools. Its foundation in Google’s global infrastructure — the same that powers Search, Gmail, and YouTube — gives it a strong edge in reliability and performance. And while it often takes a different approach than its competitors, that difference is exactly what many teams find appealing.

Global Reach and Deployment Flexibility

Google Cloud Platform (GCP) operates across 40 cloud regions, more than 120 availability zones, and nearly 200 edge locations. While it hasn’t reached AWS’s scale, GCP leverages Google’s global fiber network to deliver excellent performance and low-latency access across continents — especially useful for data-intensive and AI-driven workloads.

Well-known customers include Spotify, Target, X (Twitter), and PayPal. Dataquest, for instance, runs its learning and authoring platform on Google Kubernetes Engine (GKE), with databases hosted in Cloud SQL to improve latency and streamline performance for learners.

GCP supports all key cloud deployment models: public, private, and hybrid:

• The public cloud powers services like Compute Engine, Cloud Run, and BigQuery.
• Private deployments are available via Bare Metal Solution and Private Service Connect for compliance-sensitive workloads.
• Hybrid/multi-cloud capabilities come through Anthos, which lets you manage workloads across GCP, on-prem systems, and even AWS or Azure, all through a single Kubernetes-native interface.

While GCP took a later entry into hybrid deployments, its approach is modern and developer-friendly, especially for teams already embracing containers and multi-cloud architecture.

Core GCP services

Like AWS and Azure, GCP supports the full range of cloud service models — from virtual machines to serverless to complete software solutions — and offers a developer-friendly environment throughout.

i. Google Compute Engine(GCE)

Google Compute Engine (GCE) offers virtual machines with full administrative access, perfect for developers who want to build cloud systems with maximum control. Whether you’re deploying web servers, databases, or custom runtimes, GCE provides the core compute power to make it happen — complete with regional availability, autoscaling, and customizable machine types. It’s the IaaS layer that puts you in charge of every part of the stack.

You spin up an instance, choose your OS (Linux or Windows), and set the CPU, memory, and disk size. From there, you install packages, configure firewalls, and manage deployments just as you would on a physical machine. Meanwhile, Google takes care of the physical infrastructure — power, cooling, failover, and availability zones. Compute Engine is ideal for legacy migrations, custom server stacks, or workloads where you want deep system-level access without managing data center hardware.

ii. Cloud Storage

Cloud Storage is Google’s object storage solution for unstructured data — the kind that doesn’t fit neatly into a database. Whether you’re storing videos, backups, logs, or public assets, Cloud Storage gives you a highly available, globally distributed platform that’s ready for web-scale applications. You interact with it through REST APIs or client libraries, not block devices or file systems.

You start by creating a bucket and choosing a storage class (standard, nearline, coldline, or archive) based on access needs and budget. Files — or “objects” — are uploaded and retrieved using unique keys, and you control access with Identity and Access Management (IAM) roles or signed URLs. Google handles durability, replication, and performance tuning behind the scenes, so all you have to think about is what data goes in and how it’s accessed. It’s pure IaaS: infrastructure power, abstracted to make data storage effortless.

iii. App Engine

Google App Engine is a fully managed platform that abstracts away the entire server layer — so you can focus on code, not infrastructure. Just write your application in Python, Java, Node.js, or Go, and deploy it. App Engine automatically handles scaling, load balancing, patching, monitoring, and even zero-downtime updates. It’s PaaS in its purest form: no server setup, no containers, no DevOps overhead.

When you push your code to App Engine, Google provisions the environment, sets up routing, applies security patches, and adjusts the number of instances based on real-time traffic. You can also define environment variables, connect to databases, and integrate with GCP services like Cloud Tasks, Firestore, and Pub/Sub. Whether you’re launching a startup MVP or a global-scale web app, App Engine helps you move from idea to production fast, with almost no infrastructure in the way.

iv. Google Kubernetes Engine (GKE)

Google Kubernetes Engine (GKE) is a fully managed service that lets you run containerized applications using Kubernetes, without the operational overhead of managing the cluster infrastructure yourself. If you’re building microservices or need to scale stateless applications across multiple instances, GKE gives you all the flexibility of Kubernetes with the ease of a platform-as-a-service offering.

You define your container images, deployments, services, and scaling rules using standard Kubernetes configurations, and GKE takes care of provisioning nodes, managing the control plane, patching, load balancing, and health checks. You can enable features like auto-upgrades, horizontal pod autoscaling, or integrate with other GCP services like Cloud Monitoring and Cloud Load Balancing. It’s ideal for teams that want the power of Kubernetes without the hassle of maintaining the infrastructure underneath, letting you focus on deploying apps, not managing clusters.

v. Google Docs

Google Docs is one of the most iconic examples of Software as a Service (SaaS). It’s a fully managed, browser-based word processor that replaces traditional desktop software like Microsoft Word. You don’t install anything, maintain updates, or worry about saving files, you just log in, start typing, and everything is auto-saved and synced in the cloud. Whether you’re drafting essays, collaborating on meeting notes, or co-authoring a report, Docs lets multiple users edit the same file in real time from anywhere in the world.

All the complexity, hosting, document storage, real-time syncing, permissions, formatting engines, and version history, is handled by Google behind the scenes. As a user, you simply use the tool. It’s seamless, constantly updated, and accessible on any device. That’s the beauty of SaaS: software delivered as a fully packaged experience, where the only thing you manage is your content.

Pricing and Cost Considerations

GCP stands out for its developer-friendly pricing model**. It often includes:

• Sustained use discounts: Prices automatically drop the longer a service runs.
• Per-second billing: You pay only for what you use, with no rounding up to the nearest hour.
• Free tiers for many services — including Compute Engine, Cloud Run, and Firestore.

Compared to AWS and Azure:

• GCP is often more cost-efficient for data-heavy applications, analytics, and event-driven workloads.
• Its pricing tools (like the GCP calculator) make it easier to estimate and monitor usage.

While it may not always be the cheapest overall, GCP tends to offer the best value for data pipelines, containerized apps, and machine learning workflows.

When Does GCP Make Sense?

GCP makes a lot of sense when:

• You’re working with data, analytics, or AI/ML
• Your team is focused on containers or microservices
• You need developer-friendly automation and want to avoid infrastructure headaches
• You’re already using Google tools or open-source platforms like Kubernetes

It’s also great for smaller teams or startups that want powerful tools without having to commit to managing an entire DevOps stack.

Strengths and Limitations of GCP

Pros	Cons
Outstanding support for containers, serverless, and big data	Smaller service catalog than AWS or Azure
Best-in-class ML tools (Vertex AI, BigQuery, AutoML)	Less mature hybrid and migration tooling
Transparent, developer-friendly pricing	Smaller enterprise market share
Tight integration with Google Workspace and open-source projects	Some services still catching up in enterprise features

Which Cloud Provider Should You Choose?

• Choose AWS if you want the broadest set of services, the most global infrastructure coverage, and the flexibility to build highly customized cloud environments. It’s ideal for teams that want to scale globally, architect complex systems, or use a wide variety of tools , but it can feel overwhelming for beginners, and support costs extra.
• Choose Azure if you’re already using Microsoft tools like Windows Server, Active Directory, or Microsoft 365. Azure offers the smoothest path for enterprise migrations and hybrid environments. Its pricing model benefits existing Microsoft customers, and its governance and security tools are strong, especially in industries like finance, healthcare, and government.
• Choose Google Cloud Platform (GCP) if your work is data-heavy, containerized, or AI-driven. GCP excels at analytics, machine learning, and automated infrastructure. It’s a developer-friendly platform with clean interfaces and strong performance, particularly if you’re already using tools like BigQuery, Kubernetes, or TensorFlow.

Each provider brings something different to the table. Your choice depends on your technical goals, your existing tech stack, your budget, and how much customization or automation you need.

Many organizations use more than one provider, AWS for compute, Azure for internal systems, and GCP for analytics. What matters most is knowing where each one fits best for your needs.

Experiment: Try the Providers for Yourself

Move beyond theory and get hands-on with AWS, Azure, and GCP. Each one offers a free tier — so you can explore their strengths, tools, and quirks without committing to a subscription.

• Start with AWS: Launch a virtual machine using EC2, deploy a static site with S3 and CloudFront, or write a simple Lambda function. You’ll get a feel for how AWS handles infrastructure, automation, and its famously detailed configuration options.
• Try Azure: Use App Service to deploy a sample web app or experiment with Azure Functions to run serverless code. If you’re already using Microsoft tools, explore how easily Azure connects with Active Directory, Power BI, or Visual Studio.
• Explore GCP: Deploy a container with Cloud Run, build a small ML model in Vertex AI, or query public data in BigQuery. GCP’s UI and automation make it a great entry point for developers working on data, containers, or microservices.

Try all three, even for small, personal projects. Notice what feels intuitive, what gets in your way, and where each provider shines. That experience will tell you more than any checklist comparison ever could.