Selecting a cloud platform is one of the largest technology-based decisions you and your organization will face. The three largest cloud providers are, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform. They account for about two-thirds of the world's cloud market share.

This article will provide clear and direct comparative analysis to separate fact from fiction and hype from real data to assist you in selecting the optimal cloud platform to meet your business requirements.

Cloud Computing Defined:

A Cloud Computing Platform consists of a set of remote computing resources that can be accessed via the Internet. You do not need to own and operate physical computers to run your applications; instead, you can rent your computing power, storage capacity, and application tools through a cloud service provider. You pay according to the amount of computing resources consumed.

Top Best Cloud GPU Providers

All three of the aforementioned providers - AWS, Microsoft Azure, and Google Cloud - utilize the same business model as described above. Below is a high-level overview of each provider:

What Is AWS, Azure, and Google Cloud? 

• AWS (Amazon Web Services) is the cloud computing platform provided by Amazon and provides approximately 28 percent of the total amount spent on cloud computing globally (as of Q4 2025). AWS offers over 250 different types of services
• Microsoft Azure is the cloud platform that Microsoft makes. In the fourth quarter of 2025, it will have about 21% of the global market. Azure works very well with Windows Server, Active Directory, SQL Server, and Microsoft 365, which are all tools that many businesses already use.
• Google Cloud Platform (GCP) is Google's cloud platform. It has about 14% of the market, but it grows faster than the other two. Google Cloud is great at Kubernetes, artificial intelligence, and data analytics.

Kubernetes is a free tool that lets you run and manage a lot of software containers at once.Key Features 

Each provider does certain things very well. Here is what sets them apart.  

AWS — The Broadest Platform 

• 250+ distinct services make up AWS’ extensive range of services across nearly every category in the cloud. If there is something available in the cloud then it most likely has an equivalent service offered by the AWS platform.
• With 39 global regions and 123 availability zones, AWS provides a truly agnostic solution to global businesses needing agility, reliability, and scalability.
• The AWS ecosystem is unmatched, as evidenced by over 1,000 third party tools, training programs, and partner solutions available through the AWS partner network.

Azure - The Microsoft's Platform

• Microsoft integration Azure is compatible with windows server, active directory, SQL server, and Microsoft 365. In case your team is already utilizing these tools, then Azure would be second nature.
• 60+ geographic footprints or so all over the world - The three providers have the greatest geographic coverage.
• Hybrid and edge support Azure Stack and Azure Arc allow you to operate both on-premises and cloud servers. This would be handy when your company cannot be completely moved to the cloud, at least at the moment.

Hybrid cloud: this is a configuration when a portion of your systems operate on your own servers and the rest operates in the cloud.

Google Cloud — The Real Cloud Experience.

• BigQuery and Dataflow – Google Cloud data tools can be discussed as one of the best tools available in large-scale analytics.
• Vertex AI - Googles AI platform makes a workflow of the entire machine learning available on a single platform. Machine learning (ML) is a form of artificial intelligence that is used to predict based on data.
• There are 40 locations, 121 zones, and Google cloud covers more than 200 countries and territories.

How to Set Up Your Account 

Starting with each of the three platforms is the same way. How precise the screens vary with time, they all are subject to these four steps. 

Step 1 — Create an Account 

1. Visit the AWS, Azure, or Google Cloud Web site and select a sign-up option.
2. Create a basic profile and input information including email.
3. Select a payment plan, although you might prefer to use the free plan.
4. Confirm that you are who you are by phone or one time code. 

Step 2 — Checklist to Secure Your Admin Access 

1. In the case of AWS: Multi factor authentication (MFA) needs to be enabled on your root account, and then make an IAM user (admin).
2. In the case of Azure: Active Directory: to enable your account to have MFA, create an account into the Azure active directory and enable it.
3. In the case of Google Cloud: Create a Google Cloud organization when you have a company domain, assign the owner or the role of an administrator to others only after they are trusted. 

Step 3 — Develop a Project or Subscription

1. Create an account alias in AWS: Go to the AWS Organizations in the event that you run more than one team. 
2. In Blue: Open a subscription and then divide your resources into resource groups. Resource group is a folder of related cloud resources such as VMs, databases as well as networks.
3. On Google Cloud: It causes the creation of a separate project into each specific app or environment, such as my-app-production.

Step 4 — Choose a Region 

2. Make a selection of an area near your core users. This minimises delay (also known as latency) and in some cases minimises cost.
3. Ensure that the services that you require are present in that area. All the services are not offered in all the regions.

How to Run a Virtual Machine on Each System.

A virtual machine (VM) is a physically instantiated computer which is executed by a physical server. Any cloud platform uses it as its most typical starting point.

On AWS 

1. Open the AWS Management and select EC2.

2. Select an Amazon Machine Image (AMI) - e.g. Amazon Linux or Ubuntu Click ' Launch instance' and select an Amazon machine image.
3. Choose a type of an instance, which can be t3.micro in case of small workloads. One of the types of instances is a fixed-CPU, fixed-memory, and fixed-network-speed preset VM size. 
4. Select an SSH access key and security group configurations that permit the ports that you require, such as port 22 used in SSH.
5. Check your settings and start-up. Connect to SSH after the instance has been shown to be running.

On Azure 

1. Open the Azure portal and search for 'Virtual machines.' 

2. Click on create, and select Azure virtual machine.
3. Select a subscription, resource group and region. Select a VM image such as Windows Server or Ubuntu
4. Select, such as, a balanced workload of D4s v3. Check your administration information and serve a rule in ratched SSH or Remote Desktop. 
5. Pressing Review + create start up. Following the preparation of the VM, connect with SSH or RDP. 

On Google Cloud 

1. Open the Google Cloud console and go to Compute Engine. 

2. Create a new VM instance. Pick a region and zone. A zone is a specific data center within a region with its own power and network. 
3. Select a machine family, such as E 2 or N 2. Choose the type of the machine like e2-standard-4. 
4. Select an image of a boot disk, such as that of Debian and configure your firewall rules.
5. Click 'Create.' Through the console, you can connect through SSH.

Benchmark Results 

Independent tests make you see the performance of each platform when it is under real loads. The Cockroach Labs Cloud Report is one of the most reliable ones. It migrates the database workload which it writes to all three of the platforms.

Previous reports identified some obvious winners in each category. More recent findings are of a 'statistical dead heat.' Close to one another in all difficult cases when you select similar instances sizes, all three platforms perform similarly.

The table below shows benchmark highlights from the Cockroach Labs Cloud Report series. 

Test Category	Leader in Study	Key Finding
Network throughput	Google Cloud	GCP delivered nearly triple the network throughput of AWS and Azure in one study.
CPU micro-benchmark	Azure	Azure achieved better CPU scores in Cockroach Labs testing.
Storage read throughput	AWS	AWS i3en instances led storage read tests across providers.
End-to-end OLTP workload	All close	Cockroach Labs found overall top-end performance within the same range for all three.

OLTP (Online Transaction Processing): a type of workload with many small, fast reads and writes — like what a shopping or banking app does. 

These results are a starting point. Your own workload, instance choice, and settings will affect your results. 

Testing Details 

Cockroach Labs operates a Cloud Report yearly. It uses the same CockroachDB database in the AWS, AZ, and Google Cloud. It then executes a TPC-C-type workload - one of the standard tests which mimics a busy online store of many small database reads and writes occurring concurrently.

The test suite will be based on three tools:

1. Stress-ng to test the processors
2. iPerf to test the network throughput
3. Sysbench to test the storage I/O.

The CPU throughput of AWS was approximately 28 percent faster with stress-ng than with Google Cloud on older reports.

• Azure subsequently won the CPU results.
• Leading in network throughput tests Google Cloud.
• AWS stayed strong on storage.

Full Comparison Table 

This table covers the main criteria most teams look at before choosing a cloud platform.  

Criteria	AWS	Azure	Google Cloud
Market share (Q4 2025)	28% of cloud infra spending	21% of cloud infra spending	14% of cloud infra spending
Regions & zones	39 regions, 123 Availability Zones	60+ regions worldwide	40 regions, 121 zones
Service breadth	250+ services across all categories	Wide range, strong in Microsoft & hybrid	Broad portfolio with data and AI focus
AI / ML tools	Amazon Bedrock, Amazon Q, many ML services	Azure AI + OpenAI in Azure	Vertex AI, Gemini models, strong ML platform
Hybrid & on-premises	Outposts and Local Zones for select cases	Azure Stack and Azure Arc for deep hybrid use	Anthos and multi-cloud tools
Best known for	Breadth, ecosystem, mature tooling	Microsoft integration, enterprise hybrid	Data analytics, AI/ML, modern app workloads

No single platform wins in every category. The best fit depends on your existing tools, your team's skills, and your budget. 

Pricing 

Cloud pricing changes often. Each provider has numerous type of instances and programs of discounts. The following are the figures of a VM based pricing:

Hourly on-demand pricing — 4 to 5 vCPUs, ~10 GB RAM, 32 GB SSD: 

Monthly estimate — 4 vCPUs, 16 GB RAM, 32 GB SSD: 

Provider	VM Type	Est. Monthly Cost
AWS	T4g.xlarge	~$101 / month
Azure	Bs-series	~$121 / month
Google Cloud	E2	~$99 / month

These prices are for on-demand (pay-as-you-go) usage. Reserved instances, savings plans, and committed use discounts can cut costs by 30–70% for longer contracts. Spot or preemptible instances reduce costs further for workloads that can handle interruptions.  

What Makes Each Platform Different (USP) 

AWS — Breadth and Maturity 

AWS has the largest market share and the widest range of services of any cloud provider. It is the oldest, and is associated with strong partner network, certified engineers, and numerous external tools. AWS is difficult to compete with in case you want the greatest variety under a single roof. 

Azure — The Microsoft Stack Advantage 

If you already use Microsoft products, Azure provides your company with an advantage. Your existing accounts with Microsoft 365 can be connected to Azure within minutes. Active Directory identity management does not require any additional configuration.

Google Cloud — Data and AI at Scale 

Google cloud is the best option with teams that handle data or machine learning extensively. Analytics on billions of rows can be done in seconds with BigQuery. Vertex AI puts all the means of creating and implementing ML models in a single platform. The Kubernetes support of Google is also outstanding as it invented a container.

Pros and Cons 

AWS 

Pros: 

• Biggest market share and most prolonged history in the industry.
• You have 250+ services that have almost all forms of cloud works.
• Strong global presence with 39 regions and 123 Availability Zones.

Cons: 

• Pricing structure may seem complicated, and difficult to estimate, in new teams.
• Google Cloud has intensified its rivalry with some of the AI and analytics features.

Azure 

Pros: 

• Most appropriate with already existing Windows, SQL Server or Microsoft 365.
• Proactive hybrid cloud service through Azure Stack and Azure Arc.
• Largest regional coverage of 60 and more regions globally.

Cons: 

• New users may experience confusion on the naming of the portal and the service.
• AWS or Google Cloud is the first to release some of its new services and features.

Google Cloud 

Pros: 

• Best in class data analytics using BigQuery and dataflow.
• Best network throughput performance in sole benchmark research.
• As the proponent of the open-source tools, containers, and Kubernetes.

Cons: 

• Less market share and ecosystem, than AWS and Azure.
• Limited choices of local partners in certain areas to support the enterprise.

Quick Comparison Chart — What Fits Your Situation? 

Instead of making a final decision, use this chart as a starting point. More important than any general guide will be your personal workloads, abilities, and financial constraints.

Your Situation	Best Fit	Why
Heavy Microsoft use (Windows, SQL, Active Directory)	Azure	Native integration with Microsoft products and identity management.
Broad mix of workloads, need many service options	AWS	Largest service catalog, most regions, biggest ecosystem.
Data analytics and AI / ML are your main focus	Google Cloud	BigQuery, Vertex AI, and strong ML infrastructure.
Hybrid cloud with on-premises servers	Azure or AWS	Azure Stack / Arc and AWS Outposts both support on-premises setups.
Cost-sensitive steady workloads	All three (with discounts)	Each provider offers significant discounts for longer commitments.
Multi-cloud or spreading risk	AWS + Azure or AWS + GCP	Many enterprises combine providers to use the best service for each job.

Conclusion 

No cloud platform is the best across all the teams. The offerings of AWS, Azure, and Google Cloud all work well and are modern with capacity to support a majority of workloads. The decision to make is a matter of the tools you already have, abilities of your team and your data requirements.

• AWS fits well in group that require widest possible service offering and reach to the whole world.
• Azure fits those organizations who use already Microsoft software and require robust hybrid support.
• Google Cloud is an appropriate solution to teams whose tasks revolve around the analytics of data and AI, as well as container-based apps.

FAQ 

What is the best cloud platform in case of small startups?

Google Cloud suits startups that are data and AI-oriented. AWS fits well with start-ups that require infrastructural diversity. The actual question will be resolved based on your technology, the competency of your team, and the kind of discounts that one can get. 

Is Google cloud cheaper than AWS and Azure?

Google Cloud on-demand prices are reduced in certain VM types and regions. However, the actual costs will vary based on family at the instances, region, type of storage and discount programs. Then you should always compare with your own work load. 

Which steps do I take to start my team when we are new to cloud?

Start with one small pilot project and one provider. Use basic security options and expense notifications since the very beginning. Evaluate your performance and monthly bill.