We have the answers to your questions! - Don't miss our next open house about the data universe!

Choosing the Right Cloud Provider: AWS vs. Azure vs. GCP Unveiled

aws cloud azure

There are many advantages for a company to migrate to the cloud, whether partially or totally. These include increased data security, greater computing power and lower IT costs.

However, knowing which public cloud provider to choose is no easy task. The first thing to understand is that the choice of an Infrastructure as a Service (IaaS), Platform as a Service (PaaS) or Software as a Service (SaaS) model necessarily depends on the sector and the needs of the business.

It is important to understand the differences between cloud providers so that you can choose the one that best meets your company’s needs and expectations. In this article, we will outline the notable differences between the three multinationals in this oligopolistic market, so that you can make your choice of cloud implementation.

There are many key factors in choosing the right cloud provider. We’re going to try and shed some light on a few key points to help you make the right choice.

We will analyse these points for the three market leaders.

Amazon Web Services

Financial management can quickly become extremely high and difficult. It should be noted that it is very difficult to compare this concept between different clouds, as they all have different features and services. However, we will try to compare this price allocation with the use of instances and compare the cost of requests.

1. Costs

For a small instance, the price averages USD 69 per month for this cloud provider. For a larger instance, you can use it for $3.97 per hour.

2. Geographical area

When it comes to geographical availability, it outstrips all its competitors by taking first place. AWS services are available in 26 regions, more than 84 zones, and no fewer than 245 countries and territories are served. It is also continuing to expand into other geographical areas.

Microsoft Azure

1. Cost

It is possible to use a small instance at Microsoft Azure for an average of $70 per month. This price is similar to Amazon Web Services, but rises to $6.79 per hour for a large instance.

2. Geographical area

In terms of availability, like Amazon Web Services, Microsoft Azure offers no fewer than 54 different geographical zones, covering more than 140 countries.

Google Cloud Platform

1. Cost

As for the third leader, it offers a small instance at a lower cost than the other two competitors, at $52 per month. But you’ll have to pay no less than $5.32 per hour for a larger instance.

2. Geographical area

In terms of geographical availability, Google Cloud Platform services are available in 29 regions, 88 zones and more than 200 countries and territories. As far as future developments are concerned, this cloud provider will continue to develop in various regions such as Berlin, Columbus and Paris.

Advantages and disadvantages

Amazon Web Services

As the oldest cloud provider on the market, it offers more features to customers who choose it, with no fewer than 200 different products, including amazon kinesis, S3 aws lambda, and more. It therefore has a dominant position in this sector and a strong global reach. On the other hand, it is difficult to use this cloud in a functional way without first undergoing training.

Offering more services than its competitors can be both an advantage and a disadvantage. It quickly becomes difficult to know which product to choose, and cost management is difficult to manage with this cloud computing giant.

Microsoft Azure

Microsoft Azure is Amazon Web Services’ biggest competitor. One of the advantages of this cloud is that it offers integration with all of Microsoft’s tools and software, making cloud computing easier to integrate into a company that is already using other Microsoft software.

What’s more, compared with its two other competitors, Microsoft Azure is a hybrid cloud. As far as its operation is concerned, part of it is supported by open source. On the other hand, a major drawback of Microsoft Azure is its documentation. It is inadequate and can sometimes be confusing.

Google Cloud Platform

One of the big advantages of Google Cloud Platform over the market leader is that it is easier to manage and reduce costs, because it offers flexible contracts. What’s more, the cost is per second, compared with the other two providers who offer a cost per minute. We pay for what we use.

Like Microsoft Azure, it is partly supported by open source. What’s more, the Google Cloud Platform is intuitive and fairly easy to use. However, being the youngest, it offers fewer features and services than its two competitors. But its use is growing by the day.

Features and services

Now that we’ve highlighted the different geographical availabilities and costs associated with the products, it’s important to focus on the notable differences between the products and cloud services used. This will enable us to make a real decision about our future cloud provider.

Management Service

All three cloud providers offer user account management. This means you can use the cloud, the different products and the different data while controlling access and data security. It is possible to manage the different roles of different users. This means, for example, that you can control access to sensitive data while still having the information you need.

The three cloud providers offer a range of service management products. Their aim is to enable secure and optimal deployment between different users and resources, but also to monitor access to and reading of data.

Storage Services

The three cloud providers offer different storage services. This essentially depends on the nature of the database, whether it is relational, graph-oriented or column-oriented. In addition, some storage services are dedicated to cold data, while others are more for hot data. Storage features are specific to each cloud provider.

Amazon Web Services

Amazon Simple Storage Service or Amazon S3 is the main storage service for scalable objects at AWS. A large number of companies rely on this storage service, which offers 99.9999999% durability.

Microsoft Azure

Microsoft Azure Storage is Microsoft’s storage manager. It offers different types of storage depending on the nature of the data: blob storage, file storage, queue storage, disk storage, Azure Data Lake Storage Gen2, etc.

But there are also other types of storage, such as for OLTP and OLAP operations.

Microsoft Azure offers a very interesting service called Cosmos DB. This feature provides an OLTP storage service while enabling OLAP data to be analysed simultaneously. This is HTAP storage.

What’s more, with Azure Synapse Link, it is possible to transfer OLAP data directly into Azure Machine Learning or Azure Synapse Analytics.

Google Cloud Platform

As for the third leading cloud provider, its storage service is Google Cloud Storage. This is surely the easiest to use of the three giants in this market.

What’s more, you can manage the lifecycle of your data. This makes it possible to optimise prices and data management. What’s more, you can choose your bucket according to the type of data you have.

This storage service provides simple, effective management of cold and hot data. For example, for cold data we have the option of choosing Archive Storage or Nearline Storage, which allows us to store data that we won’t be looking at, or will be looking at very little. If we take the archive, this will be very useful for data that we want to read a little less than once a year.

It’s very low-cost storage. If, on the other hand, we want to query our data more frequently, we can choose a more specific bucket for hot data.

Machine Learning

Project Environment Preparation: Essential Steps for Smooth Workflow

Amazon Web Services

AWS is mainly used for deploying virtual machines. But it offers a number of interesting products for Machine Learning.

Microsoft Azure

Microsoft Azure also has an interesting Machine Learning service, notably with Azure DataBricks, which can be used to manage and process large volumes of data. DataBricks is developed using the Apache Spark framework.

This service is very interesting because the platform is intuitive and enables Spark to be used quickly and efficiently.

What’s more, with DataBricks you can create and delete clusters on demand. This provides a degree of flexibility while still having access to the computing power of distributed systems.

Google Cloud Platform

When it comes to Machine Learning, Google Cloud Platform is a very interesting choice. It really is the Big Data-oriented cloud provider. Its products dedicated to training machine learning models offer very high added value.

In terms of DevOps, Google Cloud Platform offers the very interesting Google Kubernetes Engine service. This service offers a Google-managed solution for Kubernetes, making it easy to use Kubernetes. All you have to do is describe the hardware requirements of your application (computing power, storage, etc.) and Google Kubernetes Engine will take care of the rest.

As you can see, the choice of cloud provider is not so easy or intuitive at first sight. You need to understand your company’s needs in order to use the cloud that best meets your expectations. If you want to know how to use these clouds, take a look at our training courses.

You are not available?

Leave us your e-mail, so that we can send you your new articles when they are published!
icon newsletter


Get monthly insider insights from experts directly in your mailbox