Let’s go over the AWS vs. Azure vs. GCP comparison. Over the last decade, cloud computing has made great strides. Today, you shouldn’t be asking whether to use cloud computing or not—it should be about which cloud platform to choose.
While the cloud computing market is flooded with numerous providers, Amazon Web Services, Azure, and Google Cloud Platform stand out proudly.
What factors do you consider when choosing between these options? Below are ways to learn the answers to such questions. Here, we compare and contrast AWS, Azure, and Google Cloud—highlighting and elaborating on their major differences.
Amazon Web Services
Amazon Web Services (AWS) pioneered cloud computing over a decade ago—leading in terms of both the number of products and customers, with it being regarded as the benchmark for cloud computing quality. In addition to computing, database, content delivery, and storage services, AWS also offers Infrastructure as a Service (IaaS).
AWS provides serverless services such as Amazon Kinesis Streams, Amazon SQS Queues, and AWS Lambda Functions for smooth and flexible data collection. Companies can choose from various platforms, operating systems, databases, and programming languages, among others, according to their needs.
Organizations benefit significantly from this provider in terms of productivity and business growth. Among the drawbacks of AWS are the complex infrastructure and the default service limits that are designed for average users.
Through a network of Microsoft-managed data centers, the Azure platform enables the creation, deployment, and management of a variety of services and applications. It offers computing, networking, data management databases, and performance services.
In addition to its extensive networking capabilities, Azure also supports multiple site-to-site connections to virtual networks—as well as connecting virtual networks across different regions. It also offers the lowest on-demand and discounted instance pricing.
Developers can also use the Azure Machine Learning Studio to write, test, and deploy algorithms.
Google Cloud Platform
Google Cloud Platform (GCP) offers an intuitive interface, lower costs, preemptible instances, and flexible compute options—making it a compelling alternative to AWS and Azure. To protect data and communications between its data centers, Google employs full-scale encryption.
Google Cloud and AWS compete in certain areas, including instance and payment configuration, privacy and traffic security, and cost efficiency. Each cloud provider offers a discount of up to 75% for a commitment of one to three years.
However, Google additionally offers a sustained use discount of up to 30% on each instance type that runs for more than 25% per month.
AWS Vs. Azure Vs. GCP Comparison
New technology based on remote servers is sweeping the new digital world, led by Amazon Web Services, Microsoft Azure, and Google Cloud Platform. In the public cloud market, there is fierce competition, and these are what sets each platform apart:
Availability in Various Regions
When choosing a cloud provider, it is essential to consider their availability and the regions they support. Cloud performance can be impacted by latency and compliance regulations, especially when dealing with big data.
As of September 2021, these are the big three’s availability:
- AWS – There are 22 geographic regions and 14 data centers, and in total, there are over 114 edge locations and 12 regional edge caches.
- Microsoft Azure – There are 54 regions, each with at least three availability zones and 116 edge locations.
- GCP – There are 34 cloud regions, 103 zones, and 200+ edge locations.
Key Cloud Tools
Presently, there is high competition among the three cloud providers. On the basis of current trends and customer demands, all three suppliers are likely to expand the tools and services they offer in the future.
AWS Key Tools
- Machine Learning and Artificial Intelligence – Amazon Web Services released Gluon, and without prior knowledge of AI, developers and non-developers can build neural networks with this open-source deep-learning library. Machine learning algorithms can be implemented on DeepLens, a camera powered by artificial intelligence that recognizes optical characters, images, and objects.
- SageMaker to Serverless – SageMaker is another AWS service for training and deploying machine learning models. As part of this platform, you’ll also find the Lex conversational interface, which will enable Alexa services, Greengrass IoT messaging, and Lambda serverless computing.
Azure Key Tools
- Cognitive Services – Bing Web Search API, Face API, Computer Vision API, and Custom Vision Service are some of the cognitive services available. With Microsoft, you can manage IoT devices and analyze their data, as well as use serverless computing solutions.
- Supporting MSFT Software – Azure supports a number of Microsoft products installed on-premises. A Windows Server Backup service is available in Windows Server 2012 R2 and Windows Server 2016. With Visual Studio Team Services, projects from Visual Studio can be hosted on Azure.
Google Cloud Key Tools
- IoT to Serverless – Google Cloud provides APIs for natural language processing, speech recognition, translation, and other advanced technologies. Additionally, it offers IoT and serverless services. It is important to note that these are still beta versions.
- Leader in AI – Google Cloud has become the leader in AI advancement. Much credit should go to TensorFlow, an open-source software library for building machine learning applications. TensorFlow is highly regarded by developers.
Microsoft Azure is considered to have the lowest on-demand costs, while Amazon is somewhere in the middle. There are a variety of pricing plans available for each of the three systems, as well as additional cost control features, such as reserved instances, budgets, and resource optimization.
Cloud platform costs are determined by several factors, including:
- Customer specifications
- Utilized services
The price for a primary instance with two virtual CPUs and eight gigabytes of RAM on Amazon Web Services is about US$69 per month. AWS’s most expensive instance, with 3.84 TB of RAM and 128 CPUs, costs roughly $3.97 per hour.
Azure charges roughly US$70 per month, for instance, with two CPUs and 8 GB of RAM. The largest Azure instance has 3.89 TB of RAM and 128 CPUs. The cost per hour is approximately $6.79.
The most basic instance on GCP, which includes two virtual CPUs and eight gigabytes of RAM, costs 25% less than on AWS. Therefore, you will have to pay about $52 a month. In terms of size, RAM, and CPUs, Google Cloud Platform offers the largest instance with 3.75 TB and 160 CPUs. You can expect to pay approximately $5.32 per hour for this service.
Choosing Between AWS Vs. Azure Vs. GCP: Which is Best for You?
Are you still unsure which cloud is suitable for your workload? You can count on us to resolve your quandary. Our team has developed, deployed, and maintained hundreds of cloud applications over the years. Our customized solutions have stood the test of time for both big companies and emerging enterprises.Depending on your company’s needs, our cloud migration services experts will provide you with the best solution. We will also ensure that the post-migration process runs smoothly. To get in touch with us, please email firstname.lastname@example.org or call us at 888-531-9995 at Laminar Consulting Service today!
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