AWS vs GCP – Which Cloud Services to Choose in 2023?

  • Google Cloud is a suite of Google’s public cloud computing resources & services whereas AWS is a secure cloud service developed and managed by Amazon.
  • Google Cloud offers Google Cloud Storage, while AWS offers Amazon Simple Storage Services.
  • In Google cloud services, data transmission is a fully encrypted format on the other hand, in AWS, data transmission is in the general format.
  • Google Cloud volume size is 1 GB to 64 TB while AWS volume size is 500 GB to 16 TB.
  • Google Cloud provides backup services, but AWS offers cloud-based disaster recovery services.

What is AWS?

Amazon Web Services (AWS) is a platform that offers flexible, reliable, scalable, easy-to-use, and cost-effective cloud computing solutions.

AWS cloud computing platform offers a massive collection of cloud services that build up a fully-fledged platform. It is known as a powerhouse of storage, databases, analytics, networking, and deployment/delivery options offered to developers.

Here are the important pros/benefits of selecting AWS web services:

  • Amazon Web Services (AWS) offers easy deployment process for an app
  • You should opt for AWS when you have DevOps teams who can configure and manage the infrastructure
  • You have very little time to spend on the deployment of a new version of your web or mobile app.
  • AWS web service is an ideal option when your project needs high computing power
  • Helps you to improve the productivity of the application development team
  • A range of automated functionalities including the configuration, scaling, setup, and others
  • It is a cost-effective service that allows you to pay only for what you use, without any up-front or long-term commitments.
  • AWS allows organizations to use the already familiar programming models, operating systems, databases, and architectures.
  • You are allowed cloud access quickly with limitless capacity.

Important features of Amazon Web Services (AWS) are:

  • Total Cost of Ownership is very low compared to any private/dedicated servers.
  • Offers Centralized Billing and management
  • Offers Hybrid Capabilities
  • Allows you to deploy your application in multiple regions around the world with just a few clicks

What is Google Cloud?

Google launched the Google Cloud Platform (GCP) in 2011. This cloud computing platform helps a business to grow and thrive. It also helps you to take advantage of Google’s infrastructure and providing them with services that is intelligent, secure, and highly flexible.

Here are the pros/benefits of selecting Google cloud services:

  • Offers higher productivity gained through Quick Access to innovation
  • Employees can work from Anywhere
  • Future-Proof infrastructure
  • It provides a serverless environment which allows you to connect cloud services with a large focus mainly on the microservices architecture.
  • Offers Powerful Data Analytics
  • Cost-efficiency due to long-term discounts
  • Big Data and Machine Learning products
  • Offers Instance and payment configuration

Important features of Google Cloud are:

  • Constantly including more Language & OS.
  • A better UI helps you to improves user experience.
  • Offers an on-demand self-service
  • Broad network access
  • Resource pooling and Rapid elasticity

AWS vs. GCP - Products and Services

AWS and GCP have over 100 products and services in their catalogs that efficiently help customers work with cloud technologies. We will look at the differences between the popular services that AWS and GCP offer to their clients. 

Compute Engine is a compute and host service that provides scalable virtual machines to clients for running their workload tasks and applications. 

GCP provides four types of compute engine instances that offer specific features:

  • General Purpose – It is used for general workloads with reasonable price and performance ratios. 

  • Compute Optimised – It is optimized for compute-intensive workloads and offers higher performance than general-purpose instances. 

  • Memory Optimised – It is designed for memory-intensive tasks, providing up to 12TB of memory per core.

  • Accelerator Optimised – It is designed for parallel processing and GPU-intensive processes. 

AWS: Typically, AWS provides different EC2 instances similar to the list above. 

  • General Purpose instances provide diverse functionalities like compute, storage, and networking in equal proportions. General Purpose instances are suitable for web servers.

  • Compute Optimised instances are ideal for high-performance tasks that require high-speed processors and are compute-intensive—for example – game servers, media encoding devices, etc. 

  • Memory Optimised instances are optimal for situations where a large amount of data is processed in memory. These EC2 instances come to EBS optimized by default and are powered by the AWS Nitro System.

  • Storage Optimised instances offer high sequential and random read/write operations capability. These are used primarily for workloads that perform read/write on huge data stored in local storage. 

  • GPU/Accelerated instances are used for graphics processing and floating-point calculation that require colossal processing power. Accelerated Instances use extra processors and dedicated GPUs that boost hardware performance. 

Kubernetes is open-source container management and orchestration system that helps in application deployment and scaling. Containers are resources that run code along with its constituent dependencies, and Kubernetes provides container management and portability with optimal resource utilization for application development. It is easier to run Kubernetes on GCP because Google has been involved in the development of Kubernetes from its inception. Elastic Kubernetes Service in AWS provides no resource monitoring tool compared to Stackdriver by GCP. 

Serverless computing is a prevalent Function-as-a-Service example that does not require the deployment of virtual machine instances. AWS Lambda is the serverless offering from AWS, and Cloud Functions is its GCP counterpart. Google Cloud Functions support only Node.js, while AWS Lambda functions support many languages, including Java, C, python, etc. It is also easier to run cloud functions when compared to AWS Lambda since it needs a few steps. On the other hand, AWS Lambda is faster than Google Cloud Functions by 0.102 million executions per second. 

Amazon and Google both have their solution for cloud storage. Let’s look at the features one by one:

AWS S3 

  • Each object is stored in a bucket, and one needs the developer given keys to retrieve these buckets. 

  • An S3 bucket can be stored from a list of regions depending on the proximity, availability, latency, and cost-related issues.  AWS has a vast web of connected data centers worldwide in all areas. It is bound to provide higher performance and speed when storing and retrieving data across large distances. 

GCP Storage 

  • Google Cloud storage provides high availability.  

  • It offers data consistency across regions and different locations. 

  • It also gives google developer console projects.

AWS glue is a fully managed, serverless extract, transform and load (ETL) service to discover, prepare and integrate data from multiple sources for machine learning, analytics, and application development. It is a serverless data integration service that makes data preparation easier, cheaper and faster. 

On the other hand, GCP Dataflow is a fully managed data processing service for batch and streaming big data processing. Dataflow allows a streaming data pipeline to be developed fast and with lower data latency. 

AWS vs. Google Cloud - Pricing

AWS: AWS offers three unique pricing features or models

  • Pay as you go: The model makes resource usage adaptable and flexible by pricing only the company’s current resources.

  • Save when you commit: The feature means that if you use AWS services for a certain period, like one year, you will be eligible to have saving offers. 

  • Pay Less by using more: AWS promotes more usage of its services by tiering the price. That means the more one uses a service, the cheaper it gets, and vice versa. 

GCP: GCP also offers features on pricing with some similarities to AWS

  • Only pay for what you use: Similar to AWS’s Pay-as-you-go model, you are only paying for resources you end up using. Thus, making it on-demand pricing.

  • Save on workloads by prepaying: The model saves customers money if they commit to using a service and pay early for the resources at discount prices. 

  • Stay in control of your spending: GCP offers many cost management tools that are freely available and provide valuable analytics like price and usage forecasts, intelligent recommendation on cost-cutting, etc. Using these, customers can inspect their spending and optimize it accordingly. 

  • Price Calculator or Estimator: GCP provides a price calculator tool using which customers can estimate the overall price for the product and services before subscribing to them and preemptively make amends in their budgets. 

GCP provides 300$ in credits to new customers to use their services and products up to the free monthly usage limit. GCP is relatively cheaper in pricing than its Amazon counterpart, AWS. It also charges for computing minute-wise and is more strict to the pay-what-you-use model. 

AWS vs. Google Cloud - Machine Learning

AWS and GCP offer cutting-edge machine learning tools from their portfolio that help develop, train, and test a machine learning model. AWS has three powerful tools: Amazon SageMaker, Amazon Lex, and Amazon Rekognition. In contrast, Google gives the clients two major options – Google Cloud AutoML for beginners and Google Cloud Machine Learning Engine for heavy-duty tasks and granular control. GCP also offers Vertex AI and Tensorflow for advanced machine learning capabilities.

AWS Machine Learning Services 

  • Amazon SageMaker is a full-fledged machine learning platform that runs on EC2 instances and can develop traditional machine learning implementations. 

  • Amazon Lex brings Natural Language Processing toolkit and speech recognition possibilities, focusing on integrating Chatbot applications. 

  • Amazon Rekognition is a computer vision suite that renders the development and testing of face/object recognition models. It can easily perform complex CV tasks like object classification, scene surveillance, and facial analysis. 

GCP Machine Learning Products 

  • Google Machine Learning Engine: It is the machine learning offering at scale from Google. Google ML engine can perform complicated Machine Learning tasks using GPU and Tensor Processing Unit while running externally trained models. With great efficacy, Google Machine Learning Engine automates resource provisioning, monitoring, model deploying, and hyperparameter tuning.  

  • Google Cloud AutoML is a machine learning toolkit explicitly built for beginners in the field. It offers functionalities like data model upload, training, and testing through its web interface. AutoML integrates well with other Google cloud services like cloud storage. It can perform all the complex machine learning problems like Face Recognition, etc.

  • Tensorflow: Tensorflow is an already renowned name in the machine learning community. Tensorflow is an open-source library for numerical computation and analysis. It is used widely in deep learning models and packs many useful Machine Learning functions.

  • Vertex AI is an MLOps platform that promotes experimentation through pre-trained APIs for natural language processing, image analysis, and computer vision.

AWS vs. GCP - Regions and Availability

Google Cloud network locations are available across 106 zones and 35 regions worldwide and over 200 countries and territories. In contrast, AWS is present in more than 245 countries and territories, with 29 launched regions and 93 availability zones. GCP is expanding its reach in different countries like Doha, Paris, Milan, Toronto, etc. At the same time, AWS is bringing its services to places such as Israel, UAE, Hyderabad, Switzerland, Jakarta, etc. 

AWS vs. GCP - Which is Better?

Comparing these two cloud giants at the forefront of the industry is complex. AWS and GCP are the most significant cloud providers and competitors like Microsoft Azure, Alibaba Cloud, IBM cloud, etc. To draw a differentiation between these technologies is like comparing iOS and Android or Mercedes and BMW. Both are good and have their own thriving cloud communities. 

We, as users, have to decide and pick a cloud platform that is compatible with our business foundation and allows us better control over our needs and demands. For example, Google offers myriad machine learning frameworks and utilities that integrate well with Google Cloud. If our goal is analytics, GCP could be a good choice. It is subjective in the end and contingent on the user/company. 

Everything is moving slowly to the cloud, and fewer on-premise applications and products remain. As cloud professionals, it is essential to have the expertise and know-how of various cloud providers in the industry. You can make critical decisions even if you have to switch between vendors. Learning the ins and outs of different cloud service providers, whether AWS or GCP, takes time and effort. Persistence is the key, ultimately. 

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