AWS vs GCP - Which One to Choose in 2024? (2024)

Are you confused about choosing the best cloud platform for your next data engineering project ? AWS vs. GCP blog compares the two major cloud platforms to help you choose the best one.

So, are you ready to explore the differences between two cloud giants, AWS vs. google cloud? Let’s get started!

AWS vs. GCP - The Cloud Battle

The image above shows a Google Trends Graph for AWS and GCP, with GCP in red and AWS in blue.

Cloud Technology has risen in the latter half of the past decade. More companies and startups are emerging now that offer cloud-related solutions. Amazon and Google are the big bulls in cloud technology, and the battle between AWS and GCP has been raging on for a while. Google launched its Cloud Platform in 2008, six years after Amazon Web Services launched in 2002. The Google trends graph above shows how the two technologies have increased over the years, with AWS maintaining a significant margin over GCP. Amazon brought innovation in technology and enjoyed a massive head start compared to Google Cloud, Microsoft Azure, and other cloud computing services.

In all this time, Amazon was able to bring to the table a wide range of products and services, one after another. It developed and optimized everything from cloud storage, computing, IaaS, and PaaS. And that is one big reason it is the market leader and dominates other cloud technologies aggressively. But not long after Google launched GCP in 2008, it began gaining market traction. Every year Google Cloud Platform is making progress in leaps and bounds, catching up to AWS and giving it fair competition.

AWS vs. GCP - The Differences and Similarities Unleashed

AWS is a product of Amazon that provides cloud services and GCP is a hub cloud services offered by Google for organisations who wish to leverage their data through cloud computing. AWS boasts a comprehensive suite of scalable and secure offerings, while GCP leverages Google's expertise in data analytics and machine learning. Both platforms enable organizations to harness the power of the cloud for enhanced productivity, innovation, and digital transformation. AWS and GCP are very similar in their services and products but implementation and specifications differ.

GCP - Google Cloud Platform - An Overview

GCP is the cloud platform developed by Google that provides many cloud computing services that run and use the already established infrastructure used by other cloud services.

Google Cloud platform offers more than 100 services, including cloud computing, storage, machine learning, resource monitoring and management, networking, and application development. It is present in more than 200 countries and 106 zones across the globe, thus enabling high-speed resource commission and redundancy. Popular instances where GCP is used widely are machine learning analytics, application modernization, security, and business collaboration. Paypal, Twitter, Forbes, Voot, and Icici are some clients that rely on GCP’s services.

Features of GCP

GCP offers services, including

• Machine learning analytics

• Application modernization

• Security

• Business Collaboration

• Productivity Management

• Cloud app development

• Data Storage, and management

AWS - Amazon Web Services - An Overview

Amazon Web Services is the largest cloud provider, developed and maintained by Amazon. It provides cloud storage and computing services across 93 availability zones and 29 geographic regions. Typical applications and services under the AWS umbrella are cloud migration, content delivery, backup and restore functions, etc. Trusted clients that use AWS services are Tata Motors, Byju’s, OYO, and Wipro, to name a few. AWS is used across numerous different industries and stands as the cloud market heavyweight.

Features of AWS

Amazon offers services, including

• Computing

• Storage

• Databases

• Serverless functions

• Cloud app integration

• VPN

• Analytics and machine learning

AWS vs. GCP - Products and Services

Source: Programmer Humor on Reddit

AWS and GCP have over 100 products and services in their catalogs that efficiently help customers work with cloud technologies. Google supports data transmission in a fully encrypted format while AWS does so in a general format. Let uslook at more such differences between the popular services that AWS and GCP offer to their clients.

1. AWS EC2 vs. Google Compute Engine

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.

2. AWS Kubernetes vs. GCP Kubernetes

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.

3. AWS Lambda and GCP Functions

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.

4. AWS S3 and GCP Storage

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.

5. AWS Glue vs. GCP Dataflow

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.

Learn more about real-world big data applications with unique examples of big data projects.

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.

  See Also

  Top Cloud Providers in 2023

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 - Terminology

GCP product and service names are straightforward and easily memorable, as is evident now. In comparison, AWS product names have an inherent quirk that is a double-edged sword for beginners. It takes more time to get used to AWS terminologies, but at the same time, once one is well acquainted, it’s pretty fun to use these names. We shall compare the terminologies used by AWS and GCP, divided into five service/product categories. The list is nowhere exhaustive but mentions the popular services/products.

AWS vs. GCP - Services

Both AWS and GCP offer several services. But, this section compares the primary AWS and google cloud services in the domains, including compute, network, security, database, storage, and container.

Google Cloud vs. AWS - Network Services

Google Cloud vs. AWS - Cloud Security Services

AWS vs. GCP - Database Services

AWS vs. GCP - Storage Services

AWS vs. GCP - Container Services

AWS vs. GCP - Compute Services

AWS vs. GCP - Market Share

Amazon launched its cloud platform, Amazon web service, almost four years before Google did.

Today, Amazon holds 34% of the market share, while Google Cloud Platform commands 11% of the world cloud market. Amazon sits atop the market share, followed by Azure, Alibaba, Google Cloud, and other cloud providers. According to reports, the cloud computing market is likely to grow at a CAGR of 19.9%, reaching $1,712.44 billion by 2029.

AWS vs. GCP - Job market

There are plentiful opportunities and roles for AWS and GCP engineers. People often switch from one technology to another, depending on their experience, ease, and liking. Organizations are rushing to move to the cloud because of its numerous benefits and flexibility. Many companies already aboard the cloud train are expanding their services and products. Hence, there is a need for cloud engineers in the market to facilitate cloud processes in such organizations. Interview questions on AWS and GCP are a good starting point to check your level of cloud technology and work on the shortcomings after that.

Linkedin shows over 24K jobs for GCP Cloud Engineers and over 45K for AWS Cloud Engineers. There is an apparent skew in the job market because GCP is relatively new and expanding its reach.

When it comes to paying scales, the salaries for Amazon and Google Cloud Engineers fall in the range of $110k- $200k per year in the United States based on the skill and experience level.

The average salary for a Google Cloud Engineer in the USA is $141,375 per annum, while the average salary for an Amazon Cloud Engineer in the USA is $136,453 per annum.

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AWS vs. GCP - Difficulty Level

AWS and GCP are equally easy and challenging. There is no specific answer that could declare one easier than the other. There is a learning curve with Google Cloud, but one should also not overlook the fact that many AWS-certified engineers are already in the market due to AWS's market share. So, the competition would be more in AWS. At last, it falls on the prospective learner to decide based on their experience. Practicing projects in AWS and GCP is pivotal to having a deeper understanding of implementation and concepts.

AWS vs. GCP - Certification

AWS offers many role-specific certification exams that one can schedule at any time over the year.

Following is a cursory list of role-specific certifications offered by AWS divided into three tiers - Practitioner, Professional, and Speciality.

• AWS Certified Cloud Practitioner

• AWS Certified Solution Architect - Associate

• AWS Certified SysOps Operator Administrator - Associate

• AWS Certified Developer - Associate

• AWS Certified Solution Architect - Professional

• AWS Certified DevOps Engineer - Professional

• AWS Certified Security - Speciality

• AWS Certified Database Speciality

• AWS Certified Data Analytics – Specialty (DAS-C01)

• AWS Certified Advanced Networking – Specialty

• AWS Certified Alexa Skill Builder – Specialty

• AWS Certified Machine Learning – Specialty

GCP segregates its certification levels into the following tiers - Foundational, Associate, and Professional. A professional certification needs three years of cloud technology experience and one year in Google Cloud. In contrast, six months of cloud technology experience is needed for the Associate level. And finally, no cloud experience is required for foundational level certification and thus is recommended for beginners and freshers.

We briefly glance over the role-specific certifications that are available to anyone jumping into Google Cloud:

• Cloud Digital Leader - Foundational

• Cloud Engineer - Associate

• Cloud Architect - Professional

• Cloud Engineer - Professional

• Cloud Developer - Professional

• Cloud Security Engineer - Professional

• Cloud Network Engineer - Professional

• Cloud DevOps Engineer - Professional

Cloud certifications aren't easy; it takes much effort and understanding to bag these badges.

Learn the A-Z of Big Data with Hadoop with the help of industry-level end-to-end solved Hadoop projects.

AWS IAM vs. GCP IAM

When it comes to cloud security, IAM (Identity and Access Management) is crucial. IAM provides a mechanism and user authentication to the cloud. Although IAM for AWS and GCP perform the same function, but they do it differently. The main difference is that AWS IAM is used to grant access and manage accounts, whereas GCP IAM is used only to grant access to accounts managed by other means.

AWS VPC vs. GCP VPC (Virtual Private Cloud)

Google Cloud VPCs are global resources with subnets inside VPCs serving as zonal resources; traffic is automatically routed across regions. While AWS VPCs are regional resources: extra resources must be added to route traffic between regions. When you create a new VCP in GCP, subnets in all accessible regions are automatically created for you, but you may switch to manual mode and configure subnets solely for the areas you require.

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.

AWS vs. Azure vs. GCP Comparison

This section lists the comparison of AWS, Azure, and GCP based on market share, services, and certifications.

• AWS vs. GCP vs. Azure Market Share

The following statistics are based on the most recent market share information available:

AWS: Amazon leads the cloud market with a total market share of 34%. AWS is a leading cloud service provider that dominates the public cloud market by offering a wide range of cloud-based products and services.

Azure: Microsoft Azure is the second largest cloud service provider, with a healthy share of 21% in the global cloud market.

Google Cloud (GCP): Google Cloud Platform, GCP, which is now in the third position with a total market share of 11%, is now making substantial growth strides in the cloud market.

• AWS vs. GCP vs. Azure Pricing

Let us compare the pricing structure of AWS, GCP, and Azure based on the machine type:

AWS

• Minimum instance: A basic instance includes two virtual CPUs and 8GB of RAM, costing you about $69/month.

• Maximum instance: The largest instance includes 3.84TB of RAM and 128 virtual CPUs, costing you around $3.97/hour.

Azure

• Minimum instance: A basic instance includes two virtual CPUs and 8GB RAM, costing you about $70/month.

• Maximum instance: The largest instance includes 3.89 TB of RAM and 128 virtual CPUs, costing you around US$6.79/hour.

Google Cloud (GCP)

• Minimum instance: The basic instance offered by the Google cloud platform includes 2 virtual CPUs and 8 GB of RAM at a 25 percent cheaper rate, which costs around $52/month.

• Maximum instance: GCP leads here as the largest instance offered by the google cloud platform includes 3.75 TB of RAM and 160 virtual CPUs, costing you around US$5.32/hour.

When it comes to billing, AWS previously used to charge on an hourly basis, but they recently started offering pay-per-minute billing models that help users save money who use the instances for minutes. In comparison, Azure follows the pay-per-minute billing model from the start. Google Cloud, on the other hand, also follows the pay-per-minute billing model from the start. Google also offers discounts to save costs up to 50% with the help of models such as "committed use" and "sustained use."

• AWS vs. Azure vs. GCP Services

The following table compares the AWS, Azure, and Google Cloud compute services.

• AWS vs. Azure vs. GCP Certification

The following table compares the AWS, Azure, and Google Cloud certifications:

FAQs on GCP vs. AWS

• Is GCP easier than AWS?

If you don't have prior experience with AWS, both technologies are equally easier and more complex. GCP has a slight edge over this as it has a bare minimum and simpler implementation. But if your goal is to be proficient in market-dominant technology, then you should start with AWS. Also, suppose one already has a background in AWS. In that case, it becomes easier to transition into GCP, and other Cloud technologies as the underlying principles are the same with varying implementation.

• Which is better, AWS or GCP?

It depends more on the organization’s existing architecture and requirements.

• Is GCP cheaper than AWS?

Its pricing model for services and products is minute-wise compared to AWS's hourly computed charge model and closer to the pay-for-what-you-use model.

• Will GCP take over AWS?

Only time will be able to tell if GCP will take over AWS. AWS has an already established foundation and grip in the market, which places it ahead of GCP. But, surely GCP has been catching up, and the year-wise revenue report for both companies proves that GCP is proliferating.

• Is GCP more secure than AWS?

Both public cloud service providers have many security features and provisions, but comparatively, AWS is more secure than GCP.

• Is AWS faster than GCP?

GCP is, in fact, faster than AWS. In an experiment based on performance and efficacy, GCP could run more than 30K transactions per minute, thus giving more throughput than AWS.