AWS SageMaker vs Google Cloud ML : Which Machine Learning Platform is Right for You?

Choosing the right machine learning platform for a business is important if you want to manage your business in a well-organized and more powerful manner. It allows you to predict your upcoming expenses as well as manage your inventory system more productively. 

Machine learning platforms provide the necessary tools to develop, deploy, and improve machine learning. 

Today we’ll discuss two of the most utilized machine learning platforms; AWS SageMaker and Google Cloud ML, and will find out which machine learning platform is right for you. 

What is AWS SageMaker 

Let’s first understand what AWS SageMakeris is and then compare these two platforms.

AWS SageMaker or Amazon SageMaker is a machine learning service used by scientists and developers. With the help of this service, they can create and train machine learning models after which they can deploy them for predictive analytics apps. 

AWS SageMaker Features

AWS SageMaker includes the following features:

  1. 1

    One-Click feature for Training the models

    By using Amazon SageMaker service you can easily specify the location of the data and then mention the type of the SageMaker cases. You can start just with one click. 

  2. 2

    Distributed training

    You can use this service to efficiently perform distributed training and split data across several GPUs. This results in a near-linear scale of productivity. It automatically distributes the model with fewer than 10 lines of code.

  3. 3

    Automatic Model Tuning of models

    AWS SageMaker has the feature of automatic model tuning of machine learning models. It uses a technique that enables it to quickly tune the model. 

  4. 4

    Profiling and Debugging Training Runs

    Before you may deploy the production model, this feature allows you to capture the metrics as well as profiles training jobs, therefore debugging training runs. 

  5. 5

    Spot Training

    Due to this feature, AWS SageMaker helps you to reduce training costs by up to 90%. Spot Training keeps the training jobs automatically running when the compute capacity becomes available. 

  6. 6

    Reinforcement Learning

    Besides traditional supervised and unsupervised learning, AWS SageMaker also supports reinforcement learning. This is another big feature it has. 

  7. 7

    Major Frameworks

    This service also supports Major Frameworks including TensorFlow, Apache MXNet, and PyTorch. These are always up-to-date frameworks and are optimized for AWS SageMaker performance. 

  8. 8


    AWS SageMaker Automated machine learning allows you to build, train, and tune the best machine learning models according to your data. You can deploy the model to the production with just one click and give it a command to improve the model quality.

Advantages of using AWS SageMaker

  • Flexible Computing Instances 

    AWS allows you to choose between several computing instances with a number of CPUs, GPUs, and RAM.  You can select a computing instance according to your app's demands.  

  • Huge Algorithm Library

    AWS’s rich algorithm library is the next advantage you’ll love. It allows you to leverage to train your model using Amazon’s pre-trained models. All those algorithms and models are optimized to run on AWS services.

  • Flexibility to host the model in an endpoint

    AWS SageMaker has the advantage of hosting the model in an endpoint, meaning you can call it from a code written in any programming language.

  • AWS Community

    The AWS machine learning community which consists of data scientists, industry experts, developers, and AI professionals with rich resumes and experience in machine learning, is always there for you to collaborate with and help you enrich your knowledge about machine learning.

  • Jupyter Notebooks

    AWS provides Jupyter notebooks to their data scientists and this is the next big advantage because this notebook enables the usage of the machine learning lifecycle at its best, including building, training, and deploying.

  • Lowe Fees with AWS SageMaker

    Since AWS is meant to make the performance of several services quicker and easier, it also gives you the advantage to pay comparatively fewer fees. You will also escape paying for the resources you don’t use with AWS SageMaker. 

Disadvantages of using AWS SageMaker

  • Additional Coding

    Compared to other platforms offering no-code features, AWS demands additional coding for your data engineering needs, which is perhaps one of the most prominent  disadvantages. 

  • Difficulty in the use of UI  

    Another con you may find while using AWS is that it’s a bit difficult to use UI by business analysts and non-technical users. 

  • The platform may lag for larger data sets

    The AWS platform gets overwhelmed when you try to pull a huge amount of data from legacy solutions.

Use cases of AWS SageMaker

Now let’s pass on to the main use cases of AWS SageMaker to understand whether you need it or not. 

  1. 1

    RStudio Interface

    AWS comes with a feature of providing an RStudio interface meaning you can easily launch RStudio with a single click. With the help of this license, developers can dial-up computers without odd and time-consuming interruptions. 

  2. 2

    Pre-built solutions

    As for pre-built solutions, there are truly good features for open-source models. With the help of pre-built solutions, you can quickly and easily get started with Machine Learning using AWS SageMaker JumpStart.

  3. 3


    With the help of AWS SageMaker Auto machine learning features, you can easily build, train and tune ML machines. This Provides direct deployment and helps to improve the model quality. 

  4. 4

    Major Frameworks

    As mentioned above AWS supports major frameworks and this is the case when you can use various popular frameworks including TensorFlow, Apache MXNet, PyTorch, etc. 

  5. 5

    Local Mode

    You can test and prototype locally with the help of Apache MXNet and TensorFlow Docker containers. They are available on GitHub and you can download them and use the Python SDK for test scripts. 

AWS SageMaker vs Google Cloud ML: Pricing and Support

When it comes to pricing and support, we see several differences between AWS SageMaker and Google Cloud ML. 

  • Pricing: compared to Google Cloud ML, AWS SageMaker offers higher pricing.  It may charge $69 per month while AWS’s price is $50 per month. 

  • Support: compared to Google Cloud ML, the support fees of AWS are cheaper and the support service is stronger. They give solutions to big issues if any. 

What is Google Cloud ML 

Google Cloud ML is a hosted platform developed to run machine learning training jobs as well as predictions at scale.

You can use Google Cloud ML Engine to learn and train a complex model with the help of GPU and TPU infrastructure. Let’s go into the details and discuss the features of Google Cloud ML. 

Google Cloud ML Features

The main features of Google Cloud ML are the following: 

  1. 1

    Computing Services

    Some of the compute services Google Cloud offers are Compute engine, Google app engine, and Kubernetes engines.  

  2. 2


    The network services Google Cloud ML offers are Cloud Load Balancing, VPC, and Content Delivery network. 

  3. 3

    Storage Services

    The storage services included in GCML are Cloud SQL, Google Cloud Storage, and Cloud Bigtable. 

  4. 4

    Big data

    The services related to big data are Google Cloud Datastore, Google Cloud Dataproc, and BigQuery. 

  5. 5

    Security and Management tools

    Security and management tools include Cloud data loss prevention, Cloud IAM, data management, google cloud console app, google Stackdriver. 

  6. 6


    IoT, in its turn, is the same as the Internet of Things and it includes Cloud IoT Core and Cloud IoT Edge. 

  7. 7

    Cloud AI

    Cloud AI is all about Cloud Auto machine learning and cloud machine learning engine. 

Advantages of using Google Cloud ML

The biggest advantages of using Google Cloud ML are:

  • Pre-trained models

    You can save lots of time by using Google Cloud ML’s pre-trained models. You can use its cloud environment machine learning to improve your business process outcomes. 

  • Quick and Easy Collaboration

    This service offers a quick and easy collaboration as it allows multiple users to get access to the data and work simultaneously. 

  • High productivity

    Since Google tries to provide up-to-date service, it always stands out with the latest innovative features that provide higher productivity to its customers. 

  • Minimal Data Storing and Full Control

    This feature includes storing minimal data on vulnerable devices and provides full control over the stored data. 

  • Maximum Security

    Google Cloud is also known for its security professionalism. Its security system is reliable and allows you to delete the cloud data any time you want.  

Disadvantages of using Google Cloud ML 

According to the user reviews the main disadvantages of Google Cloud ML are:

  • It’s costly 

    The service of Google Cloud ML is more expensive compared to traditional hosting. However, it is worth it and the users tell “you get what you pay for”.

  • Fewer Data Centers

    Compared to other platforms, Google Cloud ML offers just 3 data centers: US, Europe, and Asia and this number is relatively small. 

  • Few customization options

    Google Cloud ML offers fewer customization options like BigQuery, Spanner, and Datastore compared to other similar platforms. 

  • Limited App Engine 

    The app engine of Google Cloud ML is limited to Java, Python, PHP, and Google Go only.

Use Cases of Google Cloud ML

Here are the use cases of Google Cloud ML and if you have decided to use this platform for your business, it’s important to know where and in which circumstances you can use this service. 

  1. 1

    Service Deployment

    You can deploy models and versions and then create a model resource. After this, you can create a model version.

  2. 2

    Data Storage

    You can store your model in Google Cloud Storage and set up your Cloud Storage bucket. Then you can upload the exported model to Cloud Storage and custom code.

  3. 3

    Secure low-level Infrastructure

    This is the case when you design and build your own data centers, which unites multiple layers of physical security. 

Google Cloud Ml vs AWS SageMaker: Support and pricing

  • Pricing: Compared to AWS SageMaker, Google Cloud ML comes with a lower price tag. It may charge $50 per month while AWS’s price is $69 per month. 

  • Support: Compared to AWS SageMaker, Google Cloud ML doesn’t stand out with a strong support service. It’s also more expensive. 

AWS SageMaker vs Google Cloud ML: features, functionality, and ease of use

Amazon SageMaker and Google Cloud ML are the top-rated machine learning platforms that you may choose for your business. They are both secure, powerful, and reliable but each comes with specific features.

So, you need to decide which one suits your business needs. Let’s compare both and find out the differences. 

  • Features: when it comes to the features we see the following difference between these two platforms. The data processing pipeline looks like this: it’s Datalab,

  • Functionality: AWS offers a more comprehensive machine learning service. On the other hand, Google Cloud ML is a great platform for beginners. 

  • Ease of use: Compared to Google Cloud ML, AWS SageMaker is easier to use. It also comes with faster prototyping. However, it’s not as flexible as Google Cloud ML and if you prefer ease of use over flexibility, then choose AWS SageMaker. 

Why AWS SageMaker?

So, why do most companies choose to use AWS SageMaker to build their apps? Perhaps the fact that AWS has quite flexible features and a depth of services, makes it a better place for the team to work. 

It offers a wide range of tools, including databases, analytics, and management as well as IoT, security, and enterprise applications. 

Why Google Cloud ML?

Companies that choose Google Cloud ML appreciate its limitless internal research. Compared to many other platforms, it has the superpower in the search engine system.

It’s one of the best platforms, especially for startups and companies that give priority to technologies and new approaches. 


Although the choice is not that easy, each should do it according to their business requirements. So, before you get started, make sure it provides all the essentials for your company starting from the price to the features and use cases. 

It’s important to note that start-ups widely use Google Cloud ML as it’s cheaper and provides the necessary tools for starters. More outstanding and established companies give priority to AWS SageMaker. 

About the author


Youssef is a Senior Cloud Consultant & Founder of

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