Amazon SageMaker vs Google Cloud Datalab comparison

Amazon Web Services (AWS) and Google are both solutions in the Data Science Platforms category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.0, while Google is ranked #16 with an average rating of 7.8. Amazon Web Services (AWS) holds a 7.5% mindshare in Data Science Platforms, compared to Google’s 0.9% mindshare. Additionally, 91% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 80% of Google users who would recommend it.

Amazon SageMaker

Read 36 Amazon SageMaker reviews

6,544 Views
5,235 Comparison Views

91% willing to recommend

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

760 Views
664 Comparison Views

80% willing to recommend

Amazon SageMaker

Google Cloud Datalab

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Google Cloud Datalab and Amazon SageMaker compete in cloud-based machine learning platforms. Amazon SageMaker seems to have the upper hand with its comprehensive suite of tools and extensive features despite the compelling pricing and support of Google Cloud Datalab.

Features: Google Cloud Datalab features integration with BigQuery and Cloud Storage, supporting data analysis and visualization with Python-based notebooks. It also provides scalable cloud storage options alongside seamless integration within Google's ecosystem. Amazon SageMaker offers built-in algorithms, managed infrastructure for training and deployment, and integration with various AWS services, including Lambda and S3. It supports a plethora of machine learning frameworks and provides tools like SageMaker Studio for a comprehensive development experience.

Room for Improvement: Google Cloud Datalab could improve in automating infrastructure management, expanding AI feature capabilities, and enhancing node configuration. Amazon SageMaker might enhance user interface intuitiveness, offer more detailed documentation for less experienced users, and provide greater flexibility in pricing models for startups and independent developers.

Ease of Deployment and Customer Service: Google Cloud Datalab benefits from integration with Google's cloud services, allowing straightforward data access and collaboration. Meanwhile, Amazon SageMaker supports various deployment strategies with features like automated scaling and guided assistance, delivering extensive customer resources and flexibility to deploy models efficiently.

Pricing and ROI: Google Cloud Datalab is considered cost-effective, particularly for users within Google's ecosystem. Amazon SageMaker, potentially more expensive, offers substantial ROI due to its feature-rich environment and scalability, potentially making it a more valuable long-term investment despite higher initial costs.

To learn more, read our detailed Amazon SageMaker vs. Google Cloud Datalab Report (Updated: March 2025).

Buyer's Guide

Amazon SageMaker vs. Google Cloud Datalab

March 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Amazon SageMaker

Ranking in Data Science Platforms

3rd

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

AI Development Platforms (4th)

Google Cloud Datalab

Ranking in Data Science Platforms

16th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (18th)

Mindshare comparison

As of March 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.5%, down from 9.9% compared to the previous year. The mindshare of Google Cloud Datalab is 0.9%, down from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Hemant Paralkar

Lead Consultant at Saama

Improves team collaboration with advanced feature sharing but needs a better user experience

Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.

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Nilesh Gode

Project Manager - Data Science at Mettler-Toledo International Inc.

Easy to setup, stable and easy to design data pipelines

The scalability is average. We have not faced any issues with scalability. There are more than 500 end users using this solution in our company. It is an integral part of the daily operations. The usage pattern is not a one-time thing; employees regularly access and utilize the application. We use it at a global level with a scattered user base. This means that users don't all use the application at the same time. So, around 300 out of 500 employees use the solution, and this usage is spread out throughout the day.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."

"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."

"We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for these models, making accessing them convenient as needed."

"Amazon SageMaker is highly valuable for managing ML workloads. It connects to AWS cloud resources, making it easy to deploy algorithms and collaborate using tools like GitLab. It offers a wide range of Python libraries and other necessary tools for modelling and algorithms."

"They are doing a good job of evolving."

"The tool makes our ML model development a bit more efficient because everything is in one environment."

"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."

"The technical support of the tool was good."

More Amazon SageMaker pros

"Google Cloud Datalab is very customizable."

"All of the features of this product are quite good."

"The APIs are valuable."

"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

"For me, it has been a stable product."

"The infrastructure is highly reliable and efficient, contributing to a positive experience."

Cons

"The platform could be more accessible to users with basic coding skills, making it more intuitive and easier for beginners to use comfortably."

"The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."

"I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming."

"The solution requires a lot of data to train the model."

"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."

"The product must provide better documentation."

"The documentation must be made clearer and more user-friendly."

"For any cloud provider, the cost has to be substantially reduced, especially in the case of Amazon SageMaker, which is extremely expensive for huge workloads."

More Amazon SageMaker cons

"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."

"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."

"The product must be made more user-friendly."

"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."

"The interface should be more user-friendly."

"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."

Pricing and Cost Advice

"Databricks solution is less costly than Amazon SageMaker."

"I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees."

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."

"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."

"The solution is relatively cheaper."

"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."

"The support costs are 10% of the Amazon fees and it comes by default."

"SageMaker is worth the money for our use case."

More Amazon SageMaker pricing and cost advice

"It is affordable for us because we have a limited number of users."

"The pricing is quite reasonable, and I would give it a rating of four out of ten."

"The product is cheap."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

12%

Computer Software Company

11%

Manufacturing Company

Financial Services Firm

19%

Computer Software Company

12%

University

10%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

See all answers

What do you like most about Amazon SageMaker?

We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...

See all answers

What is your experience regarding pricing and costs for Amazon SageMaker?

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

See all answers

What do you like most about Google Cloud Datalab?

Google Cloud Datalab is very customizable.

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What needs improvement with Google Cloud Datalab?

Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcin...

See all answers

What is your primary use case for Google Cloud Datalab?

It's for our daily data processing, and there's a batch job that executes it. The process involves more than ten servers or systems. Some of them use a mobile network, some are ONTAP networks, and ...

See all answers

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Product Reports

Buyer's Guide

Amazon SageMaker

March 2025

Download Amazon SageMaker product report

Buyer's Guide

Google Cloud Datalab

March 2025

Download Google Cloud Datalab product report

Also Known As

AWS SageMaker, SageMaker

No data available

Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon Web Services (AWS)

Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.

Google

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

Information Not Available

Buyer's Guide

Amazon SageMaker vs. Google Cloud Datalab

March 2025

Free Report: Amazon SageMaker vs. Google Cloud Datalab

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