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Amazon SageMaker vs Google Cloud Datalab comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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
2nd
Average Rating
7.8
Reviews Sentiment
7.0
Number of Reviews
38
Ranking in other categories
AI Development Platforms (4th)
Google Cloud Datalab
Ranking in Data Science Platforms
19th
Average Rating
7.8
Reviews Sentiment
6.4
Number of Reviews
6
Ranking in other categories
Data Visualization (17th)
 

Mindshare comparison

As of February 2026, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 4.3%, down from 7.6% compared to the previous year. The mindshare of Google Cloud Datalab is 1.5%, up from 0.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Amazon SageMaker4.3%
Google Cloud Datalab1.5%
Other94.2%
Data Science Platforms
 

Featured Reviews

Saurabh Jaiswal - PeerSpot reviewer
Python AWS & AI Expert at a tech consulting company
Create innovative assistants with seamless data integration for large-scale projects
The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue integrate well for data transformations. The Databricks integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow, PyTorch, and MXNet, and comes with pre-trained algorithms for forecasting, anomaly detection, and more.
LJ
System Architect at UST Global España
dashboards are good and data visualization is more meaningful for the end-user
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 forcing your database with over a billion records, it can be tough for the end-user to manage the data. You need to have a single entity system in each environment. It's not because of GCP, but it would be great to have options like MongoDB or other similar tools in GCP. Then, we wouldn't always need to connect to the cloud and execute SQL queries. Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated. Once the data is collected, it should be easily sorted.

Quotes from Members

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

Pros

"We've had no problems with SageMaker's stability."
"SageMaker is a comprehensive platform where I can perform all machine learning activities."
"We were able to use the product to automate processes."
"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."
"SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project."
"The most valuable features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities."
"I appreciate the ease of use in Amazon SageMaker."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"For me, it has been a stable product."
"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."
"Google Cloud Datalab is very customizable."
 

Cons

"One area for improvement is the pricing, which can be quite high."
"While integration is available, there are concerns about how secure this integration is, particularly when exposing data to SageMaker."
"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"One area for improvement is the pricing, which can be quite high."
"The main challenge with Amazon SageMaker is the integrations."
"The main challenge with Amazon SageMaker is the integrations."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"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."
"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."
"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."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"The product must be made more user-friendly."
 

Pricing and Cost Advice

"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."
"The tool's pricing is reasonable."
"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 pricing could be better, especially for querying. The per-query model feels expensive."
"The product is expensive."
"Databricks solution is less costly than Amazon SageMaker."
"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
"The solution is relatively cheaper."
"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%
Computer Software Company
10%
Manufacturing Company
9%
University
6%
Financial Services Firm
24%
Computer Software Company
10%
University
9%
Comms Service Provider
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise11
Large Enterprise17
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...
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...
What is your experience regarding pricing and costs for Amazon SageMaker?
If you manage it effectively, their pricing is reasonable. It's similar to anything in the cloud; if you don't manage it properly, it can be expensive, but if you do, it's fine.
What do you like most about Google Cloud Datalab?
Google Cloud Datalab is very customizable.
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...
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 ...
 

Also Known As

AWS SageMaker, SageMaker
No data available
 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
Information Not Available
Find out what your peers are saying about Amazon SageMaker vs. Google Cloud Datalab and other solutions. Updated: December 2025.
881,707 professionals have used our research since 2012.