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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 (16th)
 

Mindshare comparison

As of March 2026, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 4.0%, down from 7.5% compared to the previous year. The mindshare of Google Cloud Datalab is 1.6%, up from 0.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Amazon SageMaker4.0%
Google Cloud Datalab1.6%
Other94.4%
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

"The intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"They offer insights into everyone making calls in my organization."
"The support is very good with well-trained engineers whose training curriculum is rigorous."
"We were able to use the product to automate processes."
"The few projects we have done have been promising."
"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 most tool's valuable feature, in my experience, is hyperparameter tuning. It allows us to test different parameters for the same model in parallel, which helps us quickly identify the configuration that yields the highest accuracy. This parallel computing capability saves us a lot of time."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"For me, it has been a stable product."
"Google Cloud Datalab is very customizable."
"The APIs are valuable."
"All of the features of this product are quite good."
"All of the features of this product are quite good."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
 

Cons

"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"The product must provide better documentation."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"The main challenge with Amazon SageMaker is the integrations."
"While integration is available, there are concerns about how secure this integration is, particularly when exposing data to SageMaker."
"The main challenge with Amazon SageMaker is the integrations."
"The user interface (UI) and user experience (UX) of SageMaker and AWS, in general, need improvement as they are not intuitive and require substantial time to learn how to use specific services."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"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."
"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."
"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."
"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."
 

Pricing and Cost Advice

"The pricing is comparable."
"The support costs are 10% of the Amazon fees and it comes by default."
"There is no license required for the solution since you can use it on demand."
"The solution is relatively cheaper."
"The product is expensive."
"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."
"The pricing could be better, especially for querying. The per-query model feels expensive."
"Amazon SageMaker is a very expensive product."
"The pricing is quite reasonable, and I would give it a rating of four out of ten."
"It is affordable for us because we have a limited number of users."
"The product is cheap."
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
Manufacturing Company
9%
Computer Software Company
9%
University
6%
Financial Services Firm
20%
Construction Company
12%
University
7%
Computer Software Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise11
Large Enterprise18
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: March 2026.
885,311 professionals have used our research since 2012.