Google Cloud Datalab vs Microsoft Azure Machine Learning Studio comparison

Why PeerSpot?

Google and Microsoft are both solutions in the Data Science Platforms category. Google is ranked #22, while Microsoft is ranked #9 with an average rating of 7.8. Google holds a 1.7% mindshare in Data Science Platforms, compared to Microsoft’s 2.6% mindshare. Additionally, 80% of Google users are willing to recommend the solution, compared to 93% of Microsoft users who would recommend it.

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

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1,585 Comparison Views

80% willing to recommend

Microsoft Azure Machine Lea...

Read 62 Microsoft Azure Machine Learning Studio reviews

7,805 Views
4,215 Comparison Views

93% willing to recommend

Google Cloud Datalab

Microsoft Azure Machine Lea...

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Microsoft Azure Machine Learning Studio and Google Cloud Datalab compete in the machine learning and data analysis category. Google Cloud Datalab appears to have the upper hand due to its advanced features and perceived value, particularly in integration and visualization strength.

Features: Microsoft Azure Machine Learning Studio provides drag-and-drop functionality, automated ML capabilities, and integration with R and Python. Its easy setup and range of algorithms make it suitable for varied projects. Google Cloud Datalab integrates seamlessly with Jupyter notebooks, offers strong data visualization tools, and allows for flexible data experimentation, giving users a comprehensive environment for data work.

Room for Improvement: Azure Machine Learning Studio might benefit from enhanced support for complex data transformations and better automatic distributed processing capabilities. Additionally, improving user guidance for intricate workflows could be advantageous. Google Cloud Datalab could enhance node scalability, address configuration challenges for smooth transitions, and improve support documentation for ease of use.

Ease of Deployment and Customer Service: Azure Machine Learning Studio offers straightforward cloud-based deployment supported by comprehensive documentation and extensive support resources. Google Cloud Datalab supports cloud deployment with strong integration into Google services but may require more manual setup. Datalab's customer service benefits from Google's service integration, though Azure has an advantage with more streamlined support.

Pricing and ROI: Azure Machine Learning Studio features a flexible pricing model focused on value through integration, offering a solid ROI. Google Cloud Datalab's competitive costs justify its advanced features, promising higher long-term ROI due to its comprehensive capabilities. Users may find Google Cloud's value appeal in its feature-rich environment, making it a potentially cost-effective choice in the long run.

To learn more, read our detailed Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio Report (Updated: September 2026).

Buyer's Guide

Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio

September 2026

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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

Google Cloud Datalab

Ranking in Data Science Platforms

22nd

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (26th)

Microsoft Azure Machine Lea...

Ranking in Data Science Platforms

9th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

AI Development Platforms (6th)

Mindshare comparison

As of October 2026, in the Data Science Platforms category, the mindshare of Google Cloud Datalab is 1.7%, up from 1.1% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 2.6%, down from 4.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
Microsoft Azure Machine Learning Studio	2.6%
Google Cloud Datalab	1.7%
Other	95.7%

Data Science Platforms

Featured Reviews

LijomonJose

System Architect at UST Global EspaÃ±a

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.

Read full review

reviewer2722962

Data Scientist

Platform accelerates model development, enhances collaboration, and offers efficient deployment

The best features Microsoft Azure Machine Learning Studio offers include deep integration with Python notebooks and Azure Data Lake, which allows me to import external data, and through the pipeline, I can build my models, performing what is called data injection for my model building, making that deep integration quite interesting to use. Microsoft Azure Machine Learning Studio is a powerful platform for those already in the Azure ecosystem because it allows for scalability and provides a good environment for reproducibility, as well as collaboration tools, all designed and packaged in one place, which makes it outstanding. Microsoft Azure Machine Learning Studio has positively impacted my organization by reducing our project delivery times and increasing the pace at which we work, allowing us to focus on other more important tasks. Using Microsoft Azure Machine Learning Studio has reduced our model development time from approximately four hours to about two hours.

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

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

"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."

"Google Cloud Datalab is very customizable."

"The APIs are valuable."

"Their web interface is good."

"The product enables quick data preparation and data processing pipeline as well as modeling work and it's all part of Azure Machine Learning."

"Azure Machine Learning Studio provides a platform to integrate with large language models."

"ML Studio is very easy to maintain."

"For the best, reliable results, it is the best solution to have in mind."

"The visualizations are great. It makes it very easy to understand which model is working and why."

"Microsoft Azure Machine Learning Studio offers a hands-on experience with lots of flexibility."

"One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option."

More Microsoft Azure Machine Learning Studio pros

Cons

"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."

"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."

"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."

"The initial setup of Microsoft Azure Machine Learning Studio was rigorous for someone new like me, but mastering it made things simpler."

"The data cleaning functionality is something that could be better and needs to be improved."

"In terms of improvement, I'd like to have more ability to understand the detailed impact of the variables on the model and their interactions."

"The pricing policy should be improved. I find the pricing to be not a good story in this case, as it is not affordable for everyone."

"It's not that easy to master the program, it requires some specific learning."

"As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. Right now, it is the number of algorithms available in the designer that has to be improved, though I'm sure Microsoft does it regularly. When you take a use case approach, Microsoft has done that in a lot of places, but not on the Microsoft Azure Machine Learning Studio designer. When I say use case basis, I meant recommending a product or recommending similar products, so if Microsoft can list out use cases and give me a template, it will save me a lot of time and a lot of work because I don't have to scratch my head on which algorithm is better, and I can go with what's recommended by Microsoft. I'm sure that isn't a big task for the Microsoft team who must have seen thousands of use cases already, so out of that experience if the team can come up with a standard template, I'm sure it'll help a lot of organizations cut down on the development time, as well as going with the best industry-standard algorithms rather than experimenting with mine. What I'd like to see in the next version of Microsoft Azure Machine Learning Studio, apart from the use case template, is the improvement of the availability of libraries. Microsoft should also upgrade the Python versions because the old version of Python is still supported and it takes time for Microsoft to upgrade the support for Python. The pace of upgrading Python versions of Microsoft Azure Machine Learning Studio and making those libraries available should be sped up or increased."

"A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer."

More Microsoft Azure Machine Learning Studio cons

Pricing and Cost Advice

"The product is cheap."

"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 not that expensive."

"We pay only the Azure costs for what we use, which involves some subscription costs. But essentially, you pay for what you use. There are no extra costs in addition to the standard licensing fees."

"To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS."

"The pricing for Microsoft products can be complex due to changes and being cloud-based, so it's not straightforward. I've been familiar with it for years, but sometimes details about product licenses and distribution can be unclear. For Microsoft Azure Machine Learning Studio specifically, I would rate the price a six out of ten."

"In terms of pricing, for any cloud solution, you should know the tricks of the trade and how to use it, otherwise, you'll end up paying a lot of money irrespective of the cloud provider, so at least for Microsoft Azure Machine Learning Studio pricing versus AWS, I would rate it three out of five, with one being the most expensive, and five being the cheapest. It could be cheaper, but you also have to be careful when choosing the plans, for example, consider the architecture and a lot of other factors before choosing your plan, if you don't want to end up paying more. If your cloud provider has an optimizer that seems to be available in every provider, that would keep alerting you in terms of resources not being used as much, then that would help you with budgeting."

"I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."

"There isn’t any such expensive costs and only a standard license is required."

"The solution cost is high."

More Microsoft Azure Machine Learning Studio pricing and cost advice

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Top Industries

By visitors reading reviews

Construction Company

18%

Comms Service Provider

13%

Financial Services Firm

11%

Outsourcing Company

10%

Financial Services Firm

12%

Outsourcing Company

12%

Manufacturing Company

Performing Arts

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	23
Midsize Enterprise	6
Large Enterprise	32

Questions from the Community

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Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

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What is your experience regarding pricing and costs for Microsoft Azure Machine Learning Studio?

The pricing for Microsoft Azure Machine Learning Studio is reasonable since it's pay as you go, meaning it won't cost excessively unless specific resources are used.

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What needs improvement with Microsoft Azure Machine Learning Studio?

The initial setup can be a bit challenging for someone new, as the learning curve can be steep, but once I master the platform, I find it quite manageable. I would love to see the integration of a ...

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Product Reports

Buyer's Guide

Google Cloud Datalab

October 2026

Download Google Cloud Datalab product report

Buyer's Guide

Microsoft Azure Machine Learning Studio

September 2026

Microsoft Azure Machine Learning Studio report

Download Microsoft Azure Machine Learning Studio product report

Also Known As

No data available

Azure Machine Learning, MS Azure Machine Learning Studio

Overview

Google Cloud Datalab offers an integrated environment for seamless data processing and analysis. It combines robust infrastructure with free call-up features to enhance user experience, making it a go-to choice for data-driven tasks.

Google Cloud Datalab is geared towards users seeking efficient data handling solutions. It provides a seamless setup with robust infrastructure, focusing on enhancing APIs and offering meaningful data visualization through its dashboards. Notable AI capabilities include auto-completion and data logging, although some minor configuration challenges exist. While transitioning from AWS can be complex, the platform supports dynamic data pipeline design that suits Python development, offering an end-user friendly environment.

What are the key features of Google Cloud Datalab?

Robust Infrastructure: Provides seamless setup with strong backend support.
Free Call-Up: Allows use without extra costs, enhancing accessibility.
Data Visualization: Dashboards offer meaningful insights into data trends.
AI Capabilities: Features auto-completion and data logging for efficiency.
Customizable Architecture: Adapts to different data handling needs effectively.

What benefits should users expect from Google Cloud Datalab?

Efficient Data Analysis: Streamlines exploratory data analysis in machine learning.
Enhanced API Value: API integrations add significant value to data handling.
Friendly Python Environment: Suitable for end-users looking for ease in Python development.
Adaptive Pipeline Design: Supports complex data transitions from AWS.
Global Availability: Helps overcome time lag issues with server location selection.

In specific industries, Google Cloud Datalab is instrumental in managing data analysis, machine learning exploration, and dataset preprocessing. It facilitates the transfer of workloads from AWS and ensures efficient daily data processing. Organizations benefit from its capability to provision machine learning models into Vertex AI, bolstering research and development efforts. The global availability feature plays a significant role in selecting optimal server locations, addressing time lag and connectivity challenges.

Google

Microsoft Azure Machine Learning Studio offers a drag-and-drop interface, seamless integration with tools, and compatibility with multiple programming languages, making it user-friendly and efficient for developing and deploying machine-learning models.

This platform supports Python, R, and more, with automation features like AutoML, scalable resources, and cognitive services enhance data normalization and deployment. Users can easily create models, integrate with Azure services, and accelerate data science projects using its comprehensive library. Despite its benefits, improvements are suggested for cross-platform integration, enhanced data preparation, and clearer pricing strategies. Users also look for better deployment flexibility, more algorithm options, and examples. Expanded AI features, increased accessibility, and DevOps integration would further benefit its users.

What are the main features of Microsoft Azure Machine Learning Studio?

Drag-and-Drop Interface: Simplifies model creation and experimentation.
Integration with Tools: Works seamlessly with Azure services.
Compatibility with Languages: Supports Python, R, among others.
AutoML: Automates machine learning processes.
Scalable Resources: Provides adaptability in modeling.
Cognitive Services: Enhances data processing capabilities.

What benefits and ROI should users look for?

User-Friendly Design: Encourages accessibility for non-experts.
Seamless Deployment: Eases transition from model development to deployment.
Comprehensive Library: Accelerates data science processes.
Predictive Analytics: Aids in forecasting trends and behaviors.
Fraud Detection: Enhances security measures effectively.

In healthcare, finance, and retail, users apply Microsoft Azure Machine Learning Studio for developing machine learning models, predictive analytics, and deploying models efficiently. Its low-code interface supports experimentation for customer behavior prediction, data analytics, fraud detection, and automated machine learning, facilitating their business and research applications.

Microsoft

Sample Customers

Information Not Available

Walgreens Boots Alliance, Schneider Electric, BP

Buyer's Guide

Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio

September 2026

Free Report: Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio

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