Google Cloud Datalab vs Microsoft Azure Machine Learning Studio comparison

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 2.7% 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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1,416 Comparison Views

80% willing to recommend

Microsoft Azure Machine Lea...

Read 62 Microsoft Azure Machine Learning Studio reviews

7,131 Views
3,779 Comparison Views

93% willing to recommend

Google Cloud Datalab

Microsoft Azure Machine Lea...

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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: June 2026).

Buyer's Guide

Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio

June 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 August 2026, in the Data Science Platforms category, the mindshare of Google Cloud Datalab is 1.7%, up from 1.0% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 2.7%, down from 5.0% 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	2.7%
Google Cloud Datalab	1.7%
Other	95.6%

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

"Google Cloud Datalab is very customizable."

"The APIs are valuable."

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

"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."

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

"It's a great option if you are fairly new and don't want to write too much code."

"In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning."

"I find Microsoft Azure Machine Learning Studio advantageous because it allows integration with Titan Scratch and offers an easy-to-use drag-and-drop menu for developing machine learning models."

"The most valuable feature of the solution is the availability of ChatGPT in the solution."

"The most valuable feature is data normalization."

"It's easy to deploy. It has many features which help the person avoid delving into more technical things."

"The product is well organized. The thing is how we will get the models to work within our code. We have some suggestions there, but we want to gain more experience and be ready to answer that because we are currently working on this and don't have all the answers yet. The tool is well organized. What I am very happy about is the ease of deploying new resources. You can easily create your pipeline within minutes."

"The solution is very fast and simple for a data science solution."

More Microsoft Azure Machine Learning Studio pros

Cons

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

"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."

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

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

"I think it should be made cheaper for certain people…It may appear costlier for those who don't consider time important."

"The stability is questionable, given that Microsoft will be retiring the classic version of this product in 2024, and it's unclear how this will affect projects created on the classic version."

"The AutoML feature is very basic and they should improve it by using a more robust algorithm."

"Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it. What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners."

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

"Performance is very poor."

"Easier customization and configuration would be beneficial."

"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."

More Microsoft Azure Machine Learning Studio cons

Pricing and Cost Advice

"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."

"It is less expensive than one of its competitors."

"The solution cost is high."

"I rate the solution's pricing a four on a scale of one to ten, where one is cheap, and ten is expensive."

"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."

"The product is not that expensive."

"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."

"ML Studio's pricing becomes a numbers game."

"There is a lack of certainty with the solution's pricing."

More Microsoft Azure Machine Learning Studio pricing and cost advice

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Top Industries

By visitors reading reviews

Construction Company

20%

Financial Services Firm

13%

Outsourcing Company

University

Financial Services Firm

13%

Construction Company

Outsourcing 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

August 2026

Download Google Cloud Datalab product report

Buyer's Guide

Microsoft Azure Machine Learning Studio

July 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

June 2026

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